The California-based Gamemaker Studio initially launched an open-source project in the year 2005 which was names as Unity. It is widely used in the development of various stimulation games, mostly used on mobile phones. It is embodied with a wide range of advanced features that help in reduction of work required in a quality product’s manufacturing. Its easy access and intuitive interface made it well known and a point of attraction for the developers. As a freeware and primarily a game development tool, Unity 3D provides the users with a range of special versions that are free of cost, however, the full versions can be later purchased according the user’s requirement. It appears cost free and can be accessed by anyone on their laptops or computers or any other device. Over 34% of the free mobile games were build using Unity Game Engine and are now installed or used through App Store or Play Store. Unity 3D becomes a fascination among the developers and provides them with the variety of game play options and features that make their gaming experience better. Unity 3D furthermore acts as a platform that could boost one’s career in game developing and give them a platform to rise in the gaming market. Unity’s catchy and subtle design and the graphics make it the center of attraction for a larger fanbase.  

Users above 2.8 billion have been registered for the access and use of game engines run or created by Unity till the year 2020. Along with facilities such as AR, VR, 2D and 3D game tools it also offers the user with various game production platforms such as consoles, mobiles, PC etc. Unity’s efficient use allows it developers to enjoy one platform for all their needs instead of switching between platforms. Its game instructions can easily be created through the help of its user-friendly interface and helps in creating games that load quickly and provide high quality visuals. However, all these graphics and visuals are made possible with the use of machine learning and its applications in the Unity systems. 

Machine Learning in Video Games  

Machine Learning is of prominent use in the world of gaming and development. As a subset for AI, it helps in constructing predictive and analytical models by taking in account the historical data. It is highly differentiated from the conventional AI techniques such as search trees and expert systems. 

The use of machine learning techniques is not very well known in the gaming industries as various game development agencies try not to share internal informations and facts completely with their users. Although, the knowledge about how machine learning is used in gaming is transmitted through the research projects published by various researchers and people having software and coding knowledge. The employment of deep learning agents in machine learning is a popular method used for softwares that challenge the professional human players in games with high level complexities and difficulties. Some of the games using machine learning are Minecraft, StarCraft, Doom, etc., and games other than video games or metaverse games like Chess and Go also employ machine learning for the designs and graphics for a better experience.  

Advantages of Machine Learning

Machine learning in Game development becomes more prominent in a world where these technologies are contributing in the restructuring of gaming landscape and provides its advantages in various way, some of which are

1. Personalised User Experience: Users get a more nuanced and personalised experience by providing them the authority for the creation of highly personalized video games according to their preferences. Using a highly advanced algorithm helps the player in evaluating and analysing how a player adapts certain gaming habits and uses that knowledge to further inform the gaming companies in order to offer them a better experience of customizing and creating content, challenges and rewards. 

2. Intelligent NPCs (non-Player Characters): NPCs are the computer-controlled AI characters that are in-game characters that converse with players and keep a track of their gaming habits and decisions. Further the use of machine learning and AI will help in the improvement of these characters as hyper realistic and more knowledgeable.  

3. Procedural Content Generation (PCG): By employing AI and Machine learning in gaming, the agencies and developers can include more computer assisted content in the form of levels and challenges. The PCG methods are an important feature as they help the developers in making the game more exciting and replayable, making the game more time efficient and reducing the excessive use of human resources.  

Unity also employs various machine learning techniques in order to make their gaming designs and personalized experiences. The Unity Game Developers utilize their machine learning toolkit in order to provide a training platform for the agents or users in order to make them develop better gaming skills and their employment of the state-of-the-art algorithms in order to offer their users with the authority of training intelligent agents for 2D, 3D, VR and AR games.  

The simple-to-use Python Api also contributes in training and preparing the agents for the neuroevolutionary and imitation including variety of other methods and technologies. These agents can be further employed to carry out various tasks such as controlling NPC behaviour, automatic evaluation of game builds and examining of different game design decisions pre-release. The use of ML-agents in Unity’s game development benefits both developers and AI researchers in creating a platform where the agents can evaluate Unity’s rich environments with the help of advanced AI.   

Unity’s use of Machine learning Agents

1. Create realistic and complex AI environments to train models: The major problem standing in the way of creating the novel environments in games in order to keep them functioning which often strikes out as a long and time-consuming process which asks for specialization in a particular domain. By addressing the problems occurring in the existing environment of AI training models, it can help in making the AI research more advanced. The utilization of Unity and ML-Agents helps the developers in creating a physically, visually and cognitively rich environment that can be used for further benchmarking of new algorithms and methods. 

2. A Unity ML-Agent further integrates the Unity Package and helps in providing better visuals and graphics. 

3. It creates a responsive and brain challenging game experience by creating AI characters who are interactive and highly competitive.  

In conclusion, the usage of Unity 3D Game Development also benefits the user as it is free of cost but also provides a paid version if one wishes to have a better specialization. It is accessible through a variety of different operating systems making it easy access to the source of entertainment. It further provides its user with a high quality, astonishing visual and creates a solid and helpful community. It has a smaller number of coding lines which makes it easy to use, and it is also less time-consuming while providing the best experience for your gaming and entertainment. 

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In the world of evolving digitalization and technology, various IT firms and units of organisations feel the need to construct and deploy their software applications to meet the customer’s requirements. This causes the delay in developing new softwares as there are fewer resources present and the backlog of operations face a rapid increase. The urgent need of providing more and more applications on regular basis, it becomes the responsibilities of organisations to supply their developers with tools and platforms that would ease out the work and will be time efficient in order to produce more and more applications to meet the user’s needs. In response to this, the low code and no code platforms are considered fast and budget friendly when it comes to producing more and more applications. This article will provide you insights on how the low code and no code platforms function and what are some of their key features and applications that benefit the IT firms and software developing organizations.

In today’s world, various organizations and firms are using low code and no code platforms as per their preference mechanism, leading to a drastic shift from the structuring of development to the assembling and integration of the applications. Gartner in his recent study, states that 70% of the new applications which have been recently developed will employ the low code or no code mechanisms by the year 2025 which is 25% higher than those used in the year 2020. The delay in development of software applications is leading towards the loss of various IT companies who are losing their clients and business due to the rapid requirement of software applications. Instead of waiting for the IT firms to fulfil their needs, the business teams or clients are managing and experimenting the development of new applications by utilizing the low code and no code platforms that often strike up as a fast and cost-efficient method for them as they don’t have to pay extra to an IT firm just for an application development. As there is no barriers while entering in the platforms and it cuts off the cost margins, these low code and no code platforms are becoming very popular among the regularly growing businesses and companies preferring these transformations that take place due to the use of these platforms. These new technologies help professional developers to perform their tasks three times faster than while using traditional forms.

What are low code and no code platforms?

Low Code Platforms

The mechanism of Low code platforms is a software development program that require no coding or very little coding in order to release faster and efficient app releases. These platforms enable the use of Graphical User Interface (GUI) and intuitive modelling in order to provide a better visual development of the software applications. They work as a collaboration of various software tools and provides better functioning in order to construct the best software applications. Due to the complete elimination or reduction of coding, the low code platforms provide a faster way to release the apps and helps in smooth and time efficient use of the platforms.

In 2011, the concept of Low code was first introduced in the technological world and was considered to be a challenging concept in the world of programming which leads to the increase in utilization of development platforms which help in reducing the task of coding and engages itself in the development hence reducing the work for developers.

The foundation of these platforms is deeply rooted in concepts such as automatic code generation and model-driven design. The tools employed in these platforms reduce the line-by-line coding, instead it allows the integrated tools that enables the user’s authority over flowcharts that are embodied in the visual editor, therefore automatically producing the codes automatically.

No Code ML Platforms

These frameworks allow the non-IT users to create the software by totally neglecting the use of codes. Often providing the users with an easy-to-use UI, these platforms authorize their users to visually execute the process and design the business logic through the drag and drop options present in its UI system. These platforms are generally considered ‘extraordinarily disruptive’ alongside the low code platforms. By providing a leverage to the visualist development, these platforms ensure the non-programming users to create a full application based on visual executions.

These platforms result in the elimination of IT firms from businesses as the need for coding is eliminates allowing the client to create an application completely on his own and even authorize them to personalize the app or software according to their desired outcome and requirement.

Evolution of Low Code and No Code Platforms

The beginning of Low code/ No code platform stems back to the conventional rapid application development tools like Excel, Lotus Notes and Microsoft Access that authorize users in certain ways and allows them to develop and customize the apps or softwares on their own. However, the conventional tools created a challenge for the users by requiring them to have a thorough understanding of the business and its environment in order to generate the software and apps. However, the use of low code or no code platforms provide the user the liberty to eliminate coding or have little to no knowledge of these feature through their drag and drop options. In order to carry out a successful project, it is however considered beneficial to work with experienced programmers who have an advance knowledge of these low code and no code platforms.

Before launching an app using such platforms, it is essential to identify your target audience and how they would use the app. Usually the apps created through the low code/ no code platforms are accessible to various departments and can be used by the entire enterprise as well as the users such as customers.

Few Low Code ML Platform sites and app

1. Mendix: Mainly focused on businesses, this low code developing platform helps in solving the issues of enterprise application development, workflow automation, and modernization for legacy systems. Mendix offers a wide range of features that act in collaboration with each other to provide a better app development. The feature of polls in Mendix provides the feedback that marks out the target audience and area which should be kept in focus. The integrations for SAP, Microsoft Azure and Teams, AWS, Salesforce, Google Cloud, and IBM Cloud are pre-built. The in-built API can be used to build connecters which are referred to the solutions created by the users themselves. They offer a pricing that ranges from $60 per month with a free trial plan and allows the truly agile development.
2. Appian: With low code development tools covering domains like process mining and data fabric, Appian provides end-to-end process automation. Appian’s case management capabilities act as a major factor for its high use as a low code platform. Salesforce, AWS, and SAP have pre-built integrations, and user can link their own solutions using an API. It starts from $2 per user each month and offers a free plan for trial. It also provides robust features for process automation and strong case management. The security platforms used are wide and help in better functioning and production of app. However, it appears to be a tough task while applying it in the built-in database systems. Another drawback is that it provides a limited scope of data visualisation and custom reporting.
3. Nintex: Its drag and drop feature and the pre-built templates offer a low code process automation tool. The integrated process mapping function helps in maintaining a smooth workflow management which allows changes in the scope and approach of development. The built-in robotic process automation allows the automation of tasks which come out as a habitual practice of the user such as the document generation, E-signatures, etc. Azure, AD Groups, Amazon S3, Google Translate, Outlook, Slack, Smartsheet, Signiflow, and Bacon Ipsum have their pre-built integrations in Nintex. Its prices range from $2,083 per month and offers a 30-days free trial. Its responsive support team makes it an interactive and rather helpful platform. It gives the user a robust automation and offers the comprehensive onboarding process. However, it has a steep learning curve which makes it difficult to understand in terms of operations and production and it is a rather expensive approach for the user.

Some No Code Platform sites and apps

1. CreateML: Apple provides CreateML, which is a no-code platform for the construction and refinement of bespoke machine learning models. As an independent macOS application, CreateML offers a variety of pre-trained templates. The application allows the generation of Texts, tabular data, pictures, videos, and graphics as inputs. It will create classifiers and recommender systems based on the information. The training and validation data must be parsed in the necessary forms for CreateML, which might be rather complicated. Before beginning the training, you may also customize the iteration count and fine-tune the metrics.
2. Google Cloud AutoML: A cloud-based system, it includes tables, videos intelligence, natural language processing, AutoML Translation and Vision for image classification. Developers can use the case specific models and train them even when they have little or no knowledge of machine learning. However, it could be tough for a non-developer to personalize the results using Google Cloud AutoML.
3. Graphite Notes: One of the biggest challenges with machine learning is its complexity, particularly for non-technical workers. Graphite Note can help this as it does not require coding and it prioritizes ‘business value’. It makes the machine learning analytics easy to understand and apply and people with no knowledge of coding can construct machine learning models. It authorizes the strategists, execution teams, and decision makers to forecast possible outcomes based on their datasets.

Benefits of Low Code/ No Code Platforms

1. In the rapidly growing world that requires speed and efficiency in its work, these platforms provide faster pace to the production and delivery of app and software.
2. The allow a quick and easy access to everyday workers who have little to no knowledge of coding and enables them to solve problems on their own and create the apps required in their businesses.
3. These programs reduce the work of professional developers through their automation techniques and features and allow them to focus on a larger, more important approach rather than sticking to the mundane work required in the app or software development.
4. These platforms are also cost efficient and some of them are even free of cost while others may charge little amount of money from the users in order to provide their services.

Limitations of Low Code/ No Code Platforms

1. These platforms, lead to a lack of visibility due to their low costs and often lead to the leaders losing track of ongoing projects.
2. The customization is limited in these platforms and more capabilities might be required in order to offer better functioning.

In conclusion, these low code/ No code platforms turn out to be highly efficient and important in creating apps and softwares as they provide authority to the non-technological users who have no knowledge of coding. These are a better way to manage the development of softwares while saving both time and money by providing rapid functioning at a low cost. These platforms, despite being highly useful, may have some challenges which can be overcome by improving them and making them more reliable source for future use.

The post Empower Your ML Journey: Top Low-Code and No-Code Platforms first appeared on AITechTrend.

Machine Learning Operations, or MLOps for short, is a field that streamlines the creation, deployment, and maintenance of machine learning models in production environments by fusing machine learning (ML) with DevOps methods. To guarantee the effective and dependable delivery of ML applications, it focuses on bridging the gap between data science and operations teams.

MLOps’ primary goal is to solve the difficulties involved in implementing and managing machine learning models on a large scale. Processes including data pretreatment, model training, assessment, deployment, monitoring, and maintenance can all be automated in this way. Organisations may increase model performance, boost overall productivity, and shorten the time-to-market for machine learning applications by implementing MLOps principles.

The field of machine learning and operations, or MLOps, has become increasingly important in technology. It provides an organised method for scaling, managing, and implementing machine learning models in real-world settings. The need for qualified professionals knowledgeable in MLOps methods is only going to increase as more and more industries use machine learning to automate processes and make decisions.

Starting an MLOps journey requires figuring out a plethora of ideas, resources, and procedures. Experience in the real world is just as significant as theoretical knowledge. Aspiring MLOps practitioners can learn the subtleties of model construction, deployment, and maintenance through practical projects.

The deployment, scaling, and management of machine learning models in production contexts are the main areas of focus for the quickly developing field of machine learning operations, or MLOps. Several industries have seen a sharp increase in the use of machine learning technology in recent years, which has increased the demand for reliable and effective procedures for operationalizing these models. You must begin your MLOps journey with real tasks to obtain practical experience and comprehend the fundamental ideas involved. We’ll look at eight beginner-friendly projects in this article to help get people started on MLOps in 2021.

1. Configuring an Environment for Development:

Setting up a strong development environment is essential before tackling MLOps to create and train machine learning models. Start by deciding which frameworks and technologies are best for your project based on its specifications. Python’s large library and active community make it a popular choice for machine-learning applications. An interactive platform for machine learning algorithm design and experimentation is offered by Jupyter Notebooks. Installing libraries like PyTorch or TensorFlow for deep learning jobs is another thing to think about.

After selecting your tools, make sure your system has them installed and configured correctly. Python libraries and dependencies can be installed using package managers such as pip or conda. To separate your project’s dependencies and prevent problems with other projects, create a virtual environment. Organising a robust development environment will streamline your workflow and make it easier to iterate on your machine-learning projects.

2. Using Git for Version Control:

A crucial component of contemporary software development, including machine learning initiatives, is version control. Git is a popular version control system that lets you work with team members, keep track of changes made to your codebase, and go back to earlier iterations when necessary. To begin, commit your basic codebase to a Git repository for your machine learning project.

Use Git to keep track of code modifications while you work on your project and try out various strategies. By using branches, you can work on experiments or new features without impacting the main codebase. After changes are verified and tested, merge them back into the main branch. Work together as a team by transferring and pulling changes between distant repositories like GitHub or GitLab. By mastering Git, you’ll ensure better collaboration and reproducibility in your machine-learning projects.

3. Data Preprocessing Pipeline: Cleaning, converting, and preparing data for model training are all part of the critical data preprocessing stage in the machine learning pipeline. Examine your dataset to find any missing or inconsistent values first. Implement strategies like imputation or deletion to efficiently manage missing data. Next, use feature engineering to extract pertinent data and generate new features that highlight significant trends in the data.

Using tools like pandas or sci-kit-learn, create a preprocessing pipeline after the data has been cleaned and engineered. Every preprocessing step should be included in this pipeline, and it should guarantee consistency when doing it on fresh data. Make sure your preparation pipeline generates the appropriate output by testing it on a sample dataset. Gaining proficiency in data preparation will the quality and reliability of the models. 

4. Model Training and Evaluation –

To reduce the prediction error, a suitable algorithm must be chosen, training data must be fed into it, and its parameters must be optimised. Begin by playing with various machine learning algorithms, such as neural networks, decision trees, and linear regression. Utilising relevant measures like accuracy, precision, recall, and F1-score, train each model on your dataset.

After assessing several models, choose the model that exhibits the best performance using your validation data. To further enhance the model’s performance, adjust its hyperparameters. To make sure the model is resilient and prevent overfitting, think about applying methods like cross-validation. Lastly, test the generalisation capacity of your model using an alternative test dataset. You may create dependable and accurate machine-learning models by becoming an expert in model training and evaluation.

5. Model deployment with Docker –

The process of making your trained model available for inference in real-world settings is known as model deployment. With the help of the containerisation platform Docker, you can bundle your model and all of its dependencies into a small, portable container. To begin, create a Dockerfile that lists the setup and dependencies needed to execute your model.

Construct the Docker image and run a local test to make sure everything functions as it should. Deploy the Docker image to an AWS, Azure, or Google Cloud production environment after you’re happy with the outcome. Employ Kubernetes or other container orchestration solutions to effectively manage and grow your deployed models. You will be able to deploy your machine learning models confidently and easily if you understand how to use Docker for model deployment.

6. Continuous Integration Pipeline –

Code integration and testing are automated as part of the software development process known as continuous integration, or CI. CI pipelines support maintaining the consistency and quality of your machine learning codebase in the context of machine learning. First, create a continuous integration (CI) server using GitHub Actions, Travis CI, or Jenkins.

Set up your continuous integration pipeline to automatically test your machine learning code each time a new version of the code is submitted to the repository. Unit tests, integration tests, and model assessment metrics are a few examples of these tests. To write and run your tests, use programmes like TensorFlow Extended (TFX) or Pytest. Your machine learning codebase will stay dependable and manageable for the duration of its lifecycle if you can effectively manage continuous integration workflows.

7. Model Monitoring and Logging –

It is crucial to keep an eye on your model’s performance once it has been put into production and to identify any irregularities or deviations immediately. Configure logging and monitoring tools like Grafana, Prometheus, or the ELK stack to keep tabs on system health, faults, and model metrics. Keep an eye on important metrics like accuracy, throughput, and prediction latency.

Establish alerts to inform you of any activity that deviates from expectations, and then take prompt action to remedy it. Logging records pertinent data for debugging and auditing purposes regarding model predictions, inputs, and outputs. You can make sure that your deployed machine-learning models are dependable and durable by becoming an expert in model monitoring and logging.

8. Model Lifecycle Management –

Machine learning models require a variety of tasks to be managed during their lifecycle, such as versioning, retraining, and re-deployment. Create a versioning mechanism to monitor model modifications and guarantee repeatability. Create automated retraining pipelines to continuously add new data to your models, enhancing their performance over time.

Use strategies like phased rollouts and A/B testing to safely introduce fresh iterations of your models. When deployed models stop meeting the required performance standards or become outdated, retire them and keep an eye on their performance. By becoming an expert in model lifecycle management, You can ensure that your machine learning models are accurate, dependable, and current throughout their lives.

Youtube Video – What is MLOps | MLOps Explained in just 3-minutes | Introduction to MLOps | Intellipaat (youtube.com)

It can be intimidating to start your MLOps adventure, but with the correct projects and direction, you can quickly acquire the abilities and knowledge required for success. You can gain a strong foundation in MLOps and prepare yourself to take on more difficult tasks in the industry by finishing the eight projects listed in this article. As you advance on your MLOps journey, never forget to explore, iterate, and never stop learning. Employing commitment and persistence, it is possible to realise the potential of machine learning in practical applications fully.

The post 8 Projects to Begin Your MLOps Journey in 2021 first appeared on AITechTrend.

we present the top 15 free books on machine learning and data science, encompassing a wide range of topics, from fundamental concepts to advanced techniques. Whether you’re a novice eager to embark on your learning journey or a seasoned practitioner seeking to deepen your expertise, these resources provide invaluable insights and guidance.

Certainly! Here’s a list of 15 free books on machine learning and data science:

1. Python Data Science Handbook” by Jake VanderPlas: This book covers essential Python libraries and tools for data science, including NumPy, pandas, matplotlib, and sci-kit-learn.
2. Introduction to Statistical Learning” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani: This book provides an introduction to statistical learning methods and their applications, with a focus on machine learning.
3. Deep Learning” by Yoshua Bengio, Ian Goodfellow, and Aaron Courville: This comprehensive book covers deep learning concepts, algorithms, and applications, making it suitable for both beginners and experts.
4. Bayesian Methods for Hackers” by Cameron Davidson-Pilon: This book introduces Bayesian methods and their practical applications in data analysis, with a focus on using Python and PyMC.
5. Pattern Recognition and Machine Learning” by Christopher M. Bishop: This book provides a comprehensive introduction to pattern recognition and machine learning algorithms, with a focus on probabilistic graphical models.
6. Data Science for Business” by Foster Provost and Tom Fawcett: This book explores the intersection of data science and business, covering topics such as data mining, predictive modeling, and analytics strategy.
7. The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: This classic book covers advanced topics in statistical learning, including supervised and unsupervised learning algorithms.
8. Machine Learning Yearning” by Andrew Ng: This book provides practical advice and best practices for building and deploying machine learning systems in real-world applications.
9. Think Bayes: Bayesian Statistics Made Simple” by Allen B. Downey: This book offers an introduction to Bayesian statistics using Python, with a focus on practical examples and exercises.
10. Probabilistic Programming & Bayesian Methods for Hackers” by Cameron Davidson-Pilon: This book introduces probabilistic programming and Bayesian methods using the PyMC library in Python.
11. Python Machine Learning” by Sebastian Raschka and Vahid Mirjalili: This book covers essential machine learning algorithms and techniques using Python and sci-kit-learn.
12. A First Course in Machine Learning” by Simon Rogers and Mark Girolami: This book provides a gentle introduction to machine learning concepts and algorithms, with a focus on practical applications.
13. Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank, and Mark A. Hall: This book covers data mining and machine learning techniques, with a focus on practical applications using the Weka software.
14. Introduction to Machine Learning with Python” by Andreas C. Müller and Sarah Guido: This book offers a practical introduction to machine learning concepts and techniques using Python and sci-kit-learn.
15. Foundations of Data Science” by Avrim Blum, John Hopcroft, and Ravindran Kannan: This book provides an introduction to key concepts in data science, including algorithms, statistics, and machine learning.

   The Best Book For Learning & Get Your Basics Strong In Data Science

These books cover a wide range of topics in machine learning and data science and are valuable resources for both beginners and experienced practitioners.

The post Unveiling the Ultimate Knowledge Repository: Top 15 Free Books on Machine Learning and Data Science first appeared on AITechTrend.

In recent years, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with Edge Devices and Internet of Things (IoT) has led to significant advancements in various industries. However, this convergence also introduces new security challenges and concerns. Securing edge devices and IoT systems in the context of AI/ML is crucial to ensure the integrity, confidentiality, and availability of data and the overall system.

Understanding Edge Devices and IoT in AI/ML Security

• Edge Devices

Edge devices are hardware and software systems that process data locally at the edge of the network, closer to the data source, rather than relying on a centralized cloud or data center. These devices include sensors, actuators, gateways, and embedded systems, and they play a critical role in enabling AI/ML applications at the edge.

• IoT Systems

IoT systems consist of interconnected devices that collect and exchange data over the internet. These systems are integral to various AI/ML applications, as they provide the data necessary for training and inference processes. IoT devices range from consumer gadgets to industrial sensors and actuators.

Security Challenges in AI/ML at the Edge

The integration of AI/ML with edge devices and IoT systems introduces several security challenges, including but not limited to:

• Data Security
• Device Authentication
• Secure Communication
• Firmware and Software Security.
• Resource Constraints

Strategies for Securing Edge Devices and IoT Systems in AI/ML

• Encryption and Authentication

Implementing strong encryption mechanisms and robust authentication protocols to secure data transmission and verify the legitimacy of edge devices and IoT endpoints.

• Secure Boot and Firmware Integrity

Utilizing secure boot mechanisms and ensuring the integrity of device firmware and software to prevent unauthorized code execution and tampering.

• Access Control and Segmentation

Implementing access control policies and network segmentation to restrict unauthorized access and limit the potential impact of security breaches.

• Intrusion Detection and Anomaly Detection

Deploying intrusion detection systems and anomaly detection algorithms to identify and respond to potential security threats and abnormal behavior at the edge.

• Over-the-Air (OTA) Updates

Enabling secure and reliable OTA update mechanisms to promptly patch vulnerabilities and update security configurations on edge devices and IoT systems.

• Edge AI/ML Model Security

Implementing techniques such as model encryption, differential privacy, and federated learning to protect AI/ML models and the sensitive data they process at the edge.

Future Trends and Considerations

As AI/ML applications continue to proliferate at the edge and within IoT ecosystems, several trends and considerations are shaping the landscape of edge device and IoT security in AI/ML:

• Integration of Blockchain: Exploring the integration of blockchain technology to enhance the security and integrity of edge device and IoT data transactions and interactions.
• Standardization and Regulation: The development of industry standards and regulations specific to AI/ML security in edge and IoT environments to ensure consistent and comprehensive security practices.
• Edge AI/ML Security Automation: Leveraging AI and ML techniques to automate security monitoring, threat detection, and response at the edge, enabling proactive security measures.

Pioneering Edge: Top AI/ML Security Startups Revolutionizing Edge Device and IoT Systems

The convergence of Edge Devices and Internet of Things (IoT) with Artificial Intelligence (AI) and Machine Learning (ML) has paved the way for innovative solutions in various industries. As the edge AI market continues to grow, several startups have emerged as key players, leveraging AI/ML to revolutionize the way businesses manage essential assets, predict maintenance issues, and drive real-time insights. Here’s an overview of some of the top startup companies leading the way in Edge AI for IoT Systems and AI/ML security:

• Cylera

Location: USA

Founder Name: Paul Bakoyiannis, Sean Abraham, Timur Ozekcin

Link: https://cylera.com/

Funding: $17M

Overview: Cylera, a globally trusted leader, has redefined healthcare IoT security through its innovative platform, empowering healthcare networks and hospitals with comprehensive connected medical device visibility and robust cybersecurity solutions. 

• Ockam

Location: USA

Founder Name: Matthew Gregory, Mrinal Wadhwa

Link: https://www.ockam.io/

Funding: $17.7M

Overview: Ockam, a leading data security software company, has been at the forefront of revolutionizing the way modern applications exchange and trust data across complex and hostile networks. By offering a networkless connectivity solution and secure-by-design protocols, Ockam has established itself as a trusted partner for organizations seeking robust end-to-end encryption, key management, and secure connectivity

• Refirm Labs

Location: USA

Founder Name: Peter Eacmen, Terry Dunlap

Link: https://www.refirmlabs.com/

Funding: $3.5M

Overview: ReFirm Labs has emerged as a leading provider of automated IoT firmware vulnerability discovery, assessment, and remediation solutions. The company’s flagship product, Centrifuge Platform, has garnered attention for its ability to proactively vet, validate, and continuously monitor the security of firmware in IoT devices, offering a revolutionary approach to managing and monitoring security across deployed IoT device

• Karamba Security

Location: USA

Founder Name: Ami Dotan, Assaf Harel, David Barzilai, Tal Ben David

Link: https://karambasecurity.com/

Funding: $30M

Overview: Karamba Security, a cutting-edge cybersecurity solutions provider, has established itself as a leader in the realm of end-to-end product security for connected systems. With a focus on safeguarding resource-constrained systems, Karamba’s innovative software solutions are designed to address cybersecurity challenges without disrupting R&D or supply-chain processes, offering industry-leading protection for vehicles and IoT devices

• Dellfer

Location: USA

Founder Name: Fabrice Ferino, James Blaisdell

Link: http://dellfer.com/

Funding: $10M

Overview: Dellfer, a prominent cybersecurity firm, is dedicated to providing cutting-edge security solutions to protect IoT devices and connected cars from cyber threats. The company’s innovative approach centers on securing firmware and enabling continuous protection against zero-day cyberattacks and known vulnerabilities

These startups are at the forefront of leveraging Edge Devices and IoT Systems in AI/ML security, contributing to the rapid growth of the edge AI market. As the demand for low-latency and real-time processing continues to rise, these companies are instrumental in driving innovation and shaping the future of AI/ML security in edge computing.

In conclusion, the emergence of these startups signifies a new era of AI/ML security, where the integration of edge devices and IoT systems plays a pivotal role in driving advancements across diverse industries.

The post Fortifying the Edge: Safeguarding AI/ML Security in Edge Devices and IoT Systems first appeared on AITechTrend.

The use of cutting-edge technology like artificial intelligence (AI) and machine learning (ML) has completely changed the way medical services are provided in today’s quickly changing healthcare environment. But the growing reliance on digital platforms has also made healthcare systems more vulnerable to never-before-seen cybersecurity risks. In reaction to this, the idea of Cyber Security as a Service (CSaaS) has surfaced as a crucial tactic in bolstering the security and robustness of healthcare frameworks while welcoming the possibilities presented by AI/ML breakthroughs.

The Evolving Technological Landscape

Along with creating more individualized treatment plans and more accurate diagnoses, the use of AI and ML in healthcare has also brought new challenges, especially in the area of cybersecurity. Maintaining patient privacy and the public’s confidence in healthcare systems is critical as healthcare organizations work to use AI to improve patient outcomes and operational effectiveness.

The Role of Cyber Security as a Service

Healthcare cybersecurity issues can be tackled pro-actively and all-encompassing with the help of Cyber Security as a Service (CSaaS). CSaaS providers enable continuous monitoring through anomaly detection and predictive analytics, strong authentication and access control systems, encryption for secure data transport, and continuous monitoring by utilizing AI and ML. These services, which offer real-time data on cyber dangers and enable more precise detection of malware and other security breaches, are intended to strengthen healthcare networks against constantly changing cyber threats.

Safeguarding Patient Data and System Resilience

A comprehensive strategy to cybersecurity measures is required due to the convergence of AI/ML and cybersecurity in the healthcare industry. It is becoming increasingly clear that cybersecurity must be incorporated into healthcare as AI continues to change the field. With its focus on ethical standards, strong defenses, and ongoing monitoring, CSaaS is essential to protecting healthcare networks from cyberattacks while maintaining patient confidence.

The Leadership Imperative

Future leaders in healthcare will have to spearhead innovation while maintaining the safety and robustness of healthcare systems in the face of this dynamic convergence. These leaders push for the creation of AI-driven solutions reinforced by cybersecurity precautions, and they support a well-balanced legislative framework that protects patient interests while fostering innovation. Their collaboration with medical professionals, digital experts, and policymakers aims to create strong solutions and increase public understanding of the intricacies involved in cybersecurity and artificial intelligence in healthcare.

Securing the Future of Healthcare: Trailblazing Start-ups in AI/ML and Cyber Security as a Service

In an effort to improve patient care and operational effectiveness, the healthcare sector is progressively adopting cutting-edge technology, such as machine learning (ML) and artificial intelligence (AI). Rapid digital transformation has, however, also increased cybersecurity dangers, calling for sophisticated security measures to protect sensitive patient data and vital hospital infrastructure. The convergence of AI/ML security and Cyber Security as a Service (CSaaS) is being revolutionized by a number of creative start-ups, and they will be crucial in protecting healthcare systems from cyberattacks. Here are a few of the best new businesses spearheading this initiative:

• CyberMDX

Funding: $30M

Founder Name: Amir Magner, Moti Shniberg

Link: https://cybermdx.com/

Overview: CyberMDX, founded in 2016 and headquartered in New York City, United States, specializes in AI-based cybersecurity solutions for healthcare. The company’s platform provides network-based cybersecurity solutions designed to track and maintain medical devices and clinical network assets. With a focus on real-time device security and managing operational continuity across departments, CyberMDX’s platform employs micro-segmentation policies to isolate asset-based threats effectively.

• Clear DATA

Funding: $80.4M

Founder Name: Chris Bowen, Darin Brannan, David G. Jemmett, Matt Ferrari

Link: https://www.cleardata.com/

Overview: ClearDATA, established in 2009, is a prominent player in the healthcare cloud security and compliance domain, offering advanced solutions to address the evolving challenges faced by healthcare organizations. The company’s commitment to safeguarding patient data, ensuring compliance, and mitigating cyber threats has positioned it as a leader in the industry.

• MEDIGATE

Funding: $50.4M

Founder Name: Itay Kirshenbaum, Jonathan Langer, Pini Pinhasov

Link: https://claroty.com/healthcare-cybersecurity/medigate

Overview: Medigate by Claroty stands at the forefront of healthcare cybersecurity, offering a comprehensive, modular, and SaaS-powered platform designed to safeguard the Extended Internet of Things (XIoT) in healthcare environments. With a focus on addressing the unique cybersecurity challenges inherent to healthcare, Medigate’s solutions extend cybersecurity across a broad range of XIoT devices, supporting the full healthcare cybersecurity journey with scalability, flexibility, and ease-of-use.

Medigate’s platform is tailored to secure a diverse array of XIoT devices crucial for care delivery in healthcare settings, including IV pumps, ultrasounds, smart HVAC, and lighting systems

• Cynerio

Funding: $37M 

Founder Name: Daniel Brodie, Leon Lerman

Link: https://www.cynerio.com/

Overview: Cynerio, a leading provider of 2nd Generation cybersecurity solutions for healthcare IoT, is dedicated to revolutionizing healthcare cybersecurity through its cutting-edge platform. With a focus on securing every IoT, IoMT, OT, and IT device in healthcare environments, Cynerio has emerged as a pivotal player in safeguarding patient safety, data confidentiality, and service availability.

Cynerio’s platform aligns with the NIST cybersecurity framework, focusing on reducing the risk of healthcare attacks and maximizing security coverage across a hospital’s entire IT infrastructure without any impact on patient care or services

Conclusion

In conclusion, the intersection of Cyber Security as a Service and AI/ML security in healthcare represents a critical juncture in shaping the future of healthcare innovation and patient-centric care. As the healthcare industry continues to embrace AI’s potential, the integration of CSaaS becomes essential in creating a secure, innovative, and patient-centered healthcare landscape.

As we navigate this transformative era in healthcare, the strategic integration of Cyber Security as a Service and AI/ML security emerges as a cornerstone in fortifying the healthcare industry against evolving cyber threats while harnessing the potential of advanced technologies for the betterment of patient care.

The post Fortifying Healthcare Networks: The Synergy of Cyber Security as a Service and AI/ML Security first appeared on AITechTrend.

Unlike traditional ML, which requires data scientists to manually manage data and model development using languages like Python, no-code ML offers a simplified solution. 

Some of the important No-code ML Platforms are mentioned below

1. MonkeyLearn: A no-code AI platform that enables businesses to turn data into actionable insights with text analysis.
2. Obviously.ai: A no-code tool that makes predictive analytics accessible to non-technical users, focusing on ease of use and rapid deployment.
3. Apple CreateML: A development tool from Apple that allows developers to train custom machine learning models on Mac devices with minimal coding.
4. Amazon SageMaker: A comprehensive service that provides every tool needed to build, train, and deploy machine learning models at scale.
5. Google AutoML: A suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs.
6. Microsoft Lobe: A simple, no-code desktop application that makes it easy to train and deploy machine learning models based on images.
7. DataRobot: An enterprise AI platform that democratizes data science and automates the end-to-end process for building, deploying, and maintaining machine learning models.
8. Apple CreateML: Similar to CreateML, mentioned as a distinct entry but appears to be a duplicate.
9. Google Teachable Machine: A web-based tool for creating machine learning models that is fast, easy, and accessible to everyone.
10. RunwayML: Offers creators the ability to use artificial intelligence in an intuitive way, providing tools for video, image, and text processing.
11. PyCaret: An open-source, low-code Python library for machine learning that aims to reduce the time from hypothesis to insights.Levity: A no-code AI tool designed for automating workflows, allowing users to build AI-powered applications without writing code.
12.  Superannotate: A platform for image annotation that streamlines the process of annotating images for computer vision tasks, offering efficient tools and workflows.

Let us explore some important no-code ML platforms that are making a difference in the year 2024

1. MonkeyLearn:

Technical Features: 

• MonkeyLearn is a text analysis platform that offers pre-trained models for tasks such as sentiment analysis, keyword extraction, and topic classification. 
• It also provides tools for custom model creation, allowing users to train models using their own data.

Advancements: 

• MonkeyLearn continuously updates its models and algorithms to improve accuracy and performance. 
• It offers integrations with popular platforms like Zapier and Zendesk, making it easy to incorporate text analysis into existing workflows.

Challenges: 

• While MonkeyLearn simplifies the process of text analysis, users may still face challenges in fine-tuning models for specific use cases or dealing with noisy or unstructured data.

Coding Requirement: 

• MonkeyLearn offers a user-friendly interface that requires minimal coding, making it accessible to users with varying levels of technical expertise.

How it Helps:

• MonkeyLearn empowers data scientists and analysts to extract valuable insights from text data without the need for extensive coding or machine learning expertise.

2. Obviously.ai:

Technical Features: 

• Obviously.ai  is a no-code automated machine learning platform that simplifies the process of building predictive models.
• Its features include data cleaning, feature selection, and model training. 

Advancements: 

• Obviously.ai leverages advancements in automated machine learning to streamline the model-building process and improve prediction accuracy.

Challenges: 

• While Obviously.ai simplifies the process of building predictive models, users may encounter challenges in interpreting model results or selecting the most appropriate algorithms for their data.

Coding Requirement: 

• Obviously.ai is designed to be accessible to users without coding experience, allowing them to create and deploy machine learning models through a simple, intuitive interface.

How it Helps: 

• Obviously.ai enables business users and non-technical professionals to harness the power of machine learning for predictive analytics tasks, without relying on data scientists or engineers.

3. CreateML (Apple):

Technical Features: 

• CreateML is a machine learning framework provided by Apple, designed to make it easy for developers to train and deploy machine learning models on Apple devices. 
• It offers a range of pre-trained models and tools for custom model creation, including support for image classification, object detection, and natural language processing tasks.

Advancements: 

• CreateML leverages Apple’s hardware and software ecosystem to deliver high-performance machine learning models that can run efficiently on iOS, macOS, watchOS, and tvOS devices. 
• It incorporates optimizations for Apple’s custom silicon, such as the Neural Engine in Apple’s M-series chips, to accelerate model inference and improve efficiency.

Challenges: 

• While CreateML simplifies the process of building and deploying machine learning models for Apple platforms, developers may face challenges in optimizing models for performance or integrating them into existing applications. 
• Additionally, creating custom models may require expertise in machine learning concepts and data preprocessing techniques.

Coding Requirement: 

• CreateML provides a graphical interface for training and evaluating machine learning models, reducing the need for coding and making it accessible to developers with varying levels of expertise. 
• It offers a drag-and-drop interface for building and training models, as well as APIs for integrating models into iOS, macOS, watchOS, and tvOS applications using Swift or Objective-C.

How it Helps: 

• CreateML enables developers to incorporate machine learning capabilities into their Apple applications, allowing them to deliver more intelligent and personalized experiences to users. 
• By providing a user-friendly interface and seamless integration with Apple’s development tools and platforms, CreateML empowers developers to leverage the power of machine learning without the need for extensive expertise or infrastructure.

4. Amazon SageMaker:

Technical Features: 

• Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS), offering tools for building, training, and deploying machine learning models at scale.

Advancements: 

• SageMaker incorporates the latest advancements in machine learning research and infrastructure to deliver high-performance models with minimal setup and configuration.

Challenges: 

• While SageMaker simplifies many aspects of the machine learning workflow, users may encounter challenges in managing large datasets, optimizing model performance, or integrating models with existing systems.

Coding Requirement:

• SageMaker provides a range of built-in algorithms and pre-configured environments for common machine learning tasks, reducing the need for coding and making it accessible to users with varying levels of expertise.

How it Helps: 

• SageMaker enables data scientists and machine learning engineers to accelerate the development and deployment of machine learning models, leveraging the scalability and flexibility of the AWS cloud.

5. Google AutoML:

Technical Features: 

• Google AutoML is a suite of machine learning tools that automate the process of building and deploying custom machine learning models. It offers AutoML Vision, AutoML Natural Language, and AutoML Tables for image classification, text analysis, and tabular data modeling, respectively.

Advancements: 

• AutoML leverages Google’s expertise in machine learning and infrastructure to deliver state-of-the-art models with minimal manual intervention, enabling users to focus on solving business problems rather than technical details.

Challenges: 

• While AutoML simplifies many aspects of the machine learning workflow, users may encounter challenges in fine-tuning models for specific use cases or dealing with complex data.

Coding Requirement: 

• AutoML provides a user-friendly interface that requires minimal coding, making it accessible to users with varying levels of technical expertise.

How it Helps: 

• AutoML empowers businesses to harness the power of machine learning for a wide range of tasks, from image recognition to natural language processing, without the need for extensive machine learning expertise or infrastructure.

6. Microsoft Lobe:

Technical Features: 

• Microsoft Lobe is a desktop application that enables users to build and train custom machine learning models using a simple, visual interface. 
• It supports image classification and object detection tasks.

Advancements: 

• Lobe leverages Microsoft’s research in machine learning and user experience design to deliver an intuitive and accessible tool for building custom models without writing any code.

Challenges: 

• While Lobe simplifies the process of building machine learning models, users may encounter challenges in optimizing models for performance or integrating them into existing workflows.

Coding Requirement: 

• Lobe eliminates the need for coding by providing a visual interface for model creation and training, making it accessible to users with varying levels of technical expertise.

How it Helps: 

• Lobe enables developers and businesses to harness the power of machine learning for image recognition and object detection tasks, without the need for specialized machine learning expertise or infrastructure.

7. DataRobot:

Technical Features: 

• DataRobot is an automated machine learning platform that enables users to build and deploy predictive models without writing any code. 
• It offers features such as data preparation, feature engineering, and model evaluation.

Advancements: 

• DataRobot leverages advancements in automated machine learning to streamline the model-building process and deliver accurate predictions across a wide range of use cases.

Challenges: 

• While DataRobot simplifies many aspects of the machine learning workflow, users may encounter challenges in interpreting model results, understanding model behavior, or integrating models into existing systems.

Coding Requirement: 

• DataRobot provides a user-friendly interface that requires minimal coding, making it accessible to users with varying levels of technical expertise.

How it Helps: 

• DataRobot empowers businesses to harness the power of machine learning for predictive analytics tasks, enabling them to make data-driven decisions and improve operational efficiency without relying on data scientists or engineers.

8. Google Teachable Machine:

Technical Features: 

• Google Teachable Machine is a web-based tool that enables users to create custom machine learning models for image classification, sound classification, and pose estimation tasks using a simple, intuitive interface.

Advancements: 

• Teachable Machine leverages Google’s expertise in machine learning and user experience design to deliver an accessible tool for building custom models without writing any code.

Challenges: 

• While Teachable Machine simplifies the process of building machine learning models, users may encounter challenges in optimizing models for performance or integrating them into existing workflows.

Coding Requirement: 

• Teachable Machine eliminates the need for coding by providing a visual interface for model creation and training, making it accessible to users with varying levels of technical expertise.

How it Helps: 

• Teachable Machine enables students, educators, and hobbyists to experiment with machine learning concepts and create custom models for a variety of tasks, without the need for specialized expertise or infrastructure.

9. RunwayML:

Technical Features: 

• RunwayML is a platform that enables artists, designers, and developers to create and experiment with machine learning models for creative applications. 
• It offers a range of pre-trained models and tools for custom model creation.

Advancements: 

• RunwayML leverages the latest advancements in machine learning research and creative technology to empower users to explore new forms of artistic expression and interactive design.

Challenges: 

• While RunwayML simplifies the process of working with machine learning models, users may encounter challenges in integrating models into their creative projects or understanding the technical details of model behavior.

Coding Requirement: 

• RunwayML provides a user-friendly interface that requires minimal coding, making it accessible to users with varying levels of technical expertise.

How it Helps: 

• RunwayML enables artists, designers, and developers to incorporate machine learning into their creative workflows, allowing them to explore new possibilities and push the boundaries of interactive design and digital art.

10. PyCaret:

Technical Features: 

• PyCaret is an open-source machine learning library that simplifies the process of building, training, and deploying machine learning models in Python. 
• It offers a range of pre-built functions and utilities for common machine learning tasks.

Advancements: 

• PyCaret leverages the latest advancements in machine learning research and software engineering to deliver a powerful and user-friendly library for building predictive models with minimal code.

Challenges: 

• While PyCaret simplifies many aspects of the machine learning workflow, users may encounter challenges in fine-tuning models for specific use cases or dealing with complex data.

Coding Requirement: 

• PyCaret reduces the need for coding by providing a high-level API and a range of pre-built functions for common machine learning tasks, making it accessible to users with varying levels of technical expertise.

How it Helps: 

• PyCaret empowers data scientists and machine learning engineers to accelerate the development and deployment of predictive models, enabling them to focus on solving business problems rather than technical details.

11. Levity:

(https://levity.ai/)

Technical Features: 

• Levity is a platform that enables businesses to automate document processing and data extraction tasks using machine learning. 
• It offers pre-built models for tasks such as invoice processing, receipt extraction, and form recognition.

Advancements: 

• Levity leverages advancements in machine learning research and document processing technology to deliver accurate and efficient solutions for automating repetitive tasks.

Challenges: 

• While Levity simplifies the process of automating document processing tasks, users may encounter challenges in fine-tuning models for specific use cases or integrating them into existing workflows.

Coding Requirement: 

• Levity provides a user-friendly interface that requires minimal coding, making it accessible to users with varying levels of technical expertise.

How it Helps: 

• Levity empowers businesses to streamline document processing workflows, reduce manual labor, and improve operational efficiency by automating repetitive tasks using machine learning.

12. SuperAnnotate

Technical Features: 

• Super Annotate is an image annotation platform that offers pre-built tools and workflows for tasks such as object detection, image segmentation, and image classification. 
• It provides a variety of annotation types, including bounding boxes, polygons, keypoints, and semantic segmentation masks. 
• Additionally, Super Annotate offers tools for custom model creation, allowing users to train models using their own annotated data.

Advancements: 

• Super Annotate continuously updates its annotation tools and algorithms to improve accuracy and efficiency. 
• It offers integrations with popular machine learning frameworks like TensorFlow and PyTorch, enabling seamless integration of annotated data into model training pipelines. 
• Super Annotate leverages advancements in computer vision research to deliver state-of-the-art annotation capabilities that drive better model performance.

Challenges: 

• While Super Annotate simplifies the process of image annotation, users may still face challenges in annotating complex images or dealing with large datasets. 
• Fine-tuning annotation models for specific use cases or ensuring consistency in annotations across annotators may require additional effort and coordination.

Coding Requirement: 

• Super Annotate offers a user-friendly interface that requires minimal coding, making it accessible to users with varying levels of technical expertise. 
• It provides a visual annotation tool and collaborative workspace for teams to annotate images efficiently and accurately, reducing the need for manual coding and enabling rapid annotation iteration.

How it Helps: 

• Super Annotate empowers data scientists and computer vision engineers to annotate images and train models effectively without the need for extensive coding or computer vision expertise. 
• By providing a seamless platform for image annotation and model training, Super Annotate enables teams to extract valuable insights from image data and develop robust computer vision applications.

The post Empowering Without Code: Top 12 No-Code Machine Learning Platforms in 2024 first appeared on AITechTrend.

Machine learning, a subset of artificial intelligence, has revolutionized the way we process and analyze data, leading to groundbreaking advancements across various industries. This article delves into the current landscape of machine learning and explores its booming influence across various sectors like Healthcare, Finance, Marketing and Sales, Autonomous Vehicles, Manufacturing and Logistics and Entertainment and Media.

Why Use Online Platforms to Sharpen Your Programming Skills?

As per Statista, in 2023, almost 80% of software developers learned how to code via internet sources such as blogs and videos. Conversely, about ten percent of respondents stated that they learned to code by attending coding boot camps.

Furthermore, online learning environments allow flexible learning schedules and access to an enormous library of programming resources from any location with an internet connection. They are a great option for anyone looking to hone their programming skills because of their accessibility, adaptability, and variety of resources. 

Whether you’re a beginner or an experienced developer, these platforms offer the tools and knowledge needed to excel in the dynamic world of programming. But make sure you research and go through reviews, like Flatiron School reviews, before choosing any platforms. Here are top 5 online platforms that can help you enhance your machine learning expertise.

• Coursera

Several machine learning courses from reputable universities and institutes are available on the well-known online learning site Coursera. A variety of machine learning subjects, such as deep learning, reinforcement learning, and natural language processing, are offered by Coursera through its collaboration with prestigious universities. Students can apply their knowledge to real-world challenges through Coursera’s practical projects and assignments, which makes it a great resource for developing practical skills.

• Udacity

Another well-known website that provides specialized nanodegree programs in artificial intelligence and machine learning is Udacity. The curriculum for these nanodegree programs is current and relevant because they are created in partnership with major players in the industry like Google, IBM, and Amazon. Through the use of real-world projects, Udacity’s project-based learning methodology helps students hone their machine learning abilities.

• Udemy

Programming is one of the many subjects covered by the online course marketplace Udemy. It provides an extensive array of courses at various cost points, frequently with unlimited access. Furthermore, a wide audience can access Udemy’s vast course marketplace. Courses are reasonably priced, and you have the option to review the material whenever you’d want. An instructor leads several of the courses and offers helpful advice.

• Pluralsight

Tech skill evaluations, extensive collections of tech-related courses, and learning pathways geared toward job advancement are all provided by Pluralsight. Pluralsight is designed for tech workers who wish to keep improving their skills as well. It is a useful tool for job promotion in the tech sector since skill assessments aid in the identification of knowledge gaps and learning paths direct your professional development.

• edX

edX is an online learning platform that partners with renowned universities and institutions to offer a wide range of courses, including many related to programming and technology. It provides access to high-quality education, with the option to earn certificates from prestigious institutions.

Programming abilities have a bright future ahead of them. Blockchain, machine learning, and artificial intelligence are examples of emerging technologies that are changing sectors and opening up new job opportunities. Through a dedication to ongoing education and the utilization of resources such as the ones we’ve covered, you’ll be more prepared to take on the opportunities and difficulties that lie ahead.

The post Machine Learning Mastery: 5 Online Platforms for Hands-On Skill Refinement first appeared on AITechTrend.

How Does Unsupervised Reinforcement Learning Work?

As discussed above, some AI algorithms use unsupervised reinforcement learning methods to give their system the freedom to think and learn new things and also solve a particular problem in different ways. This can be done using the trial and error method or predicting the success rate of a solution before attending to a problem. This type of learning also has a different type of datasets.

In this type of machine learning a machine considers its environment as a teacher and the solution as an intrinsic reward. Basically, the machine develops itself according to its environment and also gathers more data to develop and act accordingly.

If explained more thoroughly, then, when a machine’s algorithm is made in such a way that it will gather more information from its environment and give solutions by combining the old data and the new data which it has attained while in the environment. The data which is given to the machine is uncategorized and the machines are made in such a way that they can categorise the data given.

This type of machine learning is also quite challenging as it has its drawbacks. They are listed below:

1. This type of machine learning program faces challenges for creating labels, and annotations. The maintenance of these programs requires a lot of time and effort.
2. Reward allocation is difficult as to what type of rewards will benefit the machine in what ways. Also, the rewards should be continuous.
3. Collecting data on human behavior is also challenging as there is no specific annotation for it.

Reinforcement learning is generally used when a machine has to do a human’s work. So, unsupervised reinforcement learning is used to develop machines that are doing a human’s work to ensure that the machine can fully do what a human can do. Using both types of machine learning models can eradicate the drawbacks faced by both models and increase the productivity of a machine.  

At the start of this article, we talked about how this type of machine learning is a combination of two machine learning techniques. So, to further elaborate on the same, this type of machine learning first uses unsupervised learning’s two basic divisions that are generative and non-generative learning. Through these two models, it gathers and learns a large amount of data on how humans behave when react in a certain way. Once that is known then these models can be used to generate data to plan according to the behavior. After planning is done reinforcement learning is used to decide the reactions that will help the machine to earn rewards.

In order to speed up the learning process non-generative model can be used as an external learning model. To ensure more efficiency of the machine the use of a generative model of unsupervised learning can help a lot. The use of unsupervised learning in reinforcement learning can also speed up the learning process of a machine and also save time.

With the combination of these two models of learning a developer can create a high-value AI application that can solve complex problems in less time. Machines and applications created using these models are used in various research and development areas related to various industries across the world. AI apps that are created using this method are flexible and have a wider scope of addressing problems than apps that are created using a single model. The reason behind this is the drawback of each learning model.

Applications that use these types of learning models are used for self-driven cars, gaming, and healthcare purposes. This is not only limited to these fields but also fields that need continuous solutions to problems and also a human touch. Through this learning model apps can adjust to any given environment develop their own data set and react accordingly. The features of these two learning models enable the AI application and machine to explore and also exploit the environment to learn and gather new information about its environment. Unsupervised reinforcement learning model has a drawback in that after a certain limit, the same reaction occurs. This drawback will also be resolved shortly as the technology field develops. For decades many tech giants and other giants of their field have used machines that are manufactured with the use of unsupervised reinforcement learning to complete their day-to-day tasks at various facilities that involve in production of their products or commencing their services to its consumers.    

There is another model that can be used to develop AI applications. This model is known as a supervised learning method. In the supervised learning method the machine needs human supervision under which it completes the given task. There can be a combination of supervised learning and reinforcement learning models to create applications and machines that can help improve the production rate of an organization. There can also be a combination of all three types of learning models to create an application that is efficient in solving more problems that are much tougher for a machine or an application developed using an unsupervised reinforcement learning model.

As the demand for more interactive AI systems is growing there is a need to develop AI apps that are more efficient and effective in solving any kind of problem put in front of them. Therefore the use of a learning model that trains the AI apps in a way that it can gather and adjust itself in an environment is mandatory. Due to this requirement, unsupervised reinforcement learning of a machine or an AI has become necessary to curb complex problems put forward by people who will use this application. The technology of artificial intelligence will come into the hands of common people who will probably be a layman. The development of an application that can solve almost every problem is becoming mandatory work for developers to sell their applications at a good rate. Unsupervised reinforcement learning model is helping organizations to spend less funds on assigning human help to teach the machines their work this has increased the production rate of the company and has lessened their expenses.

Conclusion

Unsupervised reinforcement learning model is a very helpful model for making applications that can cover more ground and also help companies increase their productivity. So far this all the things that are needed to be known about unsupervised reinforcement learning. There is more to find on how to teach a machine to complete and address a problem and also to explore this model that will make the application more efficient.

The post What is Unsupervised Reinforcement Learnings? first appeared on AITechTrend.

To avert the pitfalls of GIGO, meticulous measures such as data cleaning, enrichment, or augmentation are imperative. As we embark on the journey toward AI excellence, the core principle remains clear: commitment to ensuring that input data is enriched and high quality is paramount.

Let’s understand,

What good quality training data looks like?

It is:

1. Relevant

• Definition: Dataset includes only attributes providing meaningful information.
• Importance: Requires domain knowledge for feature selection.
• Impact: Enhances model focus and prevents distraction from irrelevant features.

2. Consistent

• Definition: Similar attribute values correspond consistently to similar labels.
• Importance: Maintains dataset integrity for reliable associations.
• Impact: Facilitates smooth model training with predictable relationships.

3. Uniform

• Definition: Comparable values across all data points, minimizing outliers.
• Importance: Reduces noise and ensures model stability.
• Impact: Promotes stable learning patterns for effective generalization.

4. Comprehensive

• Definition: The dataset includes enough features to address various scenarios.
• Importance: Provides a holistic understanding of robust models.
• Impact: Enables effective handling of diverse real-world challenges.

Factors affecting training data quality

Several factors influence the quality of training datasets, impacting the model’s performance and generalization. Understanding these is crucial for developing strategies to enhance dataset quality. Here are some of the key aspects that can affect the quality of training datasets:

1. Data source selection

2. Data collection methods

3. Data volume and diversity

4. Data preprocessing technique

5. Labeling accuracy

6. Data bias

7. Domain-specific challenges

Addressing the challenges of low-quality data with enrichment 

Raw data, while essential, often lacks completeness or may not capture the full context needed for effective machine learning. Enter data enrichment – the process of enhancing and expanding the raw dataset to improve its quality. This helps in creating detailed training datasets that provide comprehensive information to AI models. Failure to enrich data properly can compromise the dataset’s quality, thereby constraining the model’s understanding and leading to inaccurate predictions.

Here are the best practices to address the challenges of substandard data:

1. Augment with external data

Reasoning: Supplementing your dataset with information extracted from external sources can provide additional context and diverse examples.

Example: Enhancing customer profiles with socioeconomic data from external databases

2. Feature engineering

Reasoning: Create new features derived from existing ones or external sources to provide the model with more relevant information.

Example: Extracting sentiment scores from user reviews to enrich a sentiment analysis model

3. Class imbalance

Reasoning: Ensure a balanced representation of different classes to prevent bias and improve model performance.

Example: Adding more examples of rare medical conditions in a healthcare dataset

4. Temporal enrichment

Reasoning: Incorporate time-related features to capture trends and seasonality, especially important for time-series data.

Example: Adding timestamps, day of the week, or month to sales data for better trend analysis

5. Geo-enrichment

Reasoning: Enhance datasets with geographical information to provide spatial context.

Example: Adding latitude and longitude to customer addresses for location-based analysis

6. Text data enhancement

Reasoning: Refine and augment the text data to extract valuable insights.

Example: Breaking down text into tokens and simplifying words to their base form to improve the quality and effectiveness of natural language processing models.

7. Image data augmentation

Reasoning: Introduce variations in images to diversify the dataset and improve the model’s ability to generalize.

Example: Rotating, flipping, or adjusting the brightness of images in a dataset for image recognition models

8. Data handling

Reasoning: Address missing values by either removing irrelevant instances or filling gaps through imputation.

Example: Populating missing customer age values by calculating the average age from the available data

Conducting data enrichment: Strategies and considerations

1. In-house teams

Pros:

• Domain expertise: Internal teams possess deep knowledge of the business domain, ensuring enriched data aligns closely with organizational goals.
• Data security: In-house processes provide greater control and security over sensitive company information.
• Customization: Tailoring enrichment strategies to specific business needs is more feasible with an in-house team.

Cons:

• Resource intensive: Building and maintaining an in-house team requires substantial time, effort, and resources.
• Skill gaps: Ensuring a diverse skill set within the team may be challenging, leading to limitations in certain enrichment techniques.
• Scalability concerns: Scaling operations might be constrained by the available resources, hindering the ability to handle large-scale enrichment projects.

2. Tools

Pros:

• Efficiency: Enrichment tools automate processes, saving time and reducing manual effort.
• Scalability: Tools can handle large datasets and scale operations more easily than manual methods.
• Consistency: Automated tools ensure a consistent application of enrichment techniques across the dataset.

Cons:

• Costs: Some advanced tools may incur licensing or subscription costs.
• Lack of customization: Pre-built tools may not be tailored to specific organizational requirements, limiting customization options.
• Learning curve: Training teams on new tools might be necessary, initially slowing down the process.

3. Outsourcing

Pros:

• Expertise access: Outsourcing allows access to specialists with expertise in various enrichment techniques.
• Cost efficiency: It can be cost-effective compared to maintaining an in-house team, especially for short-term projects.
• Scalability: B2B data enrichment outsourcing partners can quickly scale operations based on project requirements.

Cons:

• Data security: Sharing data with external entities might raise security and privacy concerns.
• Communication: Coordination and communication issues may arise due to geographical or cultural differences.
• Dependency: Relying on external providers may pose challenges if there are changes in the outsourcing arrangement.

The Next Step 

Make a fair choice!

To enhance AI reliability, ensure your training data is relevant, consistent, uniform, and comprehensive. Address challenges through smart data enrichment, considering strategies like external data augmentation, feature engineering, and more.

Dive into data enrichment best practices. Explore tools, build in-house expertise, or consider outsourcing. Elevate your AI game by fortifying your data – it’s the key to unlocking accurate predictions and insights.

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