Enhancing Edge AI with RagaAI Integration on NVIDIA Metropolis

Siddharth Jain

Mar 15, 2024

nvidia metropolis
nvidia metropolis
nvidia metropolis
nvidia metropolis

In the fast-evolving landscape of Edge AI applications, ensuring the robustness and reliability of models is paramount. With the integration of RagaAI, a comprehensive AI testing platform, into NVIDIA Metropolis Microservices, developers now have a powerful toolset at their disposal to enhance the quality and performance of their edge AI solutions. 

Ensuring the reliability of AI systems through thorough testing is a significant challenge for developers. The dynamic nature of AI applications requires constant vigilance to detect and address potential issues. However, without comprehensive testing, developers risk deploying AI models that may not perform as expected, leading to inaccuracies and inefficiencies in real-world scenarios. Effective AI testing is essential to verify the accuracy, robustness, and general performance of AI models before deployment.

For example: Data drift, an often-overlooked challenge in AI, poses a significant threat to model efficacy. Before we delve into the integration's benefits, let's understand data drift and its adverse effects on AI performance.

Data drift, refers to the gradual divergence of data distributions over time, leading to degraded model performance. This problem, made worse by changing environmental conditions, presents a big challenge for AI reliability.

To grasp how the integration of RagaAI with NVIDIA Metropolis Microservices can improve edge AI solutions, let's first delve into their functionalities.


NVIDIA Metropolis Microservices 

NVIDIA Metropolis microservices constitute a suite of adaptable, cloud-native components designed for crafting vision AI applications and solutions. This introduces an array of APIs and microservices specifically optimised for the edge with both NVIDIA Jetson Orin platform and Enterprise GPUs, further expediting the creation and deployment of edge AI applications.

These microservices empower developers to modernise their AI application infrastructure, streamline workflows, and future-proof their applications. By seamlessly integrating cutting-edge generative AI capabilities, developers gain access to a host of functionalities including video storage and management, prebuilt AI perception pipelines, tracking algorithms, system monitoring, secure edge-to-cloud IoT connectivity, and more. 

RagaAI provides a data monitoring service to complement the existing services to provide data and model related insights like drift and other key metrics. This can enhance the Vision AI solution by providing model observability.  


RagaAI

Ensuring AI quality is always a problem for data science teams around the world, whether it is dealing with experiments stuck in the lab or biased / non-robust AI in production.

RagaAI stands as a leading AI testing platform, specialising in computer vision applications. With over 300+ tests available, RagaAI automates the detection of issues, identifies root causes, and empowers users to resolve them effectively. Its multimodal capabilities cover a wide range of computer vision tasks, including object detection, semantic segmentation, instance segmentation, image classification, OCR, super-resolution, and more. 

RagaAI Testing Platform is designed to solve this exact problem by offering the following capabilities to data scientists - 

  1. Comprehensive Testing: 300+ tests to identify any issues with data, model and AI deployment settings. 

  2. Breakthrough RagaAI DNA Technology: Creating foundational model based embedding spaces to detect and fix issues related to labelling quality, drift detection, active learning, edge case detection and others. 

  3. Multi Modal Platform: Enabling testing across Computer Vision, LLMs / NLP and Structured Data Applications individually or a part of a complex pipeline. 

  4. Accessibility and Usability for Various Expertise Levels: The field of AI is diverse, with professionals ranging from seasoned experts to novices. However, many AI development platforms are either too complex for beginners or too simplistic for advanced users, creating a gap in usability and accessibility. 

The RagaAI product is designed to help data science teams focus on building the best AI products and not get bogged down with crucial but massive infrastructure development projects.


Leveraging the Integration

The integration of RagaAI, with the Analytics Module of NVIDIA Metropolis Microservices presents a powerful combination for real-time video data processing and analysis. The Analytics Module, nestled within the comprehensive Metropolis platform, is tailored to handle the intricacies of video data, providing robust capabilities for rapid analysis.

By leveraging RagaAI's advanced testing functionalities and Metropolis framework's real-time processing prowess, users can achieve enhanced accuracy and efficiency in analysing video content. RagaAI's testing platform offers comprehensive tools for assessing the performance and reliability of AI algorithms, ensuring rigorous evaluation before and after deployment. 

The integration of RagaAI into NVIDIA Metropolis Microservices enables a streamlined workflow for developers. After deploying their edge AI models using Metropolis, users can seamlessly initiate RagaAI tests to assess model and data-related issues. 

As an example, let’s say you're monitoring a live video stream from a surveillance camera. The camera, part of a multi-camera tracking system, is tracking people passing through its field of view.

However, as the weather changes and clouds roll in, the lighting conditions deteriorate. This can lead to challenges for the AI model deployed on the edge, impacting its ability to accurately detect and track individuals.

This scenario underscores the importance of integrating RagaAI with the analytics module of Metropolis. With RagaAI's advanced testing capabilities seamlessly integrated, users gain real-time visibility into data and model-related issues. 

As the user is observing the analytics on a Kibana Dashboard within the Metropolis platform, the lighting conditions worsen due to the cloudy weather, the drift graph generated by RagaAI indicates a significant increase in drift from a specific time. This suggests that the AI model may not have been adequately trained on data points with low lighting conditions or cloudy weather.

By leveraging RagaAI's extensive testing suite in conjunction with Metropolis, users can proactively address such issues. They can receive timely alerts about potential performance degradation and take corrective actions to maintain the effectiveness of their edge AI solutions. 

Moreover, they can also perform active learning easily (model retraining to fix cloudy weather conditions) with RagaAI’s active learning intelligence.

Ultimately, this integration empowers developers to continuously refine and optimise their models, ensuring consistent performance and reliability in diverse environmental conditions. This is clear from the retrained model’s performance in the same cloudy conditions as seen below.

Conclusion 

The integration of RagaAI with NVIDIA Metropolis Microservices represents a significant advancement in the field of Edge AI development. By combining the robust capabilities of both platforms, developers can ensure the quality, reliability, and accuracy of their edge AI applications. With access to a comprehensive suite of tests and seamless integration into the development workflow, developers are empowered to deliver cutting-edge edge AI solutions that meet the highest standards of performance and reliability.

In the fast-evolving landscape of Edge AI applications, ensuring the robustness and reliability of models is paramount. With the integration of RagaAI, a comprehensive AI testing platform, into NVIDIA Metropolis Microservices, developers now have a powerful toolset at their disposal to enhance the quality and performance of their edge AI solutions. 

Ensuring the reliability of AI systems through thorough testing is a significant challenge for developers. The dynamic nature of AI applications requires constant vigilance to detect and address potential issues. However, without comprehensive testing, developers risk deploying AI models that may not perform as expected, leading to inaccuracies and inefficiencies in real-world scenarios. Effective AI testing is essential to verify the accuracy, robustness, and general performance of AI models before deployment.

For example: Data drift, an often-overlooked challenge in AI, poses a significant threat to model efficacy. Before we delve into the integration's benefits, let's understand data drift and its adverse effects on AI performance.

Data drift, refers to the gradual divergence of data distributions over time, leading to degraded model performance. This problem, made worse by changing environmental conditions, presents a big challenge for AI reliability.

To grasp how the integration of RagaAI with NVIDIA Metropolis Microservices can improve edge AI solutions, let's first delve into their functionalities.


NVIDIA Metropolis Microservices 

NVIDIA Metropolis microservices constitute a suite of adaptable, cloud-native components designed for crafting vision AI applications and solutions. This introduces an array of APIs and microservices specifically optimised for the edge with both NVIDIA Jetson Orin platform and Enterprise GPUs, further expediting the creation and deployment of edge AI applications.

These microservices empower developers to modernise their AI application infrastructure, streamline workflows, and future-proof their applications. By seamlessly integrating cutting-edge generative AI capabilities, developers gain access to a host of functionalities including video storage and management, prebuilt AI perception pipelines, tracking algorithms, system monitoring, secure edge-to-cloud IoT connectivity, and more. 

RagaAI provides a data monitoring service to complement the existing services to provide data and model related insights like drift and other key metrics. This can enhance the Vision AI solution by providing model observability.  


RagaAI

Ensuring AI quality is always a problem for data science teams around the world, whether it is dealing with experiments stuck in the lab or biased / non-robust AI in production.

RagaAI stands as a leading AI testing platform, specialising in computer vision applications. With over 300+ tests available, RagaAI automates the detection of issues, identifies root causes, and empowers users to resolve them effectively. Its multimodal capabilities cover a wide range of computer vision tasks, including object detection, semantic segmentation, instance segmentation, image classification, OCR, super-resolution, and more. 

RagaAI Testing Platform is designed to solve this exact problem by offering the following capabilities to data scientists - 

  1. Comprehensive Testing: 300+ tests to identify any issues with data, model and AI deployment settings. 

  2. Breakthrough RagaAI DNA Technology: Creating foundational model based embedding spaces to detect and fix issues related to labelling quality, drift detection, active learning, edge case detection and others. 

  3. Multi Modal Platform: Enabling testing across Computer Vision, LLMs / NLP and Structured Data Applications individually or a part of a complex pipeline. 

  4. Accessibility and Usability for Various Expertise Levels: The field of AI is diverse, with professionals ranging from seasoned experts to novices. However, many AI development platforms are either too complex for beginners or too simplistic for advanced users, creating a gap in usability and accessibility. 

The RagaAI product is designed to help data science teams focus on building the best AI products and not get bogged down with crucial but massive infrastructure development projects.


Leveraging the Integration

The integration of RagaAI, with the Analytics Module of NVIDIA Metropolis Microservices presents a powerful combination for real-time video data processing and analysis. The Analytics Module, nestled within the comprehensive Metropolis platform, is tailored to handle the intricacies of video data, providing robust capabilities for rapid analysis.

By leveraging RagaAI's advanced testing functionalities and Metropolis framework's real-time processing prowess, users can achieve enhanced accuracy and efficiency in analysing video content. RagaAI's testing platform offers comprehensive tools for assessing the performance and reliability of AI algorithms, ensuring rigorous evaluation before and after deployment. 

The integration of RagaAI into NVIDIA Metropolis Microservices enables a streamlined workflow for developers. After deploying their edge AI models using Metropolis, users can seamlessly initiate RagaAI tests to assess model and data-related issues. 

As an example, let’s say you're monitoring a live video stream from a surveillance camera. The camera, part of a multi-camera tracking system, is tracking people passing through its field of view.

However, as the weather changes and clouds roll in, the lighting conditions deteriorate. This can lead to challenges for the AI model deployed on the edge, impacting its ability to accurately detect and track individuals.

This scenario underscores the importance of integrating RagaAI with the analytics module of Metropolis. With RagaAI's advanced testing capabilities seamlessly integrated, users gain real-time visibility into data and model-related issues. 

As the user is observing the analytics on a Kibana Dashboard within the Metropolis platform, the lighting conditions worsen due to the cloudy weather, the drift graph generated by RagaAI indicates a significant increase in drift from a specific time. This suggests that the AI model may not have been adequately trained on data points with low lighting conditions or cloudy weather.

By leveraging RagaAI's extensive testing suite in conjunction with Metropolis, users can proactively address such issues. They can receive timely alerts about potential performance degradation and take corrective actions to maintain the effectiveness of their edge AI solutions. 

Moreover, they can also perform active learning easily (model retraining to fix cloudy weather conditions) with RagaAI’s active learning intelligence.

Ultimately, this integration empowers developers to continuously refine and optimise their models, ensuring consistent performance and reliability in diverse environmental conditions. This is clear from the retrained model’s performance in the same cloudy conditions as seen below.

Conclusion 

The integration of RagaAI with NVIDIA Metropolis Microservices represents a significant advancement in the field of Edge AI development. By combining the robust capabilities of both platforms, developers can ensure the quality, reliability, and accuracy of their edge AI applications. With access to a comprehensive suite of tests and seamless integration into the development workflow, developers are empowered to deliver cutting-edge edge AI solutions that meet the highest standards of performance and reliability.

In the fast-evolving landscape of Edge AI applications, ensuring the robustness and reliability of models is paramount. With the integration of RagaAI, a comprehensive AI testing platform, into NVIDIA Metropolis Microservices, developers now have a powerful toolset at their disposal to enhance the quality and performance of their edge AI solutions. 

Ensuring the reliability of AI systems through thorough testing is a significant challenge for developers. The dynamic nature of AI applications requires constant vigilance to detect and address potential issues. However, without comprehensive testing, developers risk deploying AI models that may not perform as expected, leading to inaccuracies and inefficiencies in real-world scenarios. Effective AI testing is essential to verify the accuracy, robustness, and general performance of AI models before deployment.

For example: Data drift, an often-overlooked challenge in AI, poses a significant threat to model efficacy. Before we delve into the integration's benefits, let's understand data drift and its adverse effects on AI performance.

Data drift, refers to the gradual divergence of data distributions over time, leading to degraded model performance. This problem, made worse by changing environmental conditions, presents a big challenge for AI reliability.

To grasp how the integration of RagaAI with NVIDIA Metropolis Microservices can improve edge AI solutions, let's first delve into their functionalities.


NVIDIA Metropolis Microservices 

NVIDIA Metropolis microservices constitute a suite of adaptable, cloud-native components designed for crafting vision AI applications and solutions. This introduces an array of APIs and microservices specifically optimised for the edge with both NVIDIA Jetson Orin platform and Enterprise GPUs, further expediting the creation and deployment of edge AI applications.

These microservices empower developers to modernise their AI application infrastructure, streamline workflows, and future-proof their applications. By seamlessly integrating cutting-edge generative AI capabilities, developers gain access to a host of functionalities including video storage and management, prebuilt AI perception pipelines, tracking algorithms, system monitoring, secure edge-to-cloud IoT connectivity, and more. 

RagaAI provides a data monitoring service to complement the existing services to provide data and model related insights like drift and other key metrics. This can enhance the Vision AI solution by providing model observability.  


RagaAI

Ensuring AI quality is always a problem for data science teams around the world, whether it is dealing with experiments stuck in the lab or biased / non-robust AI in production.

RagaAI stands as a leading AI testing platform, specialising in computer vision applications. With over 300+ tests available, RagaAI automates the detection of issues, identifies root causes, and empowers users to resolve them effectively. Its multimodal capabilities cover a wide range of computer vision tasks, including object detection, semantic segmentation, instance segmentation, image classification, OCR, super-resolution, and more. 

RagaAI Testing Platform is designed to solve this exact problem by offering the following capabilities to data scientists - 

  1. Comprehensive Testing: 300+ tests to identify any issues with data, model and AI deployment settings. 

  2. Breakthrough RagaAI DNA Technology: Creating foundational model based embedding spaces to detect and fix issues related to labelling quality, drift detection, active learning, edge case detection and others. 

  3. Multi Modal Platform: Enabling testing across Computer Vision, LLMs / NLP and Structured Data Applications individually or a part of a complex pipeline. 

  4. Accessibility and Usability for Various Expertise Levels: The field of AI is diverse, with professionals ranging from seasoned experts to novices. However, many AI development platforms are either too complex for beginners or too simplistic for advanced users, creating a gap in usability and accessibility. 

The RagaAI product is designed to help data science teams focus on building the best AI products and not get bogged down with crucial but massive infrastructure development projects.


Leveraging the Integration

The integration of RagaAI, with the Analytics Module of NVIDIA Metropolis Microservices presents a powerful combination for real-time video data processing and analysis. The Analytics Module, nestled within the comprehensive Metropolis platform, is tailored to handle the intricacies of video data, providing robust capabilities for rapid analysis.

By leveraging RagaAI's advanced testing functionalities and Metropolis framework's real-time processing prowess, users can achieve enhanced accuracy and efficiency in analysing video content. RagaAI's testing platform offers comprehensive tools for assessing the performance and reliability of AI algorithms, ensuring rigorous evaluation before and after deployment. 

The integration of RagaAI into NVIDIA Metropolis Microservices enables a streamlined workflow for developers. After deploying their edge AI models using Metropolis, users can seamlessly initiate RagaAI tests to assess model and data-related issues. 

As an example, let’s say you're monitoring a live video stream from a surveillance camera. The camera, part of a multi-camera tracking system, is tracking people passing through its field of view.

However, as the weather changes and clouds roll in, the lighting conditions deteriorate. This can lead to challenges for the AI model deployed on the edge, impacting its ability to accurately detect and track individuals.

This scenario underscores the importance of integrating RagaAI with the analytics module of Metropolis. With RagaAI's advanced testing capabilities seamlessly integrated, users gain real-time visibility into data and model-related issues. 

As the user is observing the analytics on a Kibana Dashboard within the Metropolis platform, the lighting conditions worsen due to the cloudy weather, the drift graph generated by RagaAI indicates a significant increase in drift from a specific time. This suggests that the AI model may not have been adequately trained on data points with low lighting conditions or cloudy weather.

By leveraging RagaAI's extensive testing suite in conjunction with Metropolis, users can proactively address such issues. They can receive timely alerts about potential performance degradation and take corrective actions to maintain the effectiveness of their edge AI solutions. 

Moreover, they can also perform active learning easily (model retraining to fix cloudy weather conditions) with RagaAI’s active learning intelligence.

Ultimately, this integration empowers developers to continuously refine and optimise their models, ensuring consistent performance and reliability in diverse environmental conditions. This is clear from the retrained model’s performance in the same cloudy conditions as seen below.

Conclusion 

The integration of RagaAI with NVIDIA Metropolis Microservices represents a significant advancement in the field of Edge AI development. By combining the robust capabilities of both platforms, developers can ensure the quality, reliability, and accuracy of their edge AI applications. With access to a comprehensive suite of tests and seamless integration into the development workflow, developers are empowered to deliver cutting-edge edge AI solutions that meet the highest standards of performance and reliability.

In the fast-evolving landscape of Edge AI applications, ensuring the robustness and reliability of models is paramount. With the integration of RagaAI, a comprehensive AI testing platform, into NVIDIA Metropolis Microservices, developers now have a powerful toolset at their disposal to enhance the quality and performance of their edge AI solutions. 

Ensuring the reliability of AI systems through thorough testing is a significant challenge for developers. The dynamic nature of AI applications requires constant vigilance to detect and address potential issues. However, without comprehensive testing, developers risk deploying AI models that may not perform as expected, leading to inaccuracies and inefficiencies in real-world scenarios. Effective AI testing is essential to verify the accuracy, robustness, and general performance of AI models before deployment.

For example: Data drift, an often-overlooked challenge in AI, poses a significant threat to model efficacy. Before we delve into the integration's benefits, let's understand data drift and its adverse effects on AI performance.

Data drift, refers to the gradual divergence of data distributions over time, leading to degraded model performance. This problem, made worse by changing environmental conditions, presents a big challenge for AI reliability.

To grasp how the integration of RagaAI with NVIDIA Metropolis Microservices can improve edge AI solutions, let's first delve into their functionalities.


NVIDIA Metropolis Microservices 

NVIDIA Metropolis microservices constitute a suite of adaptable, cloud-native components designed for crafting vision AI applications and solutions. This introduces an array of APIs and microservices specifically optimised for the edge with both NVIDIA Jetson Orin platform and Enterprise GPUs, further expediting the creation and deployment of edge AI applications.

These microservices empower developers to modernise their AI application infrastructure, streamline workflows, and future-proof their applications. By seamlessly integrating cutting-edge generative AI capabilities, developers gain access to a host of functionalities including video storage and management, prebuilt AI perception pipelines, tracking algorithms, system monitoring, secure edge-to-cloud IoT connectivity, and more. 

RagaAI provides a data monitoring service to complement the existing services to provide data and model related insights like drift and other key metrics. This can enhance the Vision AI solution by providing model observability.  


RagaAI

Ensuring AI quality is always a problem for data science teams around the world, whether it is dealing with experiments stuck in the lab or biased / non-robust AI in production.

RagaAI stands as a leading AI testing platform, specialising in computer vision applications. With over 300+ tests available, RagaAI automates the detection of issues, identifies root causes, and empowers users to resolve them effectively. Its multimodal capabilities cover a wide range of computer vision tasks, including object detection, semantic segmentation, instance segmentation, image classification, OCR, super-resolution, and more. 

RagaAI Testing Platform is designed to solve this exact problem by offering the following capabilities to data scientists - 

  1. Comprehensive Testing: 300+ tests to identify any issues with data, model and AI deployment settings. 

  2. Breakthrough RagaAI DNA Technology: Creating foundational model based embedding spaces to detect and fix issues related to labelling quality, drift detection, active learning, edge case detection and others. 

  3. Multi Modal Platform: Enabling testing across Computer Vision, LLMs / NLP and Structured Data Applications individually or a part of a complex pipeline. 

  4. Accessibility and Usability for Various Expertise Levels: The field of AI is diverse, with professionals ranging from seasoned experts to novices. However, many AI development platforms are either too complex for beginners or too simplistic for advanced users, creating a gap in usability and accessibility. 

The RagaAI product is designed to help data science teams focus on building the best AI products and not get bogged down with crucial but massive infrastructure development projects.


Leveraging the Integration

The integration of RagaAI, with the Analytics Module of NVIDIA Metropolis Microservices presents a powerful combination for real-time video data processing and analysis. The Analytics Module, nestled within the comprehensive Metropolis platform, is tailored to handle the intricacies of video data, providing robust capabilities for rapid analysis.

By leveraging RagaAI's advanced testing functionalities and Metropolis framework's real-time processing prowess, users can achieve enhanced accuracy and efficiency in analysing video content. RagaAI's testing platform offers comprehensive tools for assessing the performance and reliability of AI algorithms, ensuring rigorous evaluation before and after deployment. 

The integration of RagaAI into NVIDIA Metropolis Microservices enables a streamlined workflow for developers. After deploying their edge AI models using Metropolis, users can seamlessly initiate RagaAI tests to assess model and data-related issues. 

As an example, let’s say you're monitoring a live video stream from a surveillance camera. The camera, part of a multi-camera tracking system, is tracking people passing through its field of view.

However, as the weather changes and clouds roll in, the lighting conditions deteriorate. This can lead to challenges for the AI model deployed on the edge, impacting its ability to accurately detect and track individuals.

This scenario underscores the importance of integrating RagaAI with the analytics module of Metropolis. With RagaAI's advanced testing capabilities seamlessly integrated, users gain real-time visibility into data and model-related issues. 

As the user is observing the analytics on a Kibana Dashboard within the Metropolis platform, the lighting conditions worsen due to the cloudy weather, the drift graph generated by RagaAI indicates a significant increase in drift from a specific time. This suggests that the AI model may not have been adequately trained on data points with low lighting conditions or cloudy weather.

By leveraging RagaAI's extensive testing suite in conjunction with Metropolis, users can proactively address such issues. They can receive timely alerts about potential performance degradation and take corrective actions to maintain the effectiveness of their edge AI solutions. 

Moreover, they can also perform active learning easily (model retraining to fix cloudy weather conditions) with RagaAI’s active learning intelligence.

Ultimately, this integration empowers developers to continuously refine and optimise their models, ensuring consistent performance and reliability in diverse environmental conditions. This is clear from the retrained model’s performance in the same cloudy conditions as seen below.

Conclusion 

The integration of RagaAI with NVIDIA Metropolis Microservices represents a significant advancement in the field of Edge AI development. By combining the robust capabilities of both platforms, developers can ensure the quality, reliability, and accuracy of their edge AI applications. With access to a comprehensive suite of tests and seamless integration into the development workflow, developers are empowered to deliver cutting-edge edge AI solutions that meet the highest standards of performance and reliability.

In the fast-evolving landscape of Edge AI applications, ensuring the robustness and reliability of models is paramount. With the integration of RagaAI, a comprehensive AI testing platform, into NVIDIA Metropolis Microservices, developers now have a powerful toolset at their disposal to enhance the quality and performance of their edge AI solutions. 

Ensuring the reliability of AI systems through thorough testing is a significant challenge for developers. The dynamic nature of AI applications requires constant vigilance to detect and address potential issues. However, without comprehensive testing, developers risk deploying AI models that may not perform as expected, leading to inaccuracies and inefficiencies in real-world scenarios. Effective AI testing is essential to verify the accuracy, robustness, and general performance of AI models before deployment.

For example: Data drift, an often-overlooked challenge in AI, poses a significant threat to model efficacy. Before we delve into the integration's benefits, let's understand data drift and its adverse effects on AI performance.

Data drift, refers to the gradual divergence of data distributions over time, leading to degraded model performance. This problem, made worse by changing environmental conditions, presents a big challenge for AI reliability.

To grasp how the integration of RagaAI with NVIDIA Metropolis Microservices can improve edge AI solutions, let's first delve into their functionalities.


NVIDIA Metropolis Microservices 

NVIDIA Metropolis microservices constitute a suite of adaptable, cloud-native components designed for crafting vision AI applications and solutions. This introduces an array of APIs and microservices specifically optimised for the edge with both NVIDIA Jetson Orin platform and Enterprise GPUs, further expediting the creation and deployment of edge AI applications.

These microservices empower developers to modernise their AI application infrastructure, streamline workflows, and future-proof their applications. By seamlessly integrating cutting-edge generative AI capabilities, developers gain access to a host of functionalities including video storage and management, prebuilt AI perception pipelines, tracking algorithms, system monitoring, secure edge-to-cloud IoT connectivity, and more. 

RagaAI provides a data monitoring service to complement the existing services to provide data and model related insights like drift and other key metrics. This can enhance the Vision AI solution by providing model observability.  


RagaAI

Ensuring AI quality is always a problem for data science teams around the world, whether it is dealing with experiments stuck in the lab or biased / non-robust AI in production.

RagaAI stands as a leading AI testing platform, specialising in computer vision applications. With over 300+ tests available, RagaAI automates the detection of issues, identifies root causes, and empowers users to resolve them effectively. Its multimodal capabilities cover a wide range of computer vision tasks, including object detection, semantic segmentation, instance segmentation, image classification, OCR, super-resolution, and more. 

RagaAI Testing Platform is designed to solve this exact problem by offering the following capabilities to data scientists - 

  1. Comprehensive Testing: 300+ tests to identify any issues with data, model and AI deployment settings. 

  2. Breakthrough RagaAI DNA Technology: Creating foundational model based embedding spaces to detect and fix issues related to labelling quality, drift detection, active learning, edge case detection and others. 

  3. Multi Modal Platform: Enabling testing across Computer Vision, LLMs / NLP and Structured Data Applications individually or a part of a complex pipeline. 

  4. Accessibility and Usability for Various Expertise Levels: The field of AI is diverse, with professionals ranging from seasoned experts to novices. However, many AI development platforms are either too complex for beginners or too simplistic for advanced users, creating a gap in usability and accessibility. 

The RagaAI product is designed to help data science teams focus on building the best AI products and not get bogged down with crucial but massive infrastructure development projects.


Leveraging the Integration

The integration of RagaAI, with the Analytics Module of NVIDIA Metropolis Microservices presents a powerful combination for real-time video data processing and analysis. The Analytics Module, nestled within the comprehensive Metropolis platform, is tailored to handle the intricacies of video data, providing robust capabilities for rapid analysis.

By leveraging RagaAI's advanced testing functionalities and Metropolis framework's real-time processing prowess, users can achieve enhanced accuracy and efficiency in analysing video content. RagaAI's testing platform offers comprehensive tools for assessing the performance and reliability of AI algorithms, ensuring rigorous evaluation before and after deployment. 

The integration of RagaAI into NVIDIA Metropolis Microservices enables a streamlined workflow for developers. After deploying their edge AI models using Metropolis, users can seamlessly initiate RagaAI tests to assess model and data-related issues. 

As an example, let’s say you're monitoring a live video stream from a surveillance camera. The camera, part of a multi-camera tracking system, is tracking people passing through its field of view.

However, as the weather changes and clouds roll in, the lighting conditions deteriorate. This can lead to challenges for the AI model deployed on the edge, impacting its ability to accurately detect and track individuals.

This scenario underscores the importance of integrating RagaAI with the analytics module of Metropolis. With RagaAI's advanced testing capabilities seamlessly integrated, users gain real-time visibility into data and model-related issues. 

As the user is observing the analytics on a Kibana Dashboard within the Metropolis platform, the lighting conditions worsen due to the cloudy weather, the drift graph generated by RagaAI indicates a significant increase in drift from a specific time. This suggests that the AI model may not have been adequately trained on data points with low lighting conditions or cloudy weather.

By leveraging RagaAI's extensive testing suite in conjunction with Metropolis, users can proactively address such issues. They can receive timely alerts about potential performance degradation and take corrective actions to maintain the effectiveness of their edge AI solutions. 

Moreover, they can also perform active learning easily (model retraining to fix cloudy weather conditions) with RagaAI’s active learning intelligence.

Ultimately, this integration empowers developers to continuously refine and optimise their models, ensuring consistent performance and reliability in diverse environmental conditions. This is clear from the retrained model’s performance in the same cloudy conditions as seen below.

Conclusion 

The integration of RagaAI with NVIDIA Metropolis Microservices represents a significant advancement in the field of Edge AI development. By combining the robust capabilities of both platforms, developers can ensure the quality, reliability, and accuracy of their edge AI applications. With access to a comprehensive suite of tests and seamless integration into the development workflow, developers are empowered to deliver cutting-edge edge AI solutions that meet the highest standards of performance and reliability.

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Building Your First LLM Agent Framework Application

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Understanding Data Fragmentation and Strategies to Overcome It

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Advantages Of Using LLMs For Rapid Application Development

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Understanding React Agent in LangChain Engineering

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Using RagaAI Catalyst to Evaluate LLM Applications

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Step-by-Step Guide on Training Large Language Models

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Understanding LLM Agent Architecture

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Understanding the Need and Possibilities of AI Guardrails Today

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How to Prepare Quality Dataset for LLM Training

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Understanding Multi-Agent LLM Framework and Its Performance Scaling

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Understanding and Tackling Data Drift: Causes, Impact, and Automation Strategies

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Introducing RagaAI Catalyst: Best in class automated LLM evaluation with 93% Human Alignment

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Key Pillars and Techniques for LLM Observability and Monitoring

Rehan Asif

Jul 24, 2024

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Introduction to What is LLM Agents and How They Work?

Rehan Asif

Jul 24, 2024

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Analysis of the Large Language Model Landscape Evolution

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Jul 24, 2024

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Marketing Success With Retrieval Augmented Generation (RAG) Platforms

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Developing AI Agent Strategies Using GPT

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Identifying Triggers for Retraining AI Models to Maintain Performance

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Jul 16, 2024

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Agentic Design Patterns In LLM-Based Applications

Rehan Asif

Jul 16, 2024

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Generative AI And Document Question Answering With LLMs

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How to Fine-Tune ChatGPT for Your Use Case - Step by Step Guide

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Security and LLM Firewall Controls

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Understanding the Use of Guardrail Metrics in Ensuring LLM Safety

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Exploring the Future of LLM and Generative AI Infrastructure

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Comprehensive Guide to RLHF and Fine Tuning LLMs from Scratch

Rehan Asif

Jul 13, 2024

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Using Synthetic Data To Enrich RAG Applications

Jigar Gupta

Jul 13, 2024

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Comparing Different Large Language Model (LLM) Frameworks

Rehan Asif

Jul 12, 2024

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Integrating AI Models with Continuous Integration Systems

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Jul 12, 2024

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Understanding Retrieval Augmented Generation for Large Language Models: A Survey

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Jul 12, 2024

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Leveraging AI For Enhanced Retail Customer Experiences

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Jul 1, 2024

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Enhancing Enterprise Search Using RAG and LLMs

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Jul 1, 2024

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Importance of Accuracy and Reliability in Tabular Data Models

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Information Retrieval And LLMs: RAG Explained

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Introduction to LLM Powered Autonomous Agents

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Guide on Unified Multi-Dimensional LLM Evaluation and Benchmark Metrics

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Innovations In AI For Healthcare

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Implementing AI-Driven Inventory Management For The Retail Industry

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Practical Retrieval Augmented Generation: Use Cases And Impact

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20 LLM Project Ideas For Beginners Using Large Language Models

Rehan Asif

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Understanding LLM Parameters: Tuning Top-P, Temperature And Tokens

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Jun 23, 2024

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Understanding Large Action Models In AI

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Jun 23, 2024

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Building And Implementing Custom LLM Guardrails

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Understanding LLM Alignment: A Simple Guide

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Practical Strategies For Self-Hosting Large Language Models

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Practical Guide For Deploying LLMs In Production

Rehan Asif

Jun 12, 2024

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The Impact Of Generative Models On Content Creation

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Implementing Regression Tests In AI Development

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Jun 12, 2024

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In-Depth Case Studies in AI Model Testing: Exploring Real-World Applications and Insights

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Jun 11, 2024

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Techniques and Importance of Stress Testing AI Systems

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Jun 11, 2024

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Navigating Global AI Regulations and Standards

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Jun 10, 2024

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The Cost of Errors In AI Application Development

Rehan Asif

Jun 10, 2024

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Best Practices In Data Governance For AI

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Jun 10, 2024

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Success Stories And Case Studies Of AI Adoption Across Industries

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Exploring The Frontiers Of Deep Learning Applications

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May 1, 2024

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Integration Of RAG Platforms With Existing Enterprise Systems

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Apr 30, 2024

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Multimodal LLMS Using Image And Text

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Apr 30, 2024

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Understanding ML Model Monitoring In Production

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Apr 30, 2024

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Strategic Approach To Testing AI-Powered Applications And Systems

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Navigating GDPR Compliance for AI Applications

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The Impact of AI Governance on Innovation and Development Speed

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Best Practices For Testing Computer Vision Models

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Building Low-Code LLM Apps with Visual Programming

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Understanding AI regulations In Finance

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Compliance Automation: Getting Started with Regulatory Management

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Practical Guide to Fine-Tuning OpenAI GPT Models Using Python

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Comparing Different Large Language Models (LLM)

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Evaluating Large Language Models: Methods And Metrics

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Apr 22, 2024

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Challenges and Strategies for Implementing Enterprise LLM

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Enhancing Computer Vision with Synthetic Data: Advantages and Generation Techniques

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Building Trust In Artificial Intelligence Systems

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A Brief Guide To LLM Parameters: Tuning and Optimization

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Unlocking The Potential Of Computer Vision Testing: Key Techniques And Tools

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Understanding AI Regulatory Compliance And Its Importance

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Understanding The Basics Of AI Governance

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Understanding Prompt Engineering: A Guide

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Examples And Strategies To Mitigate AI Bias In Real-Life

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Understanding The Basics Of LLM Fine-tuning With Custom Data

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Overview Of Key Concepts In AI Safety And Security
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Understanding Hallucinations In LLMs

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Demystifying FDA's Approach to AI/ML in Healthcare: Your Ultimate Guide

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Navigating AI Governance in Aerospace Industry

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Apr 3, 2024

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The White House Executive Order on Safe and Trustworthy AI

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Mar 29, 2024

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The EU AI Act - All you need to know

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RagaAI releases the most comprehensive open-source LLM Evaluation and Guardrails package

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Mar 7, 2024

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RagaAI LLM Hub
RagaAI LLM Hub
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RagaAI LLM Hub
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Identifying edge cases within CelebA Dataset using RagaAI testing Platform

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Feb 15, 2024

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How to Detect and Fix AI Issues with RagaAI

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Feb 16, 2024

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Detection of Labelling Issue in CIFAR-10 Dataset using RagaAI Platform

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RagaAI emerges from Stealth with the most Comprehensive Testing Platform for AI

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Author

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Jan 13, 2024

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Copyright © RagaAI | 2024

691 S Milpitas Blvd, Suite 217, Milpitas, CA 95035, United States

Get Started With RagaAI®

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Resources

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Copyright © RagaAI | 2024

691 S Milpitas Blvd, Suite 217, Milpitas, CA 95035, United States