AI Observability

What Is AI Observability?

AI Observability refers to the ability to monitor and understand the functionality of generative and predictive AI machine learning models throughout their lifecycle within an ecosystem. It’s an essential aspect of MLOps (Machine Learning Operations) and, especially, Large Language Model Operations (LLMOps), aligning with DevOps and IT operations to ensure the seamless integration and performance of generative and predictive AI models in real-time workflows. Observability enables the tracking of metrics, performance issues, and outputs generated by AI models, providing a comprehensive view through an organization’s observability platform.

Key components of AI Observability include:

1. Metrics and Performance Monitoring: Monitoring model performance metrics and other key indicators to ensure that AI models are operating as expected. This includes real-time monitoring and root cause analysis to troubleshoot and address performance issues.
2. Model Monitoring and Management: Utilizing observability tools to track the performance and functionality of machine learning models, algorithms, and pipelines, ensuring their optimal operation throughout the lifecycle.
3. Visualization and Dashboards: Providing dashboards for visualization of metrics, datasets, and actionable insights, aiding in the analysis and interpretation of AI model performance.
4. Automation and Lifecycle Management (ALM): Automating various stages in the AI model lifecycle, from data preparation to model deployment, and ensuring smooth workflows.
5. Data Quality and Explainability: Ensuring the quality of datasets used, and providing explainability for AI models’ decisions, enhancing trust and understanding among stakeholders.
6. Generative AI and Advanced Algorithms: Employing advanced algorithms and generative AI techniques to enhance the capabilities and performance of AI models.
7. User and Customer Experience: Ensuring that the AI observability solution enhances the user and customer experience by providing a robust platform for data scientists and other stakeholders to interact with AI and ML systems.
8. AIOps and Integration: Integrating AI observability with AIOps (Artificial Intelligence for IT Operations) and other observability solutions, enabling a unified approach to managing application performance and infrastructure.
9. APIs and Telemetry: Utilizing APIs for seamless integration and collecting telemetry data to provide deeper insights into the operation and performance of AI models.
10. Community and Ecosystem: Building a supportive ecosystem around AI observability that includes data science professionals, end-users, and a variety of observability tools and platforms.

Why Is AI Observability Important? 

AI Observability is crucial for organizations looking to leverage AI and machine learning technologies, ensuring that they can efficiently manage, monitor, and gain insights from their generative and predictive AI models, thereby driving better decision-making and enhanced customer experiences. It’s becoming increasingly important with the introduction of generative AI into the enterprise ecosystem, because of the risks associated with the possibility of returning incorrect responses, “hallucinations”. 

AI Observability + DataRobot

AI observability capabilities within the DataRobot AI platform help ensure that organizations know when something goes wrong and understand why it went wrong and be able to intervene to continuously optimize the performance of AI models. By tracking service, drift, prediction data, training data, and custom metrics, enterprises can keep their models and predictions relevant in a fast-changing world. 

DataRobot and its MLOps capabilities provide world-class scalability for model deployment. Models across the organization, regardless of where they were built, can be supervised and managed under one single platform. In addition to DataRobot models, open source models deployed outside of DataRobot MLOps can also be managed and monitored by the DataRobot platform.

It is not enough to just monitor performance and log errors. To get a complete understanding of the internal state of your AI/ML system, you also need visibility into prediction requests and the ability to slice and dice prediction data over time. Not knowing the context of a performance issue delays the resolution, as the user will have to diagnose via trial and error, which is problematic for business critical models.

This is a key difference between model monitoring and model observability: model monitoring exposes what the problem is; model observability helps understand why the problem occurred. Both must go hand in hand.

With model observability capabilities, DataRobot MLOps users gain full visibility and the ability to track information regarding service, drift, prediction and training data, as well as custom metrics that are relevant to your business. DataRobot customers now have enhanced visibility into hundreds of models across the organization. 

To quantify how well your models are doing, DataRobot provides you with a comprehensive set of data science metrics — from the standards (Log Loss, RMSE) to the more specific (SMAPE, Tweedie Deviance). But many of the things you need to measure for your business are hyper specific for your unique problems and opportunities — specific business KPIs or data science secrets. With DataRobot Custom Metrics, you can monitor details specific to your business.

After DataRobot has delivered an optimal model, Production Lifecycle Management capabilities of the platform help ensure that the currently deployed model will always be the best one even as the world changes around it. MLOps delivers automated strategies to keep production models at peak performance, regardless of external conditions.

For example, DataRobot Data Drift and Accuracy Monitoring detects when reality differs from the situation when the training dataset was created and the model trained. Meanwhile, DataRobot can continuously train challenger models based on more up-to-date data. Once a challenger is detected to outperform the current champion model, the DataRobot AI platform notifies you about changing to this new candidate model.DataRobot also allows organizations to solve the generative AI confidence problem by pairing each generative model with a predictive AI guard model that evaluates the quality of the output. This framework has broad applicability across use cases where accuracy and truthfulness are paramount.