ML Observability Fundamentals
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ML Observability Fundamentals 
Arize University Courses
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Course Curriculum
Overview of the ML Observability course, what to expect and tools you will need to complete the course. Find out how this course will help you gain confidence in taking models from research to production.
Course video (7 minutes)
Quiz (10 minutes)
Lab (7 minutes)
ML performance tracing is the methodology for pinpointing the source of a model performance problem and mapping back to the underlying data issue causing that problem. Learn how to performance trace a model to find the root cause of the performance degradation by comparing different model versions at the slice level.
Course video (8 minutes)
Quiz (10 minutes)
Lab (6 minutes)
Learn how to identify the various types of drift in productionalized ML models. From data drift to concept drift, these different forms of data movement often go undetected as they occur gradually over time, so it’s crucial to drift trace a model to find and resolve the root cause of the model’s recent drift.
Course video (10 minutes)
Quiz (10 minutes)
Lab (6 minutes)
Learn how to get your own ML models into the Arize platform in order to begin monitoring for performance, drift, and common data quality issues in production.
Course video (7 minutes)
Quiz (10 minutes)
Lab (6 minutes)
Learn about how teams in industry are leveraging Explainability methods to compute feature importance values and how these values can be leveraged in production RCA.
Course video (8 minutes)
Quiz (10 minutes)
Lab (5 minutes)
Learn about the types of bias that can seep into your ML models in production, as well as how to uncover the features and cohorts likely contributing to algorithmic bias.
Course video (12 minutes)
Quiz (10 minutes)
Lab (5 minutes)
Applying ML Observability to unstructured use cases is especially important due to the lack of visibility into how embeddings perform regularly. Learn how to analyze embedding drift in unstructured data and troubleshoot with an interactive 2D or 3D UMAP.
Course video (10 minutes)
Quiz (10 minutes)
Lab (6 minutes)
Skills you will learn
Technical requirements
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Completion requirements
Upon completing all units of the ML Observability Curriculum, you will receive a Certificate of Completion to highlight your new skillset. Completion of all the unit labs, as well as the passing of each unit quiz are required for completion.