The Definitive Machine Learning Observability Checklist

The machine learning infrastructure ecosystem is confusing, crowded and complex. With so many companies making competing claims and so much at stake when model performance regresses in production, it can be easy to feel overwhelmed. However, the need for better ML observability tools to monitor, troubleshoot, and explain model decisions is clear. This newly-updated checklist covers the essential elements to consider when evaluating an ML observability platform in 2023. Whether you’re readying an RFP or assessing individual platforms, this buyer’s guide can help with product and technical requirements to consider across:  

  • Unstructured Data Monitoring
  • Model Lineage, Validation & Comparison
  • Data Quality & Drift Monitoring & Troubleshooting
  • Performance Monitoring & Troubleshooting
  • Explainability
  • Business Impact Analysis
  • Integration Functionality
  • UI/UX Experience & Scalability To Meet Current Analytics Complexity

Read the Checklist

About the author

Aparna Dhinakaran
Co-founder & Chief Product Officer

Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a pioneer and early leader in machine learning (ML) observability. A frequent speaker at top conferences and thought leader in the space, Dhinakaran was recently named to the Forbes 30 Under 30. Before Arize, Dhinakaran was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michealangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

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