ML Observability Checklist for Startups

Startups are crucibles for innovation, but getting from zero to one is difficult — particularly for machine learning (ML) teams. In a company’s earliest stages, ML teams must not only prove the value of ML internally but also train, deploy, and maintain a company’s first models. That means doing more with less. It also means laying a good technical foundation for future success without significantly adding to overhead. Based on Arize’s experience tracking billions of model predictions daily for top startups, this guide on what to look for in an ML observability platform covers:

  • Model Lineage, Validation & Comparison
  • Unstructured Data Monitoring
  • 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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