Building ML Platforms
Mike discusses the challenges of building an ML platform that balances the needs of data scientists for flexibility and engineers for stability. The team focuses on providing tools for data scientists to prototype in Jupyter notebooks and seamlessly transition models to production. Balancing priorities and stakeholders throughout the ML lifecycle is key.In this clip
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Practical AI
UBER and Intel’s Machine Learning platforms
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