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A new data-centric approach to building robust MLOps practices
Discover the fastest path to get machine learning models to production. The Big Book of MLOps will show you how data engineers, data scientists, and machine learning engineers can build and collaborate on a common platform, using powerful and open frameworks such as Delta Lake for data pipelines, MLflow for model management and Databricks Workflows for automation.
Report Snap Shot
- The essential components of an MLOps reference architecture
- The key stakeholders to involve as you build and deploy machine learning applications
- How to leverage the same platform for data and models and get to production faster
- How to monitor data and models through the complete ML lifecycle with end-to-end lineage
- 'Best practices to guide your MLOps planning and decision-making
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