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Overview
This course will guide participants through a comprehensive exploration of machine learning model operations, focusing on MLOps and model lifecycle management. The initial segment covers essential MLOps components and best practices, providing participants with a strong foundation for effectively operationalizing machine learning models. In the latter part of the course, we will delve into the basics of the model lifecycle, demonstrating how to navigate it seamlessly using the Model Registry in conjunction with the Unity Catalog for efficient model management. By the course's conclusion, participants will have gained practical insights and a well-rounded understanding of MLOps principles, equipped with the skills needed to navigate the intricate landscape of machine learning model operations.
Architecting MLOps Solutions Implementation and Monitoring MLOps Solution
At a minimum, you should be familiar with the following before attempting to take this content:
Coming Soon...
Outline Modern MLOps Defining MLOps MLOps on Databricks Working with Asset Bundles Architecting MLOps Solutions Introduction Opinionated MLOps Principles Recommended MLOps Architectures Model Testing Job with the Databricks CLI Implementation and Monitoring MLOps Solution Introduction Implementation of MLOps Stacks Type of Model Monitoring Monitoring in Machine Learning Lakehouse Monitoring Dashboard
Outline
Architecting MLOps Solutions
Implementation and Monitoring MLOps Solution
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