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Overview
In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.
Overview of Machine Learning Operations on Databricks Continuous Workflows for Machine Learning Operations Testing Strategies with Databricks Model Quality and Lakehouse Monitoring Streamlining Multiple Environment Deployments - DABs
This course is for professionals at the “Professional” skill level, i.e., those with intermediate-level knowledge of machine learning concepts, development, and use of Python and Git in ML projects.
The content was developed for participants with these skills/knowledge/abilities:
Coming Soon...
Overview of Machine Learning Operations on Databricks Review of MLOps Streamlining Development to Deployment Continuous Workflows for Machine Learning Operations Streamlining MLOps Streamlining MLOps with Databricks Testing Strategies with Databricks Automate Comprehensive Testing Model Rollout Strategies with Databricks Model Quality and Lakehouse Monitoring Introduction to Monitoring Lakehouse Monitoring Streamlining Multiple Environment Deployments - DABs Build ML assets as CodeCourse Summary and Next Steps
Overview of Machine Learning Operations on Databricks
Continuous Workflows for Machine Learning Operations
Testing Strategies with Databricks
Model Quality and Lakehouse Monitoring
Streamlining Multiple Environment Deployments - DABs
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