Machine Learning Operations
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.
Note:
1. This is the fourth course in the 'Machine Learning with Databricks’ series.
2. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.
At a minimum, you should be familiar with the following before attempting to take this content:
• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations, import repos from git)
• Intermediate programming experience with Python, including data manipulation libraries (pandas, numpy) and working with APIs (REST endpoints, JSON payloads)
• Basic knowledge of MLflow for experiment tracking, model logging, model registry operations, and model lifecycle management
• Understanding of machine learning fundamentals, including model training, evaluation, deployment workflows, and performance monitoring concepts
• Familiarity with MLOps concepts, including data quality assessment, feature engineering, model testing, and continuous monitoring practices
• Basic experience with command-line interfaces and authentication setup for cloud platforms and development tools
• Understanding of Lakeflow Jobs and workflow orchestration concepts (task dependencies, conditional logic, scheduling, notifications)
• Basic knowledge of model monitoring and drift detection principles including performance metrics and anomaly detection
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