Overview
In this course, you will learn the best practices for managing machine learning experiments and models with MLflow. There are two main components in this course: (i) using MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions (ii) examining various production issues, different deployment paradigms, and post-production concerns. By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models.
Prerequisites
- Intermediate experience with Python and pandas
- Working knowledge of machine learning and data science (scikit-learn, TensorFlow, etc.)
- Familiarity with Apache Spark
Outline
Module 1: ML in production overview
Module 2: Data management with Delta and Databricks Feature Store
Module 3: Experiment tracking and versioning with MLflow Tracking
Module 4: Model management with MLflow Models and Model Registry
Module 5: Automated testing with webhooks
Module 6: Deployment paradigms
Module 7: Monitoring and CI/CD
QA reserves the right to improve the specification and format of its courses for the benefit of its customers without notice to the customer.
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