Machine Learning in Production
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Description
Learn best practices for managing machine learning experiments and models with MLflow. This course will teach you how to:
- Use MLflow to track the machine learning lifecycle, package models for deployment and manage model versions
- Handle various production issues, 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.
This course will prepare you to take the Databricks Certified Machine Learning Professional exam.
Duration
1 full day or 2 half days
Objectives
- Track, version and manage machine learning experiments
- Leverage Databricks Feature Store for reproducible data management
- Implement strategies for deploying models for batch, streaming and real time
- Build monitoring solutions, including drift detection
Prerequisites
- Intermediate experience with Python and pandas
- Working knowledge of machine learning and data science (including technologies such as scikit-learn and TensorFlow)
- Familiarity with Apache Spark
Outline
Day 1
- ML in Production Vverview
- Data Management with Delta and Databricks Feature Store
- Experiment Tracking and Versioning with MLflow Tracking
- Model Management with MLflow Models and Model Registry
- Automated Testing with webhooks
- Deployment Paradigms
- Monitoring and CI/CD