Machine Learning in Production
Description
In this course, you will learn MLOps best practices for putting machine learning models into production. The first half of the course uses a feature store to register training data and uses MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions. The second half of the course examines production issues including deployment paradigms, monitoring, and CI/CD. 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 (or completion of Introduction to Python for Data Science & Data Engineering)
Familiarity with Apache Spark (or completion of Apache Spark Programming)
Working knowledge of machine learning and data science (or completion of Scalable Machine Learning with Apache Spark)
Outline
Day 1
ML in production overview
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
Upcoming Public Classes
Public Class Registration
If your company has purchased success credits or has a learning subscription, please fill out the public training requests form. Otherwise, you can register below.
Private Class Delivery
If your organization would like to request a private delivery of the course, please fill out the request form below.
Questions?
If you have any questions, please refer to our Frequently Asked Questions page.