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Machine Learning in Production

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...

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$1,000 USD GSA  $906.80
Course Code MLPROD
Duration 1 day
Available Formats Classroom

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 and (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.

  • This course will prepare you to take the Databricks Certified Machine Learning Professional exam.

Skills Gained

  • 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 (scikit-learn, TensorFlow, etc.)
  • Familiarity with Apache Spark

Course Details

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