Machine Learning Practitioner

DATB-AMLO : Advanced Machine Learning Operations

Course Duration

2 Hours

Delivery

ILT/VILT

Overview

Overview Course Curriculum Training Schedule Exam & Certification FAQs

In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.

What You'll Learn

Overview of Machine Learning Operations on Databricks
Continuous Workflows for Machine Learning Operations
Testing Strategies with Databricks
Model Quality and Lakehouse Monitoring
Streamlining Multiple Environment Deployments - DABs

Who Should Attend

This course is ideal for professionals who:

Are working as machine learning engineers, MLOps practitioners or data scientists tasked with operationalizing ML workflows in production.
Are responsible for deploying, monitoring and governing models at scale using the Databricks Lakehouse and supporting infrastructure (CI/CD, asset bundling, model serving).
Want to build end-to-end ML operations capabilities: model testing, drift detection, rollout strategies, A/B testing and metrics tracking.
Have intermediate-level experience in Python, ML model development, version control (e.g., Git) and a working understanding of traditional ML concepts and platform usage.
Are part of teams moving from experimentation to production-grade ML systems and seek to standardise reuse, governance, monitoring and scaling of ML assets.

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

The user should have intermediate-level knowledge of traditional machine learning concepts, development, and the use of Python and Git for ML projects.
It is recommended that the user has intermediate-level experience with Python.

Learning Journey

Coming Soon...

1.Overview of Machine Learning Operations on Databricks

Review of MLOps
Streamlining Development to Deployment

2.Continuous Workflows for Machine Learning Operations

Streamlining MLOps
Streamlining MLOps with Databricks

3.Testing Strategies with Databricks

Automate Comprehensive Testing
Model Rollout Strategies with Databricks

4.Model Quality and Lakehouse Monitoring

Introduction to Monitoring
Lakehouse Monitoring

5.Streamlining Multiple Environment Deployments - DABs

Build ML assets as CodeCourse Summary and Next Steps

Frequently Asked Questions (FAQs)

None

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