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Advanced Machine Learning Operations

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.


Note: This course is the second in the series of Advanced Machine Learning with Databricks. 

Skill Level
Professional
Duration
2h
Prerequisites
  • 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. 

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

See all our registration options

Registration options

Databricks has a delivery method for wherever you are on your learning journey

Runtime

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Register now

Instructors

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

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Learning

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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Scale

Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Data Engineer

Data Ingestion with Lakeflow Connect

This course provides a comprehensive introduction to Lakeflow Connect as a scalable and simplified solution for ingesting data into Databricks from a variety of data sources. You will begin by exploring the different types of connectors within Lakeflow Connect (Standard and Managed), learn about various ingestion techniques, including batch, incremental batch, and streaming, and then review the key benefits of Delta tables and the Medallion architecture.

From there, you will gain practical skills to efficiently ingest data from cloud object storage using Lakeflow Connect Standard Connectors with methods such as CREATE TABLE AS (CTAS), COPY INTO, and Auto Loader, along with the benefits and considerations of each approach. You will then learn how to append metadata columns to your bronze level tables during ingestion into the Databricks data intelligence platform. This is followed by working with the rescued data column, which handles records that don’t match the schema of your bronze table, including strategies for managing this rescued data.

The course also introduces techniques for ingesting and flattening semi-structured JSON data, as well as enterprise-grade data ingestion using Lakeflow Connect Managed Connectors.

Finally, learners will explore alternative ingestion strategies, including MERGE INTO operations and leveraging the Databricks Marketplace, equipping you with foundational knowledge to support modern data engineering ingestion.

Free
2h
Associate

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.