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:

1. This course is the second in the series of Advanced Machine Learning.

2. Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Skill Level

Professional

Duration

Prerequisites

In this course, the content was developed for participants with these skills/knowledge/abilities:

• Access to a Databricks workspace with administrator permissions and familiarity with basic Databricks operations (create clusters, run notebooks, basic notebook operations)

• Intermediate experience with Git version control, including repository management and Personal Access Token (PAT) configuration for GitHub integration

• Basic knowledge of CI/CD workflows, pipeline configurations, and DevOps concepts for automated deployment processes

• Intermediate programming experience with Python, MLflow for model tracking and management, and Unity Catalog for data governance

• Familiarity with machine learning model development lifecycle, including feature engineering, model training, validation, and deployment concepts

• Experience with command line interfaces, particularly Databricks CLI installation, configuration, and authentication using personal access tokens

• Understanding of model deployment strategies, including A/B testing, traffic distribution, and real-time inference concepts

• Basic knowledge of Databricks Workflows for job creation, task dependencies, and workflow orchestration

Self-Paced

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

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

Self-Paced

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

Instructor-Led

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

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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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

Model Development at Scale

In this course, you will develop an in-depth understanding of how to design, implement, and govern scalable machine learning systems that operate effectively at enterprise scale. The curriculum is organized into three experiential modules: developing distributed ML workflows with frameworks such as Apache SparkML and Ray, transitioning local ML development to distributed compute using tools like Pandas on Spark, and operationalizing and governing production models with Databricks’ MLOps ecosystem.

Through hands-on projects, you will construct end-to-end distributed ML pipelines using the SparkML workflow, applying Transformers, Estimators, and the fit/transform paradigm for both classification and regression tasks. You will version, compare, and manage experiments using MLflow 3.0 to ensure reproducibility and governance, capturing lineage between data, features, and model artifacts. Additionally, you will apply scalable Hyperparameter Optimization frameworks to improve model performance at scale.

The course concludes by demonstrating complete lifecycle management, from experimentation to production deployment, using Unity Catalog and Model Serving. You will learn to operationalize trained models, monitor their performance, and implement strong governance over models, features, and Delta assets within the Databricks environment.

Free

Professional

Data Governance Fundamentals - Korean

이 강의에서는 데이터 거버넌스와 Unity Catalog의 기본을 탐구하게 됩니다. 데이터 카탈로그의 진화와 목적, Unity Catalog의 아키텍처와 구성 요소, 그리고 거버넌스에서 데이터 품질, 보안, 규정 준수, 리니지, 감사의 중요성에 대해 배우게 됩니다.

Languages Available: English | 日本語 | 한국어

Get Started with Data Governance on Databricks

In this course, you will explore how Unity Catalog enables secure, centralized data governance and fine-grained access control on Databricks. You will learn about table and volume types, catalog and schema configuration, group-based access management, and strategies for migrating existing access controls into Unity Catalog. The course also explains how to design and apply fine-grained controls such as row-level security, column masking, and attribute-based access control, how to combine these mechanisms across data and AI assets, and how to align them with broader governance requirements for compliant, scalable access management.

Languages Available: English | 日本語 | 한국어

Free

Onboarding