Machine Learning Operations

This course will guide participants through a comprehensive exploration of machine learning model operations, focusing on MLOps and model lifecycle management. The initial segment covers essential MLOps components and best practices, providing participants with a strong foundation for effectively operationalizing machine learning models. In the latter part of the course, we will delve into the basics of the model lifecycle, demonstrating how to navigate it seamlessly using the Model Registry in conjunction with the Unity Catalog for efficient model management. By the course's conclusion, participants will have gained practical insights and a well-rounded understanding of MLOps principles, equipped with the skills needed to navigate the intricate landscape of machine learning model operations.

Note:

1. This is the fourth course in the 'Machine Learning with Databricks’ series.

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

Associate

Duration

Prerequisites

At a minimum, you should be familiar with the following before attempting to take this content:

• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations, import repos from git)

• Intermediate programming experience with Python, including data manipulation libraries (pandas, numpy) and working with APIs (REST endpoints, JSON payloads)

• Basic knowledge of MLflow for experiment tracking, model logging, model registry operations, and model lifecycle management

• Understanding of machine learning fundamentals, including model training, evaluation, deployment workflows, and performance monitoring concepts

• Familiarity with MLOps concepts, including data quality assessment, feature engineering, model testing, and continuous monitoring practices

• Basic experience with command-line interfaces and authentication setup for cloud platforms and development tools

• Understanding of Lakeflow Jobs and workflow orchestration concepts (task dependencies, conditional logic, scheduling, notifications)

• Basic knowledge of model monitoring and drift detection principles including performance metrics and anomaly detection

Self-Paced

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Self-Paced

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

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

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Upcoming Public Classes

Data Warehousing Practitioner

Data Interoperability with Unity Catalog

This course explores how to achieve seamless data interoperability between systems using Databricks Unity Catalog as a unified governance layer, using both Delta and Iceberg open table formats. Participants will learn to implement, manage and optimize a data environment where tables can be accessed by both Delta and Iceberg clients, enabling cross platform analytics and eliminating data silos.

Advanced Techniques with Spark Declarative Pipelines

This course explores Databricks' Lakeflow Spark Declarative Pipelines (SDP) for building production-grade streaming pipelines. You will learn advanced design patterns, robust data quality enforcement, and cross-platform integration essential for real-world lakehouse engineering.

Throughout the course, you will dive into modern data ingestion and processing techniques, mastering tools like Liquid Clustering for layout optimization and the Multiplex Streaming pattern for mixed-schema events. By the end of the modules, you will know how to confidently handle schema evolution, automate Change Data Capture (CDC), and ensure data integrity.

Through lectures and hands-on demos, you will:

• Build multi-flow pipelines to ingest multi-source data into a unified Bronze table.

• Apply Liquid Clustering and Data Quality Expectations across Silver and Gold layers.

• Implement the Multiplex pattern with Iceberg UniForm for cross-platform data access.

• Automate SCD Type 2 history tracking using AUTO CDC INTO.

• Design zero-data-loss quarantine pipelines to audit and manage invalid records.

Note:

1. This course is the first in the 'Advanced Data Engineering with Databricks' series.

2. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

Data Modeling Strategies

This course offers a deep dive into designing data models within the Databricks Lakehouse environment, and understanding the data products lifecycle. Participants will learn to align business requirements with data organization and model design leveraging Delta Lake and Unity Catalog for defining data architectures, and techniques for data integration and sharing.

Note: The course includes practice labs that the learners should perform after going through the entire course.

Languages Available: English | 日本語 | Português BR | 한국어

Paid & Subscription

Lab

Associate