Machine Learning Model Development

This comprehensive course provides a practical guide to developing traditional machine learning models on Databricks, emphasizing hands-on demonstrations and workflows using popular ML libraries. Participants will explore key ML techniques, including regression and clustering, while leveraging Databricks' powerful capabilities. The course covers MLflow integration for model tracking, Databricks Feature Store for feature management, and Optuna for hyperparameter tuning. Additionally, participants will learn how to accelerate model development with Genie Code, Databricks' AI-powered coding assistant that uses natural language, MCP connections, instructions, and skills to guide the full ML lifecycle. By the end of the course, learners will have real-world, practical skills to develop, optimize, and deploy machine learning models efficiently in the Databricks environment.

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

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 (databricks-sdk, REST endpoints)

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

• Understanding of machine learning fundamentals, including model training, evaluation, batch inference, and real-time deployment concepts

• Intermediate experience with Unity Catalog for data governance and model registry management

• Basic familiarity with Feature Engineering concepts, including feature tables, feature lookups, and offline vs online feature stores

• Understanding of Delta Lake operations (create tables, perform updates, optimize files, and liquid clustering) and data storage optimization techniques

• Basic knowledge of Apache Spark and PySpark for distributed data processing and User Defined Functions (UDFs)

Outline

Model Development Workflow

• Model Development and MLflow

• Evaluating Model Performance

Hyperparameter Tuning

• Hyperparameter Tuning Fundamentals

• Hyperparameter Tuning with Optuna

Agentic Machine Learning

• Introduction to Genie Code

Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	Jul 07	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 07	09 AM - 01 PM (America/New_York)	-	English	$750.00

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Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	Jul 07	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 07	09 AM - 01 PM (America/New_York)	-	English	$750.00

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Upcoming Public Classes

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Languages Available: English | 日本語 | 한국어