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
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 |
|---|---|---|---|---|
Oct 27 | 09 AM - 01 PM (Asia/Kolkata) | - | English | $750.00 |
Dec 02 | 09 AM - 01 PM (Asia/Kolkata) | - | English | $750.00 |
Dec 02 | 01 PM - 05 PM (Europe/Paris) | - | English | $750.00 |
Dec 02 | 09 AM - 01 PM (America/New_York) | - | English | $750.00 |
Jan 06 | 09 AM - 01 PM (Asia/Singapore) | - | English | $750.00 |
Jan 06 | 01 PM - 05 PM (America/New_York) | - | English | $750.00 |
Jan 07 | 09 AM - 01 PM (Europe/Paris) | - | English | $750.00 |
Public Class Registration
If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.
Private Class Request
If your company is interested in private training, please submit a request.
Registration options
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
Register now
Instructor-Led
Public and private courses taught by expert instructors across half-day to two-day courses
Register now
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
Purchase now
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
