Skip to main content

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. This course focuses on executing common tasks efficiently with AutoML and MLflow. Participants will delve into key topics, including regression and classification models, harnessing Databricks' capabilities to track model training, leveraging feature stores for model development, and implementing hyperparameter tuning. Additionally, the course covers AutoML for rapid and low-code model training, ensuring that participants gain practical, real-world skills for streamlined and effective machine learning model development in the Databricks environment.


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

Skill Level
Associate
Duration
4h
Prerequisites

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

  • Knowledge of fundamental concepts of regression and classification methods

  • Familiarity with Databricks workspace and notebooks

  • Intermediate level knowledge of Python

Outline

  • Model Development and MLflow
  • Evaluating Model Performance
    • Hyperparameter Tuning Fundamentals
    • Hyperparameter Tuning with Hyperopt
    • Automated Model Development with AutoML


Upcoming Public Classes

Date
Time
Language
Price
Apr 22
01 PM - 05 PM (Asia/Kolkata)
English
$750.00
Apr 23
09 AM - 01 PM (Europe/London)
English
$750.00
Apr 25
09 AM - 01 PM (America/New_York)
English
$750.00
May 19
09 AM - 01 PM (Asia/Kolkata)
English
$750.00
May 20
01 PM - 05 PM (America/New_York)
English
$750.00
Jun 25
09 AM - 01 PM (Europe/London)
English
$750.00
Jun 26
01 PM - 05 PM (Asia/Kolkata)
English
$750.00
Jun 27
09 AM - 01 PM (America/New_York)
English
$750.00
Jul 21
01 PM - 05 PM (America/New_York)
English
$750.00
Jul 24
01 PM - 05 PM (Europe/London)
English
$750.00
Jul 25
09 AM - 01 PM (Asia/Kolkata)
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.

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

Register now

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

Purchase now

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

Automated Deployment with Databricks Asset Bundles

This course provides a comprehensive review of DevOps principles and their application to Databricks projects. It begins with an overview of core DevOps, DataOps, continuous integration (CI), continuous deployment (CD), and testing, and explores how these principles can be applied to data engineering pipelines.

The course then focuses on continuous deployment within the CI/CD process, examining tools like the Databricks REST API, SDK, and CLI for project deployment. You will learn about Databricks Asset Bundles (DABs) and how they fit into the CI/CD process. You’ll dive into their key components, folder structure, and how they streamline deployment across various target environments in Databricks. You will also learn how to add variables, modify, validate, deploy, and execute Databricks Asset Bundles for multiple environments with different configurations using the Databricks CLI.

Finally, the course introduces Visual Studio Code as an Interactive Development Environment (IDE) for building, testing, and deploying Databricks Asset Bundles locally, optimizing your development process. The course concludes with an introduction to automating deployment pipelines using GitHub Actions to enhance the CI/CD workflow with Databricks Asset Bundles.

By the end of this course, you will be equipped to automate Databricks project deployments with Databricks Asset Bundles, improving efficiency through DevOps practices.

Paid
4h
Lab
instructor-led
Professional

Questions?

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