Machine Learning Practitioner

DATB-MLMDEV : Machine Learning Model Development

Course Duration

4 Hours

Delivery

ILT/VILT

Overview

Overview Course Curriculum Training Schedule Exam & Certification FAQs

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 training with Databricks AutoML. 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.

What You'll Learn

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

Who Should Attend

Data scientists and machine-learning engineers working with regression, clustering or classification who want to leverage Databricks for model development.
Professionals using Python and familiar with ML libraries (such as scikit-learn) looking to adopt MLflow, the Databricks Feature Store and hyperparameter tuning frameworks into their workflow.
Practitioners tasked with managing the end-to-end model-development lifecycle, including model tracking, experiment management and automated model training (AutoML) within the Databricks platform.
Individuals with intermediate Python skills and a basic understanding of machine-learning concepts (for example regression vs classification) who wish to deepen their practical capabilities on the Databricks Lakehouse.
Teams aiming to increase productivity in model development by leveraging Databricks features for experiment tracking, feature management and collaborative model workflows.

Prerequisites

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

Knowledge of fundamental concepts in ML (e.g. regression vs classification models)
Familiarity with the Databricks Workspace, Notebooks
Intermediate-level knowledge of Python
Basic knowledge of ML libraries (e.g. scikit-learn)
Familiarity with Unity Catalog and MLflow is a plus

Learning Journey

Coming Soon...

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

Frequently Asked Questions (FAQs)

None

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Course Curriculum

Training Schedule

Exam & Certification

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