Practical Machine Learning on Databricks
Seamlessly transition ML models and MLOps on Databricks
Created by Debu Sinha
Explore how to harness Databricks for end-to-end machine learning, from data preparation to automated deployment. You'll get practical experience with tools like AutoML and MLflow, making it easier to manage and scale your ML projects. Build confidence in moving models from development to production with real-world workflows.
Packt | Nov 2023 | 244 min
What You Will Learn
You'll work through practical examples that guide you from setting up your Databricks environment to building and deploying machine learning models. Each step focuses on hands-on tasks, like configuring AutoML, tracking experiments, and managing model versions. By practicing with these tools, you'll be ready to handle real ML projects on Databricks.
Key Features
- Set up and automate ML pipelines using Databricks AutoML and MLflow
- Prepare and manage data efficiently with Databricks Feature Store
- Deploy, monitor, and govern models using MLflow Model Registry
Target Audience
Designed for data scientists, engineers, and developers with solid Python and ML experience who want to move their workflows to Databricks. If you already understand Spark basics and want to streamline ML operations, automate deployment, and scale solutions, you'll find clear guidance and actionable skills to reach your goals.
