Databricks Machine Learning Build, Tune & Deploy ML Models

Build, tune, track, and register machine learning models on Databricks using Spark, MLflow, Optuna, and AutoML.
What you'll learn

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Build and configure production-grade Databricks ML Runtime clusters for high-performance distributed workloads.
Process large datasets and build scalable ML feature pipelines using PySpark, Delta Lake, and Feature Store.
Automate distributed hyperparameter tuning and model tracking using Optuna, Hyperopt, AutoML, and MLflow.
Register, version, and manage enterprise models in Unity Catalog while optimizing cloud compute costs.
Requirements

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Basic Python programming knowledge and familiarity with foundational machine learning concepts (such as Scikit-Learn or basic model training). Access to a free Databricks Community Edition account or a standard Databricks workspace.
Description

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This course contains the use of artificial intelligence.
Build a complete, tracked, tuned, and registered machine learning model in Databricks-starting with a Delta table and ending with a portfolio-ready project.
Most Databricks learners can open a notebook and run a query. Far fewer can confidently turn real Spark data into a machine learning workflow that is reproducible, cost-aware, measurable, and ready for team handoff.
In this practical course, you will build a customer churn prediction project using Databricks Runtime ML, Spark, MLflow, Optuna, AutoML, and feature-management practices. You will learn how to move from raw data to model decision without getting lost in disconnected tools, untracked experiments, or expensive trial-and-error tuning.
Who this course is for

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Data Scientists, ML Engineers, and Data Engineers who know local Python ML (Scikit-Learn/Pandas) and want to learn how to scale, automate, tune, and deploy end-to-end ML pipelines on enterprise Databricks infrastructure.
Homepage

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