Databricks Machine Learning Build, Tune & Deploy ML Models
Published 9/2026
Created by ACHRAF ER-RAYA
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 6 Lectures ( 1h 48m ) | Size: 1.1 GB

Build, tune, track, and register machine learning models on Databricks using Spark, MLflow, Optuna, and AutoML.
[h2]What you'll learn[/h2]
⚡ 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.
[h2]Requirements[/h2]
❗ 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.
[h2]Description[/h2]
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.
[h2]Who this course is for[/h2]
⭐ 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.
[h2]Homepage[/h2]

https://www.udemy.com/course/databricks-machine-learning-build-tune-deploy-ml-models

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