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DP-3014: Build Machine Learning solution with Azure Databricks
This intermediate course shows data scientists and ML engineers how to build, train and deploy machine learning models at scale with Azure Databricks. It covers Apache Spark, MLflow for managing the model lifecycle, hyperparameter tuning, AutoML, deep learning and running models in production.
Duration
8 Hours
Modules
1 Modules
Course type
Microsoft
Course Overview
Master scalable machine learning with Azure Databricks, a powerful cloud-based platform built on Apache Spark. This intermediate-level course equips data scientists and ML engineers to build, train, and deploy models using open-source frameworks and integrated tools like MLflow and AutoML.
Before You Begin
- Experience with Python programming
- Familiarity with ML frameworks (Scikit-Learn, PyTorch, TensorFlow)
- Basic understanding of data science workflows
- Completion of “Create Machine Learning Models” learning path recommended
Key outcomes
- Build ML Pipelines Create scalable workflows using Databricks and Spark.
- Train and Evaluate Models Use ML frameworks for training and performance checks.
- Automate Model Selection Streamline hyperparameter tuning and model choice.
- Deploy and Monitor Models Put models into production and track performance.
Tools you’ll use
Azure Databricks
MLflow
Apache Spark
Optune
Skills you’ll gain
- Scalable Spark Training
- ML Lifecycle Management
- Hyperparameter Tuning with Optune
- Deep Learning Development
- Production Model Deployment
- Distributed Model Training
- MLflow Experiment Tracking
- AI Model Optimization
What You’ll Learn
1 module
1Lesson 1: Build Machine Learning Solution using Azure Databricks
Module 1: Explore Azure Databricks Module 2: Use Apache Spark in Azure Databricks Module 3: Train a Machine Learning Model in Azure Databricks Module 4: Use MLflow in Azure Databricks Module 5: Tune Hyperparameters in Azure Databricks Module 6: Use AutoML in Azure Databricks Module 7: Train Deep Learning Models in Azure Databricks Module 8: Manage Machine Learning in Production with Azure Databricks
Certification & Credentials
Certificate track
8 Hours
Target Audience
Intended roles
- Data Scientist
- Machine Learning Engineer
Frequently asked questions
What is Azure Databricks used for?
Azure Databricks is a cloud platform for big data analytics and scalable machine learning.
Do I need to know Python for this course?
Yes, Python experience is essential for working with ML frameworks and Databricks notebooks.
Can I use open-source ML libraries in Databricks?
Absolutely. Databricks supports Scikit-Learn, PyTorch, TensorFlow, and more.
Is this course suitable for beginners?
No, it’s designed for intermediate learners with prior ML and Python experience.
How do I deploy models in Databricks?
You’ll learn to use MLflow and Databricks tools to deploy models into production environments.
| Duration | 8 Hours |
|---|---|
| Modules | 1 Modules |
| Course type | Microsoft |
| Category | Microsoft |
| Intended roles | Data Scientist, Machine Learning Engineer |
| Prerequisites | Experience with Python programming, Familiarity with ML frameworks (Scikit-Learn, PyTorch, TensorFlow), Basic understanding of data science workflows, Completion of “Create Machine Learning Models” learning path recommended |
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