Machine Learning on Azure
Build and deploy data-science & ML solutions using Azure services (AML, Databricks, Power BI) and Automated ML best practices.
Get Course Info
Audience: Developers / Architects
Duration: 4 days
Format: Lectures and hands-on labs (50% lecture, 50% lab)
Overview
Microsoft Azure offers an integrated stack of services for Machine Learning. This course provides an end-to-end journey: ML foundations with Python & R, Automated ML, Azure Machine Learning workspace, Databricks, and deployment pipelines—preparing students for Azure ML certification.
Objective
Build and deploy data-science & ML solutions using Azure services (AML, Databricks, Power BI) and Automated ML best practices.
What You Will Learn
- ML overview & best practices
- Automated ML workflow & guardrails
- Azure Machine Learning Studio & Workbench
- Feature engineering & model interpretability
- Azure Databricks for ML (Spark)
- Deploying & monitoring models via Azure services & Stream Analytics
Course Details
Audience: Developers / Architects
Duration: 4 days
Format: Lectures and hands-on labs (50% lecture, 50% lab)
- Interest in ML; familiarity with Python or R helpful
Setup: Modern laptop · Internet · Chrome · SSH client
Detailed Outline
- Model parameters vs. hyperparameters
- Performance metrics
- Feature engineering
- Algorithm selection
- Task detection
- Metric selection
- Monitoring & retraining
- Guardrails
- End-to-end lifecycle
- Collaboration & monitoring
- Deployment
- AML workspace setup
- Azure Notebooks & VM
- Auto-featurisation
- Deploying Auto-ML models
- Databricks environment
- Regression & classification algorithms
- Using Auto-ML
- Azure Portal UI
- Power BI integration
- End-to-end demo
Ready to Get Started?
Contact us to learn more about this course and schedule your training.