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Online Course Azure Machine Learning Studio
Skill Developement

Azure Machine Learning Studio

Where cloud meets intelligence—build, train, and deploy ML models seamlessly on Azure
Course ModeOffline
Duration2 Weeks
Starts30 Jun 2025
Ends11 Jul 2025
Course Fee
₹1,000 /-
Registration Closed
About The Course

Overview

This course offers a comprehensive introduction to Azure Machine Learning Studio, Microsoft’s cloud-based platform for building, training, and deploying machine learning models. Whether you're a beginner or an experienced developer, you'll learn how to use Azure's powerful tools—including automated ML, the visual designer, and code-first notebooks—to accelerate your AI projects. Through hands-on labs and real-world scenarios, you'll gain the skills to manage datasets, train models, deploy endpoints, and integrate MLOps workflows.

Learning Goals

Course Outcomes

By the end of this course, you will be able to:

  • ✅ Navigate and utilize Azure Machine Learning Studio effectively.

  • ✅ Prepare, upload, and manage datasets for machine learning workflows.

  • ✅ Build and deploy machine learning models using both no-code and code-first approaches.

  • ✅ Leverage Automated ML to rapidly train and tune models.

  • ✅ Use Jupyter Notebooks within Azure to run custom ML experiments.

  • ✅ Train, evaluate, and optimize models at scale using cloud compute resources.

  • ✅ Deploy models as real-time or batch inference endpoints.

  • ✅ Monitor deployed models and manage the full ML lifecycle with MLOps best practices.

  • ✅ Collaborate using versioning, experiment tracking, and pipeline automation.

Curriculum

Course / Modules

Course Modules: Azure Machine Learning Studio

Module 1: Introduction to Azure ML Studio

  • What is Azure Machine Learning?

  • Key concepts: Workspaces, Datasets, Compute, Environments

  • Navigating the ML Studio interface

Module 2: Data Preparation and Management

  • Uploading and managing datasets

  • Data labeling and transformation

  • Data versioning and reuse

Module 3: Visual Designer (No-Code ML)

  • Building ML pipelines with drag-and-drop tools

  • Training and evaluating models visually

  • Deploying visual designer models

Module 4: Automated Machine Learning (AutoML)

  • Configuring AutoML for classification, regression, and forecasting

  • Interpreting AutoML results

  • Deploying AutoML models

Module 5: Code-First ML with Notebooks

  • Using Jupyter Notebooks in ML Studio

  • Training custom models with Scikit-learn, TensorFlow, or PyTorch

  • Working with Azure ML SDK and CLI

Module 6: Model Training and Tuning

  • Setting up compute clusters

  • Running training jobs

  • Hyperparameter tuning with sweep jobs

Module 7: Model Deployment

  • Registering models

  • Real-time vs. batch inference

  • Deploying to endpoints and testing

Module 8: Monitoring and MLOps

  • Tracking experiments with MLFlow

  • Model versioning and reproducibility

  • Integrating with Azure DevOps/GitHub for CI/CD

Module 9: Capstone Project

  • End-to-end project using real-world data

  • Demonstrating data ingestion, model training, and deployment

  • Presentation and peer review

Learn From Experts

Our Trainers

GAURAV NEMA
GAURAV NEMA
 Present: C.O.O and C.T.O. at INNOBIMB INFOTECH Pvt Ltd  Solution Architect and Consultant in CRISP  Solution Architect and Data Analys...
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Contact

Name: Menali Paul

Mobile No: 7415445081

Email: [email protected]