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.
Our Trainers
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 Analyst in RGPV Software Architect in Different Software Companies Technical Expert from last 10 Years Training to faculties of RGPV Artificial Neural Networks Training to faculties of RGPV Machine Learning Expert Training on Software Development & Cloud in Leading Companies, Central Govt. Colleges, State Govt Colleges & Private Colleges across the country. Work as a Project Manager with 20 Developer
Modules
Course Modules: Azure Machine Learning Studio
Module 1: Introduction to Azure ML Studio
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What is Azure Machine Learning?
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Key concepts: Workspaces, Datasets, Compute, Environments
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Navigating the ML Studio interface
Module 2: Data Preparation and Management
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Uploading and managing datasets
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Data labeling and transformation
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Data versioning and reuse
Module 3: Visual Designer (No-Code ML)
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Building ML pipelines with drag-and-drop tools
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Training and evaluating models visually
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Deploying visual designer models
Module 4: Automated Machine Learning (AutoML)
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Configuring AutoML for classification, regression, and forecasting
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Interpreting AutoML results
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Deploying AutoML models
Module 5: Code-First ML with Notebooks
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Using Jupyter Notebooks in ML Studio
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Training custom models with Scikit-learn, TensorFlow, or PyTorch
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Working with Azure ML SDK and CLI
Module 6: Model Training and Tuning
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Setting up compute clusters
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Running training jobs
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Hyperparameter tuning with sweep jobs
Module 7: Model Deployment
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Registering models
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Real-time vs. batch inference
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Deploying to endpoints and testing
Module 8: Monitoring and MLOps
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Tracking experiments with MLFlow
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Model versioning and reproducibility
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Integrating with Azure DevOps/GitHub for CI/CD
Module 9: Capstone Project
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End-to-end project using real-world data
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Demonstrating data ingestion, model training, and deployment
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Presentation and peer review
Outcomes
By the end of this course, you will be able to:
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✅ Navigate and utilize Azure Machine Learning Studio effectively.
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✅ Prepare, upload, and manage datasets for machine learning workflows.
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✅ Build and deploy machine learning models using both no-code and code-first approaches.
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✅ Leverage Automated ML to rapidly train and tune models.
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✅ Use Jupyter Notebooks within Azure to run custom ML experiments.
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✅ Train, evaluate, and optimize models at scale using cloud compute resources.
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✅ Deploy models as real-time or batch inference endpoints.
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✅ Monitor deployed models and manage the full ML lifecycle with MLOps best practices.
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✅ Collaborate using versioning, experiment tracking, and pipeline automation.
