Description

Take the Next Step in Your Azure and Machine Learning Journey!
Whether you're an aspiring data scientist, cloud engineer, software developer, or business leader, this course will equip you with the skills to harness Azure’s powerful machine learning ecosystem for scalable, real-world AI solutions. Learn how Azure ML Studio, AutoML, Python, and integrated Azure services are transforming data preparation, model training, deployment, and monitoring—enabling faster, smarter, and more impactful decision-making.

Guided by hands-on projects and real-world use cases, you will:

• Master foundational machine learning concepts and Azure ML workflows applied to real business scenarios.

• Gain hands-on experience collecting, managing, and preparing data using Azure Blob Storage, Data Lake, and ML Studio.

• Learn to train, optimize, and deploy models using AutoML, the Azure ML SDK, and scalable compute resources.

• Explore industry applications in predictive analytics, recommendation systems, sentiment analysis, and AI-powered automation.

• Understand best practices for MLOps, workflow automation, security, compliance, and cost optimization in Azure ML environments.

• Position yourself for a competitive advantage by developing in-demand skills at the intersection of cloud computing, artificial intelligence, and data analytics.

Master foundational machine learning concepts and Azure ML workflows applied to real business scenarios.

Gain hands-on experience collecting, managing, and preparing data using Azure Blob Storage, Data Lake, and ML Studio.

Learn to train, optimize, and deploy models using AutoML, the Azure ML SDK, and scalable compute resources.

Explore industry applications in predictive analytics, recommendation systems, sentiment analysis, and AI-powered automation.

Understand best practices for MLOps, workflow automation, security, compliance, and cost optimization in Azure ML environments.

Position yourself for a competitive advantage by developing in-demand skills at the intersection of cloud computing, artificial intelligence, and data analytics.

The Frameworks of the Course

• Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises—designed to help you deeply understand how to apply Azure Machine Learning for building, training, deploying, and managing AI solutions in the cloud.

• The course includes industry-specific case studies, Azure ML tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to develop, optimize, and operationalize machine learning models using Azure’s powerful ecosystem.

• In the first part of the course, you’ll learn the basics of machine learning, Azure Cloud Services, and how Azure ML enhances scalability, automation, and integration in AI workflows.

• In the middle part of the course, you will gain hands-on experience using Azure ML Studio, AutoML, Jupyter Notebooks, Python SDK, and integrated services like Azure Data Lake and Power BI to collect, process, and analyze data, train models, and create interactive dashboards.

• In the final part of the course, you will explore MLOps automation, cost optimization, security and compliance strategies, and real-world applications across industries. All your queries will be addressed within 48 hours, with full support throughout your learning journey.

Course Content:

Part 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. Introduction to Azure and Machine Learning

1.1. Basics of Machine Learning - Key Concepts and Use Cases

1.2. Overview of Azure Cloud Services

1.3. Introduction to Azure Machine Learning Services

1.4. Key Features of Azure ML Studio

1.5. Hands-On Activity - Set up an Azure account and explore the Azure portal, Navigate Azure ML Studio and create a workspace

1.6. Conclusion of Introduction to Azure and Machine Learning

Module 2. Data Management on Azure ML

2.1. Data Storage and Management with Azure Blob Storage

2.2. Data Preparation and Feature Engineering in Azure ML Studio

2.3. Introduction to Azure Data Lake for Big Data Analytics

2.4. Importing and Managing Datasets in Azure ML

2.5. Hands - On Activity - Upload datasets to Azure Blob Storage and Connect them to o Azure ML, Perform basic data preprocessing using Azure ML Designer

2.6. Conclusion of Data Management on Azure ML

Module 3. Building and Training Models on Azure ML

3.1. Overview of Azure ML Designer for No - Code ML Development

3.2. Using Jupyter Notebooks and SDK for Code - Based Model Development

3.3. Automated ML (AutoML) in Azure

3.4. Custom Model Training with Azure ML Compute Instances and Clusters

3.5. Hands-On Activity - Train a model using AutoML in Azure ML Studio, Develop a custom ML model using Python and Azure ML SDK.

3.6. Conclusion of Building and Training Models on Azure ML

Module 4. Model Optimization and Evaluation

4.1. Hyperparameter Tuning with Azure ML Hyperdrive

4.2. Evaluating Model Performance Metrics

4.3. Cross-Validation and Model Selection Techniques

4.4. Model Explainability with Azure Interpretability Toolkit

4.5. Hands-On Activity - Optimize a model using Hyperdrive, Evaluate and visualize model performance in Azure ML Studio

4.6. Conclusion of Model Optimization and Evaluation

Module 5. Deploying Machine Learning Models with Azure ML

5.1. Creating Inference Pipelines in Azure ML

5.2. Real Time vs Batch Inference on Azure

5.3. Model Deployment to Azure Kubernetes Service(AKS) or Azure Container Instances.

5.4. Endpoint Configuration and Authentication

5.5. Hands-On Activity - Deploy a trained model to an Azure ML endpoint, Test the deployed model with sample inputs

5.6. Conclusion of Deploying Machine Learning Models with Azure ML

Module 6. Integrating Azure ML with Other Azure Services

6.1. Data Analytics with Azure Synapse and Power BI

6.2. Monitoring and Logging with Azure Monitor

6.3. Workflow Automation with Azure Logic Apps

6.4. Building AI-Powered Applications with Cognitive Services

6.5. Hands-On Activity - Create a dashboard in Power BI integrating predictions from an Azure ML model

6.6. Conclusion of Integrating Azure ML with other Azure Services

Module 7. MLOps and Workflow Automation

7.1. Introduction to MLOps and CI/CD for Machine Learning

7.2. Azure Pipelines for ML Workflow Automation

7.3. Managing Model Versioning and Lifecycles

7.4. Monitoring and Retraining Deployed Models

7.5. Hands-On Activity - Implement an automated ML pipeline using Azure DevOps

7.6. Conclusion of MLOps and Workflow Automation

Module 8. Security, Compliance, and Cost Optimization

8.1. Data Security in Azure ML - Role-Based Access Control (RBAC)

8.2. Compliance with Industry Standards (GDPR, HIPAA, etc.)

8.3. Cost Optimization Strategies for Azure ML Workloads

8.4. Azure ML Pricing Models and Billing Practices

8.5. Hands - On Activity - Set up RBAC roles for a project in Azure ML, Estimate and monitor costs using Azure Cost Management

8.6. Conclusion of Security, Compliance and Cost Optimization

Module 9. Real-World Use Cases and Applications

9.1. Financial Services - Fraud Detection and Risk Management

9.2. Healthcare - Predictive Analytics and Diagnostics

9.3. Retail - Demand Forecasting and Personalization

9.4. Manufacturing - Predictive Maintenance

9.5. Hands-On Activity - Solve a domain-specific problem using Azure ML Services

9.6. Conclusion of Real-World Use Cases and Applications

Part 2: Capstone Project.