Advance tools and application of AI in Microsoft Azure #254009

Upon successful completion of this course, participants will be able to:

• Architect end-to-end AI solutions by selecting and integrating the appropriate Azure AI services for a given use case.

• Build, train, and deploy complex machine learning models using the Azure Machine Learning SDK and studio interface.

• Implement pre-built AI capabilities into applications using Azure Cognitive Services and Azure OpenAI Service.

• Automate the machine learning lifecycle (MLOps) using Azure ML pipelines, model registry, and endpoints for continuous integration and deployment (CI/CD).

• Apply responsible AI principles in practice using Azure’s tools for fairness, interpretability, and data governance.

• Design and optimize AI solutions for performance, cost, and scalability within the Azure ecosystem.

This course is designed for data professionals, developers, and cloud architects who want to build advanced AI solutions on Microsoft Azure.

• Data Scientists & ML Engineers

• Cloud Developers & Solution Architects

• AI/Software Developers looking to integrate AI into applications

• DevOps/MLOps Engineers focused on AI workloads

• IT Professionals with a background in data and analytics

• Technical Data Analysts upskilling into machine learning

Prerequisites: Foundational knowledge of machine learning concepts. Basic familiarity with Python and the Azure portal is highly recommended. Experience with earlier Azure AI Fundamentals is beneficial but not mandatory.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Laying the Advanced Foundation in Azure AI

• Module 1: The Azure AI Ecosystem Strategy

  • Overview of the Azure AI portfolio: Azure ML, Cognitive Services, Azure OpenAI, and Applied AI Services.

  • Choosing the right tool: Custom models vs. pre-built APIs.

• Module 2: Core Azure Machine Learning Workspace

  • Deep dive into Azure Machine Learning workspace architecture: compute instances, clusters, datastores, and datasets.

  • Securing the workspace with managed identities and VNet integration.

• Hands-On Lab: Setting up an enterprise-ready AML workspace and configuring compute targets.

Day 2: Advanced Model Development & Training

• Module 3: The Azure Machine Learning SDK

  • Using the Python SDK for experiment tracking, model training, and hyperparameter tuning with HyperDrive.

  • Leveraging automated ML (AutoML) for classification, regression, and forecasting.

• Module 4: Advanced Training Techniques

  • Training models on specialized compute (GPU clusters).

  • Managing complex environments with Conda and Docker.

• Hands-On Lab: Building a model using both the SDK and AutoML, and comparing results.

Day 3: Integrating Intelligent APIs and Responsible AI

• Module 5: Azure Cognitive Services for Advanced Applications

  • Implementing vision, language, speech, and decision services.

  • Customizing Cognitive Services (e.g., Custom Vision, Custom Text Analytics).

• Module 6: Azure OpenAI Service

  • Accessing and fine-tuning state-of-the-art large language models (GPT, Embeddings).

  • Prompt engineering and building conversational AI agents.

• Module 7: Implementing Responsible AI

  • Using Azure’s Responsible AI dashboard: Fairlearn, InterpretML, and Error Analysis.

  • Detecting and mitigating model bias.

• Hands-On Lab: Building a multi-service solution that combines a custom AML model with the Language service and OpenAI API.

Day 4: MLOps and Deployment at Scale

• Module 8: Mastering the MLOps Lifecycle with Azure ML

  • Packaging and registering models in the Azure ML model registry.

  • Building reproducible ML pipelines using the Azure ML Pipelines SDK.

• Module 9: Deployment and Monitoring

  • Deploying models to real-time and batch endpoints.

  • Implementing advanced monitoring for data drift, model performance degradation, and triggering retraining.

• Hands-On Lab: Creating an ML pipeline that trains a model, registers it, and deploys it to a managed endpoint. Configuring data drift detection.

Day 5: Capstone Project and Architecture Optimization

• Module 10: Solution Architecture and Optimization

  • Designing for security, cost, and high availability.

  • Reviewing best practices and common architectural patterns.

• Capstone Project:

  • Participants are given a complex business problem and must architect, build, deploy, and monitor a full AI solution on Azure, integrating multiple services covered during the week.

• Course Wrap-Up:

  • Review of key learnings, Azure certification paths, and resources for continued learning.

  • Q&A and action planning.