AI & LLMOps Mastery with Azure

• Deploy machine learning models using Azure Machine Learning.
• Implement AI-powered search solutions using Azure AI Search.
• Develop generative AI applications using Microsoft Foundry.
• Build RAG-based agents using custom data.
• Fine-tune and evaluate language models.
• Understand LLMOps and the AI model lifecycle.
• Automate model retraining and deployment.
• Monitor LLM performance, drift, and biases.
• Implement CI/CD pipelines for LLM applications.
• Build and deploy AI-powered chatbots.

Day 1: Machine Learning with Azure ML

• Make data available in Azure Machine Learning
• Work with compute targets in Azure ML
• Configure environments for ML workflows
• Run a training script as a command job
• Track model training using MLflow
• Register and manage MLflow models in Azure ML
• Deploy trained models to managed online endpoints

Day 2: Implementing Search Solutions with Azure AI Search

• Introduction to Azure AI Search & its capabilities
• Creating search resources in Azure
• Implementing AI-powered search skills
• Enriching search indexes for better retrieval
• Integrating AI Search with client applications

Day 3: Generative AI with Azure AI Foundry

• Introduction to Azure AI Foundry
• Exploring & deploying models from the Foundry model catalog
• Developing LLM-based applications using Prompt Flow
• Building RAG-based agents with custom data
• Fine-tuning LLMs in Azure AI Foundry
• Evaluating Generative AI models for performance
• Implementing Responsible AI principles

Day 4: LLMOps with Azure & Developing Bots

Mastering LLMOps for Large-Scale AI Deployment

• Understanding LLMOps & its role in AI model lifecycle
• Managing scalability, performance, and cost optimization
• Automating model retraining & deployment
• Monitoring LLM performance, drift, and biases
• Best practices for secure & responsible LLM deployment

Developing Bots with Azure Bot Services

• Introduction to Azure Bot Services & Conversational AI
• Designing and deploying AI-powered chatbots
• Integrating bots with Azure AI and OpenAI APIs
• Implementing Azure AI Speech & Language Services in chatbots

4 Days

Certification: Microsoft Certified: Machine Learning Operations Engineer Associate

Related Exam: AI-300 – Operationalizing Machine Learning and Generative AI Solutions

Duration: 120 minutes

• Data preparation and compute setup in Azure Machine Learning
• Running and tracking ML training jobs
• Deploying a machine learning model to an endpoint
• Setting up an AI-powered search solution
• Implementing search enrichment and indexing
• Integrating Azure AI Search with an external application
• Deploying models from the Microsoft Foundry model catalog
• Developing an LLM-powered application using Prompt Flow
• Fine-tuning a language model and evaluating its performance
• Automating LLM model retraining with Azure MLOps
• Monitoring AI models for drift and performance issues
• Implementing CI/CD pipelines for LLM applications
• Building and deploying a chatbot with Azure Bot Services

• Data Scientists
• Machine Learning Engineers
• DevOps Professionals
• Infrastructure specialists working with Azure AI
• Professionals working with machine learning and generative AI systems

• Basic understanding of Azure CLI (az)
• Experience with Azure Developer CLI (azd)
• Proficiency with Git and GitHub CLI (gh)
• An Azure subscription for hands-on practice in the LLMOps module