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Home > Technical Courses > Artificial Intelligence AI > Operationalize Machine Learning and Generative AI Solutions (AI-300)
AI-300 focuses on deploying, managing, and optimizing machine learning and generative AI solutions on Azure.
This course covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.
Category
ID
Duration
Level
Price
Artificial Intelligence AI
14091
4 Day(s)
Intermediate
$2,076.00
Objectives
Core objectives of the AI-300 course include: • Implement MLOps and GenAIOps: Design and operate full-lifecycle operational workflows for both traditional machine learning models and generative AI agents. • Automate AI Workflows: Build repeatable continuous integration and continuous delivery (CI/CD) pipelines using GitHub Actions and Azure-native tools. • Manage and Track Models: Utilize MLflow for experiment tracking, run automated machine learning experiments, and optimize hyperparameters. • Optimize Generative AI Applications: Select and fine-tune embedding models, manage prompt versions, and apply advanced retrieval strategies for Retrieval-Augmented Generation (RAG) frameworks. • Implement Monitoring and Tracing: Set up logging, performance metrics, and tracing for complex workflows to debug and analyze generative AI behavior. • Ensure Governance and Observability: Evaluate models using Responsible AI dashboards, monitor for data drift, and apply security best practices across the AI lifecycle.
Questions?
To succeed, you are strongly recommended to possess foundational skills across the following core areas: · Python Programming: Strong working proficiency in Python or R is required for writing scripts and configuring pipelines. · Machine Learning Fundamentals: A solid understanding of data science concepts and experience developing, training, and evaluating traditional machine learning models. · Azure Machine Learning: Hands-on familiarity with the Azure Machine Learning platform, including workspaces, data assets, and compute targets. · Generative AI Concepts: Entry-level knowledge of large language models (LLMs), prompt engineering, and cloud-based generative AI tools. · DevOps Fundamentals: Basic knowledge of source control (Git), Continuous Integration/Continuous Deployment (CI/CD) pipelines, and executing commands via command-line interfaces (CLIs)
Operationalize Machine Learning and Generative AI Solutions (AI-300)
Class Schedule
Live Online
Course Overview
Training Delivery Methods
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