AI-300T00: Operationalize ML and Generative AI Solutions

The Operationalize Machine Learning and Generative AI Solutions (AI-300T00) course is a 4-day training program focused on MLOps and GenAI operations. Participants will master Azure Machine Learning, model deployment, monitoring, CI/CD for ML, and responsible AI in production. Delivered by Esamatic srl, Microsoft Learning Partner in Milan, with Microsoft Certified Trainers.

• Duration: 4 days of instructor-led training
• Focus: MLOps and generative AI operations
• Platform: Azure Machine Learning, Azure DevOps, GitHub Actions
• Approach: Production-focused labs with CI/CD pipelines
• Certification: Supports Azure Data Scientist Associate certification

Course Overview: Operationalize Machine Learning and Generative AI Solutions

Building ML and generative AI models is only half the challenge—operationalizing them for reliable production use is where most organizations struggle. This course covers the complete MLOps lifecycle on Azure, from model training and registration to automated deployment, monitoring, retraining, and governance. Participants will also learn how to apply operational best practices to generative AI solutions including LLM deployment and prompt management.

Learning Objectives

1. Azure ML Operations — Design and implement MLOps workflows using Azure Machine Learning for reproducible, scalable model management
2. Model Deployment — Deploy ML and generative AI models to managed endpoints, containers, and edge devices with proper versioning
3. CI/CD for ML — Build automated CI/CD pipelines for model training, validation, and deployment using Azure DevOps or GitHub Actions
4. Monitoring & Governance — Implement model monitoring, drift detection, responsible AI checks, and governance policies for production AI systems

Who Should Attend

This course is designed for ML engineers, data scientists, DevOps engineers, and platform engineers who need to operationalize AI and ML models in production environments.

Career Benefits

MLOps expertise bridges the gap between data science and production engineering, making it one of the most valuable specializations in the AI field. Professionals with MLOps skills are essential for any organization serious about deploying AI at scale.

Prerequisites

• Experience with machine learning model development
• Proficiency in Python programming
• Familiarity with Azure Machine Learning basics
• Understanding of CI/CD concepts and DevOps practices

Frequently Asked Questions

Does the course cover generative AI operations specifically?

Yes. The course includes dedicated modules on operationalizing generative AI solutions, including LLM deployment, prompt versioning, evaluation pipelines, and content safety monitoring.

Which CI/CD tools are covered?

The course covers both Azure DevOps Pipelines and GitHub Actions for ML CI/CD, giving participants flexibility to use whichever platform their organization prefers.

Is this course different from a standard DevOps course?

Yes. While it incorporates DevOps principles, the course is specifically tailored for ML and AI workloads, covering unique challenges like data versioning, model drift, experiment tracking, and responsible AI monitoring.

Will I build production-ready MLOps pipelines?

Yes. The course includes extensive labs where you build complete MLOps pipelines from model training through automated deployment and monitoring in Azure.