AI-300: Operationalize machine learning and generative AI solutions - Certification Associate

The AI-300: Operationalize machine learning and generative AI solutions course replaces the DP-100 course, which will be retired in April 2026.

AI-300 Course: Operationalize machine learning and generative AI solutions

Course overview

The AI-300: Operationalizing Machine Learning and Generative AI Solutions course prepares students to design, implement, and operate Machine Learning Operations (MLOps) and Generative Artificial Intelligence Operations (GenAIOps) solutions on Azure. It covers creating a secure and scalable AI infrastructure, managing the entire lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry.

Students will gain practical knowledge of automation, continuous integration and delivery, infrastructure as code, and observability using tools like 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.

Virtual course with a free certification exam included. Don't miss this opportunity! The exam is valued at €126 + VAT and is included at no additional cost.

Promotion valid until June 30, 2026. One-attempt exam available only in Virtual - Distance Learning mode.

What the official Microsoft course at Nanfor includes

The course includes official Microsoft Learn materials, expert tutor presentations, official labs, specialized tutoring, personalized sessions with an expert tutor, certification preparation, and a certificate of completion, combining official content with Nanfor's expert guidance.

Learn about all components

Training objectives

Upon completing the AI‑300: Operationalize machine learning and generative AI solutions course, students will be able to:

• Design and implement production-ready AI solutions in Microsoft Azure.
  
• Operate the complete lifecycle of Machine Learning models using MLOps practices.
  
• Deploy, evaluate, monitor, and optimize generative AI applications (GenAIOps).
  
• Automate training, deployment, and evaluation processes using CI/CD.
  Implement infrastructure as code and automated workflows with GitHub Actions.
  
• Monitor the performance, quality, and traceability of AI models and agents in production environments.
  
• Collaborate effectively with Data Science and DevOps teams on enterprise AI projects.

Prerequisites

To benefit fully from the AI‑300 course, students are recommended to have:

• Prior experience in Python.
  
• Fundamental knowledge of Machine Learning.
  
• Familiarity with Microsoft Azure or cloud environments.
  
• Basic knowledge of DevOps, such as:
  • Version control (Git).
    
  • CI/CD pipelines.
    
  • Command-line interface (CLI) usage.

This course is not introductory and is aimed at technical profiles who already work with or wish to work with AI in production environments.

Level: Intermediate - Product: Azure - Role: AI Engineer, Data Scientist

Who is this course for?

This course is designed for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-level artificial intelligence solutions in Azure.

It is suitable for students with experience in Python, a fundamental understanding of machine learning concepts, and basic knowledge of DevOps practices such as source control, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using native Azure services.

Elements of the AI-300 Microsoft Learn collection

• Operationalizing Machine Learning Models (MLOps) (7 modules)
• Operationalizing Generative AI Applications (GenAIOps) (6 modules)

Course content Operationalizing Machine Learning and Generative AI Solutions - Program

Unit 1: Implementing Generative AI and Machine Learning Solutions

• Designing a Machine Learning training solution
  
• Experimenting with Azure Machine Learning
  
• Optimizing model training in Azure Machine Learning
  
• Performing hyperparameter tuning with Azure Machine Learning

Labs

• Case Study: Designing a Machine Learning Solution
  
• Case Study: Designing a Data Ingestion Solution
  
• Case Study: Choosing the Model Training Service
  
• Case Study: Designing a Model Deployment Solution
  
• Exercise: Finding the best classification model with Azure Machine Learning
  
• Exercise: Optimizing model training in Azure Machine Learning
  
• Exercise: Performing hyperparameter tuning with a sweep job

Unit 2: Implementing Generative AI and Machine Learning Solutions

• Running pipelines in Azure Machine Learning
  
• Planning and preparing an MLOps solution with Azure Machine Learning
  
• Automating model training with GitHub Actions
  
• Deploying and monitoring a model in Azure Machine Learning

Labs

• Exercise: Running pipelines in Azure Machine Learning
  
• Exercise: Deploying and monitoring a model in Azure Machine Learning

Unit 3: Implementing Generative AI and Machine Learning Solutions

• Planning and preparing a GenAIOps solution
  
• Managing agent prompts in Microsoft Foundry with GitHub
  
• Evaluating and optimizing AI agents through structured experiments

Labs

• Exercise: Planning and preparing a GenAIOps solution
  
• Exercise: Developing prompt and agent versions
  
• Exercise: Designing and optimizing prompts

Unit 4: Implementing Generative AI and Machine Learning Solutions

• Automating AI evaluations with GitHub Actions and Microsoft Foundry
  
• Implementing observability and monitoring for generative AI workloads
  
• Optimizing and fine-tuning AI agents for production

Labs

• Exercise: Automated evaluation with cloud evaluators
  
• Exercise: Monitoring and tracking the generative AI agent
  
• Exercise: Optimizing AI agents with fine-tuning

Language

• Course: English / Spanish
• Labs: English / Spanish

Microsoft Associate Certification: Machine Learning Operations (MLOps) Engineer Associate

Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate

Nanfor's AI-300 course prepares for the official Microsoft Machine Learning Operations (MLOps) Engineer Associate certification

Level: Intermediate
Role: AI Engineer
Product: Azure Machine Learning, Microsoft Foundry
Subject: Artificial Intelligence, Machine Learning

Do you want to take this course? Request information now

Frequently Asked Questions

Is this course suitable for Artificial Intelligence beginners?

No. AI‑300 is designed for technical profiles with previous experience in machine learning, programming, and Azure. Its focus is on bringing AI solutions to real production environments.

What differentiates this course from other AI courses in Azure?

This course focuses on operationalizing AI solutions, applying MLOps and GenAIOps practices, automation, monitoring, and scalability, beyond model training.

Does it cover generative AI solutions?

Yes. The course includes the design, deployment, evaluation, and optimization of generative AI applications and agents using tools like Microsoft Foundry.

What tools are used during the course?

The course works with key tools from the Microsoft ecosystem, such as:

• Azure Machine Learning
  
• Microsoft Foundry
  
• GitHub Actions
  
• Azure CLI and CI/CD automation

Does this course prepare for any official Microsoft certification?

Yes. The course content is aligned with the AI‑300 exam and the Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate certification.

What is the duration and mode of the course?

The training is virtual - distance learning with support always by your side. It takes place in Nanfor's virtual classroom, with 3 months of access and the possibility of extending for one more month (not available for subsidized training).

In-person or telepresence training can also be arranged on demand.