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

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

Course overview 

The AI-300 Operationalize machine learning and generative AI solutions course prepares students to design, implement, and operate Machine Learning (MLOps) and generative artificial intelligence (GenAIOps) operations in Azure solutions. It covers the creation of a secure and scalable artificial intelligence infrastructure, the management of the full lifecycle of traditional machine learning models with Azure Machine Learning, and the deployment, evaluation, monitoring, and optimization of generative artificial intelligence applications and agents using Microsoft Foundry.

Students will gain practical knowledge of automation, continuous integration and delivery, infrastructure as code, and observability 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 artificial intelligence systems aligned with modern MLOps and GenAIOps best practices.

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

Promotion valid until December 31, 2026. One-attempt exam available only in Virtual - Distance Learning format.

What the official Microsoft course at Nanfor includes

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

Learn all the components

Training objectives

Upon completion of the AI‑300: Operationalize machine learning and generative AI solutions course, the student will be able to:

• Design and implement production-ready AI solutions in Microsoft Azure.
  
• Operate the full 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 make the most of the AI‑300 course, it is recommended that the student has:

• Previous experience with Python.
  
• Foundational 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 or wish to work with AI in production environments.

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

Who is this course intended for?

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

It is suitable for students with experience in Python, foundational knowledge 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 Microsoft Learn AI-300 collection

• Operationalizing machine learning models (MLOps) (7 modules)
• Operationalizing generative AI applications (GenAIOps) (6 modules)

Operationalize machine learning and generative AI solutions course content - 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 using 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 of generative AI workloads
  
• Optimizing and 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

The AI-300 course at Nanfor prepares you 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

Want to take this course? Request information now

Frequently Asked Questions

Is this course suitable for beginners in Artificial Intelligence?

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 sets this course apart 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.

Are generative AI solutions covered?

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

What tools are used during the course?

The course uses 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 you 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 format of the course?

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

On-demand in-person or remote training can also be arranged.