AI-300T00: Operationalize Machine Learning and Generative AI Solutions Training Course

Master MLOps and GenAIOps on Microsoft Azure

Artificial Intelligence is rapidly moving from experimentation to production. Organisations are increasingly investing in machine learning and generative AI solutions to automate processes, improve customer experiences, and unlock business value. However, developing AI models is only one part of the journey. The real challenge lies in operationalising those solutions effectively, securely, and at scale.

The AI-300T00: Operationalize Machine Learning and Generative AI Solutions course is designed to help IT professionals, AI engineers, machine learning engineers, data scientists, and DevOps specialists build the skills needed to deploy, manage, monitor, and optimise AI workloads in production environments using Microsoft Azure.

This intensive four-day Microsoft training course provides practical experience with both Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps). Participants learn how to automate workflows, manage machine learning lifecycles, deploy generative AI applications, and implement continuous integration and continuous delivery (CI/CD) practices using Azure Machine Learning, Microsoft Foundry, GitHub Actions, Azure CLI, and Infrastructure as Code tools such as Bicep.

Whether your organisation is deploying predictive machine learning models or building advanced AI agents powered by large language models, this course provides the operational expertise required to ensure reliability, scalability, governance, and performance.

Ready to book your place?

What is AI-300T00?

AI-300T00 is a Microsoft training course focused on operationalising machine learning and generative AI solutions within Azure environments. It bridges the gap between AI development and production operations by introducing industry-standard MLOps and GenAIOps practices.

The course teaches learners how to:

Build scalable AI infrastructure
Automate machine learning workflows
Implement CI/CD pipelines for AI workloads
Track and manage machine learning experiments
Deploy and monitor production models
Evaluate and optimise AI agents
Implement observability and tracing
Manage prompts and prompt versioning
Apply governance and responsible AI practices

By the end of the course, participants will understand how to create production-ready AI systems that align with modern operational standards.

Why MLOps and GenAIOps Matter

The Evolution of AI Operations

Traditional software development follows established DevOps practices that enable rapid deployment, monitoring, and continuous improvement. AI systems introduce additional complexity because they rely on data, models, prompts, training pipelines, and evaluation processes that must also be managed throughout their lifecycle.

MLOps extends DevOps principles to machine learning systems by providing structured approaches for:

Model development
Experiment tracking
Data versioning
Automated deployment
Monitoring and retraining

GenAIOps builds upon these principles to address the unique challenges of generative AI applications, including:

Prompt engineering
Agent orchestration
Response evaluation
Hallucination monitoring
Safety controls
Model optimisation

As organisations deploy increasingly sophisticated AI solutions, MLOps and GenAIOps have become essential disciplines for maintaining quality, security, compliance, and business value.

Who Should Attend AI-300T00?

This course is ideal for professionals responsible for deploying, managing, or supporting AI solutions within Azure environments.

Recommended Roles

AI Engineers

Professionals designing and deploying machine learning and generative AI solutions.

Machine Learning Engineers

Engineers responsible for training, evaluating, deploying, and maintaining machine learning models.

Data Scientists

Individuals seeking to transition machine learning projects from experimentation into production.

DevOps Engineers

Technical professionals looking to expand their expertise into AI and machine learning operations.

Cloud Architects

Architects designing scalable AI infrastructure and governance frameworks.

Technical Leads

Decision-makers overseeing AI implementation projects and operational strategies.

Course Overview

The AI-300T00 course spans four intensive days and combines theory with practical exercises.

Participants gain hands-on experience using:

Azure Machine Learning
Microsoft Foundry
GitHub Actions
Azure CLI
MLflow
Bicep
Python
Azure Monitoring Services

The curriculum covers the complete lifecycle of machine learning and generative AI applications.

Module 1: Experiment with Azure Machine Learning

Building the Foundation for Successful Models

Machine learning success starts with effective experimentation.

In this module, learners explore how Azure Machine Learning supports data preparation, automated machine learning, model evaluation, and experiment tracking.

Key Learning Areas

Data Preprocessing

Learn how to prepare datasets for training by:

Cleaning data
Transforming variables
Managing missing values
Configuring feature engineering

Automated Machine Learning

Understand how Azure AutoML accelerates model development through automated experimentation and model selection.

Model Evaluation

Compare candidate models using performance metrics and validation techniques.

MLflow Integration

Track experiments and maintain reproducibility through MLflow.

Responsible AI Dashboard

Evaluate fairness, explainability, and transparency using Azure’s Responsible AI tools.

Module 2: Perform Hyperparameter Tuning with Azure Machine Learning

Improving Model Performance Through Optimisation

Hyperparameter tuning is essential for maximising model accuracy and efficiency.

Topics Covered

Defining search spaces
Sampling methods
Early termination strategies
Sweep jobs for automated optimisation

Learners discover how to automate the search for optimal model configurations while minimising computational costs.

Module 3: Run Pipelines in Azure Machine Learning

Automating Repeatable AI Workflows

Production machine learning requires consistency and automation.

Learning Objectives

Create Components

Build reusable processing and training components.

Build Pipelines

Connect components into structured workflows.

Execute Pipeline Jobs

Run automated workflows that support scalable model development and deployment.

Pipeline automation improves collaboration, reproducibility, and operational efficiency.

Module 4: Trigger Azure Machine Learning Jobs with GitHub Actions

Bringing DevOps Principles to Machine Learning

This module introduces CI/CD concepts for machine learning workloads.

Participants learn how GitHub Actions can automatically trigger training workflows whenever changes are committed to source repositories.

Benefits Include

Reduced manual effort
Faster deployment cycles
Improved consistency
Enhanced governance

Module 5: Trigger GitHub Actions with Feature-Based Development

Supporting Collaborative Development

Modern AI projects require collaboration between multiple teams.

This module demonstrates how feature branching strategies can trigger automated workflows while maintaining code quality and deployment stability.

Topics include:

Feature branches
Pull requests
Workflow automation
Development lifecycle management

Module 6: Work with Environments in GitHub Actions

Managing Deployment Environments

Environment management is critical for safe AI deployments.

Participants learn how to configure:

Development environments
Testing environments
Staging environments
Production environments

The module introduces deployment approvals, environment-specific configurations, and governance controls.

Module 7: Deploy a Model with GitHub Actions

Automating Production Deployments

Once models are validated, they must be deployed reliably.

This module focuses on:

Automated deployment pipelines
Deployment validation
Release management
Continuous delivery practices

Learners gain practical experience deploying machine learning models directly from GitHub workflows.

Module 8: Plan and Prepare a GenAIOps Solution

Understanding Generative AI Operations

Generative AI introduces unique operational challenges that require specialised management approaches.

Topics Covered

Identifying Use Cases

Explore business applications for generative AI solutions.

Model Selection

Understand how to choose the right foundation model for specific requirements.

Lifecycle Management

Learn the stages involved in developing and operating language model applications.

GenAIOps Frameworks

Review tools and frameworks that support enterprise-scale implementation.

Module 9: Manage Prompts for Agents in Microsoft Foundry with GitHub

Applying Version Control to Prompt Engineering

Prompt management is becoming as important as source code management.

Learners explore:

Prompt versioning
Git-based prompt management
Repository organisation
Safe deployment practices

This module helps organisations maintain consistency, traceability, and governance for AI agents.

Module 10: Evaluate and Optimise AI Agents Through Structured Experiments

Measuring AI Agent Performance

Generative AI applications require rigorous evaluation.

Participants learn how to:

Design evaluation experiments
Create testing methodologies
Apply scoring rubrics
Compare optimisation strategies

Structured experimentation enables organisations to improve response quality and user satisfaction.

Module 11: Automate AI Evaluations with Microsoft Foundry and GitHub Actions

Embedding Quality Assurance into GenAIOps

Manual testing is not scalable for enterprise AI applications.

This module demonstrates how to automate evaluations using:

Python
Microsoft Foundry
GitHub Actions
Evaluation datasets

Learners discover how continuous evaluation supports ongoing quality improvements.

Module 12: Monitor Your Generative AI Application

Ensuring Reliability in Production

Monitoring provides visibility into application health and performance.

Key Monitoring Areas

Usage Metrics

Track adoption and utilisation patterns.

Performance Metrics

Measure latency and response times.

Quality Metrics

Evaluate output relevance and effectiveness.

Cost Metrics

Monitor resource consumption and operational expenditure.

The module explores Azure monitoring services and integration techniques.

Module 13: Analyse and Debug Your Generative AI Application with Tracing

Gaining Deep Operational Visibility

Tracing provides detailed insights into complex AI workflows.

Participants learn:

What should be traced
How tracing improves debugging
Advanced tracing techniques
Root-cause analysis
Performance optimisation

Tracing enables teams to identify issues quickly and improve application reliability.

Skills Gained from AI-300T00

After completing this course, learners will be able to:

Implement MLOps Solutions

Automate machine learning workflows
Manage model lifecycles
Deploy scalable AI systems

Build GenAIOps Frameworks

Deploy AI agents
Manage prompts
Evaluate generative AI performance

Create CI/CD Pipelines

Configure GitHub Actions
Automate testing and deployment
Support continuous improvement

Monitor Production AI Systems

Track performance metrics
Analyse operational data
Optimise reliability

Apply Responsible AI Practices

Evaluate fairness
Improve transparency
Enhance governance

Benefits for Organisations

Organisations that invest in AI-300T00 training gain significant advantages.

Faster AI Deployments

Automated pipelines reduce time-to-production.

Improved Reliability

Monitoring and observability improve operational stability.

Better Governance

Version control and automation enhance compliance.

Enhanced Collaboration

Data science, engineering, and DevOps teams work more effectively together.

Reduced Operational Risk

Structured processes minimise deployment failures and model drift.

Why Learn MLOps and GenAIOps Now?

Demand for AI professionals continues to grow rapidly.

As organisations move beyond proof-of-concept projects, operational expertise is becoming one of the most valuable skill sets in the AI industry.

Professionals with MLOps and GenAIOps capabilities are increasingly sought after because they can bridge the gap between innovation and production.

The AI-300T00 course provides practical, hands-on experience with the tools and methodologies organisations are actively adopting today.

Frequently Asked Questions

Is AI-300T00 suitable for beginners?

A basic understanding of Azure, machine learning concepts, and software development practices is recommended. The course is best suited to technical professionals seeking operational AI skills.

What is the difference between MLOps and GenAIOps?

MLOps focuses on operationalising traditional machine learning models, while GenAIOps addresses the deployment, evaluation, monitoring, and optimisation of generative AI applications and agents.

Does the course include hands-on labs?

Yes. Learners work directly with Azure Machine Learning, Microsoft Foundry, GitHub Actions, MLflow, Azure CLI, and Bicep throughout the course.

How long is the AI-300T00 course?

The course is delivered over four days and combines instructor-led learning with practical exercises.

What Azure services are covered?

Key services include Azure Machine Learning, Azure Monitoring tools, Azure CLI, Microsoft Foundry, GitHub Actions, and Infrastructure as Code solutions using Bicep.

Is AI-300T00 relevant for AI engineers?

Yes. The course is specifically designed for AI engineers, machine learning engineers, DevOps professionals, data scientists, and cloud architects responsible for production AI systems.

Ready to book your place?

The AI-300T00: Operationalize Machine Learning and Generative AI Solutions course equips professionals with the skills needed to deploy, manage, monitor, and optimise modern AI workloads on Microsoft Azure.

Book your place now >

From machine learning experimentation and automated model training to prompt management, AI agent evaluation, monitoring, and tracing, the course delivers comprehensive coverage of both MLOps and GenAIOps best practices.

As organisations increasingly rely on AI to drive innovation and competitive advantage, professionals who can operationalise these systems effectively will play a critical role in ensuring success. AI-300T00 provides the practical knowledge and hands-on experience required to build reliable, scalable, and production-ready AI solutions in today’s rapidly evolving technology landscape.

For more information, or for course booking support, please contact us book an appointment below.