Azure Machine Learning MLOps Training (AI-300)

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Azure Machine Learning MLOps Training (AI-300)

Azure Machine Learning MLOps Training (AI-300)

Azure Machine Learning MLOps Training (AI-300) is 4‑day instructor-led course empowers learners to design, implement, and manage complex Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions using Azure. The AI‑300: Operationalizing Machine Learning and Generative AI Solutions training is a cutting-edge Azure Machine Learning MLOps Training program designed for professionals who want to move beyond experimentation and deliver production‑grade AI systems at scale. The course emphasizes real-world implementation, focusing on automation, CI/CD pipelines, infrastructure-as-code, and observability using tools like GitHub Actions, Azure CLI, and Bicep. You will learn how to collaborate effectively with data science and DevOps teams to deliver secure, scalable, and production-ready AI solutions aligned with modern MLOps practices.


The Azure Machine Learning MLOps Training (AI-300) curriculum is aligned with the Microsoft AI‑300 certification and focuses on real-world scenarios including model lifecycle management, monitoring, governance, and performance optimization. By completing this training, participants will be able to:


  • Design and implement complex MLOps pipelines using Azure Machine Learning
  • Automate model lifecycle using CI/CD and infrastructure-as-code practices
  • Deploy, monitor, and optimize generative AI applications and agents
  • Implement scalable and secure AI infrastructure for enterprise workloads
  • Collaborate across Data Science and DevOps teams to operationalize AI solutions effectively
Advance Your Skills with Flexmind (Microsoft Partner)

Who should attend the AI-300: Operationalizing Machine Learning and Generative AI Solutions course ?

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For Professionals

This course is best suited for professionals responsible for building, deploying, and operating production‑grade AI systems on Azure. Ideal job roles include AI Engineers, Azure AI Engineers, Machine Learning Engineers, DevOps Engineers, Platform Engineers.

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For Businesses

Organizations should nominate professionals who are responsible for taking AI solutions from prototype to production and ensuring they scale securely and reliably. Recommended roles to nominate AI / ML Engineers, Data Scientists, DevOps Engineers, Solution Architects.

Prerequisites for the "AI-300: Operationalizing Machine Learning and Generative AI Solutions" Course

Before attending this course, students should have:


  • Familiarity with Python programming
  • A basic understanding of machine learning concepts
  • Experience with Azure services (Azure portal, subscriptions, resource groups)
  • Familiarity with GitHub and version control
  • Experience with Azure Machine Learning or Azure AI services is beneficial but not required.

Key Features of Flexmind AI-300: Operationalizing Machine Learning and Generative AI Solutions Training

This training is delivered by Flexmind through flexible online and offline formats and is designed to align with the most current certification exam requirements. The key features of this training are as follows:

4 Day · 32 Hours
Microsoft Certified Trainer
Microsoft Official Curriculum
Cloud Lab Access
Applied Workshop

Course Duration

The course has a total duration of 32 hours and is completed over 4 days.

Instructor-Led Training

Delivered by a senior Microsoft Certified Trainer with real-world, enterprise-scale experience in designing and developing Azure Machine Learning MLOps solutions in Microsoft Azure.

Microsoft Official Courseware

Delivered by Flexmind using official Microsoft courseware, this program blends study material, hands‑on labs, and applied workshops with instructor-led guidance throughout.

Applied Workshop

The final‑day applied workshop allows learners to practice their skills by selecting a scenario and building a complete solution, including any required automation.

Course Completion Certificate


Course completion includes certification, formally validating the skills gained and reinforcing professional credibility.

Course Outline - Azure Machine Learning MLOps Training (AI-300)

Module 1: Design a machine learning training solution

  • Understand the machine learning process
  • Design a data ingestion solution
  • Choose a service to train a machine learning model
  • Decide between compute options
  • Decide on real-time or batch deployment
  • Case study – Design an ML solution

Module 2: Experiment with Azure Machine Learning

  • Explore model training options
  • Explore Automated Machine Learning
  • Understand scaling and normalization
  • Run an Automated Machine Learning experiment
  • Configure an AutoML experiment
  • Evaluate and compare models

Module 3: Optimize model training with Azure Machine Learning

  • Convert a notebook to a script
  • Configure a command job

Module 4: Perform hyperparameter tuning with Azure Machine Learning

  • Understand hyperparameter tuning
  • Define a search space
  • Configure a sampling method
  • Configure an early termination policy - Bandit policy, Median stopping policy, Truncation selection policy
  • Lab Exercise – Perform hyperparameter tuning with a sweep job

Module 5: Run pipelines in Azure Machine Learning

  • Create a component
  • Register a component
  • Create a pipeline
  • Run a pipeline job
  • Lab Exercise – Run pipelines in Azure Machine Learning

Module 6: Plan and prepare an MLOps solution with Azure Machine Learning

  • Design an MLOps architecture
  • Monitor the model

Module 7: Automate model training with GitHub Actions

  • Enable source control & integrate GitHub with Azure ML
  • Trunk-based development workflow
  • Trigger AML jobs with GitHub Actions

Module 8: Register an MLflow model in Azure Machine Learning

  • Log a model with MLflow
  • Understand the MLmodel file format
  • Deploy a model to a batch endpoint
  • Deploy a model to a managed online endpoint
  • Lab Exercise – Deploy and monitor a model in Azure Machine Learning

Module 9: Plan and prepare a GenAIOps solution

  • Define agent specifications
  • Navigate the GenAIOps lifecycle
  • Identify tools across the GenAIOps lifecycle
  • Lab Exercise – Plan and prepare a GenAIOps solution

Module 10: Manage prompts for agents in Microsoft Foundry with GitHub

  • Version control for prompts
  • Understand Foundry agents and prompt versioning
  • GitHub repository structure
  • Lab Exercise – Develop prompt and agent versions

Module 11: Evaluate and optimize AI agents through structured experiments

  • Design evaluation experiments
  • Git-based experimentation workflow
  • Evaluate agent responses consistently
  • Lab Exercise – Design and optimize prompts

Module 12: Automate AI evaluations with Microsoft Foundry and GitHub Actions

  • Understand why automated evaluations matter
  • Design human-in-the-loop (HITL) for your workflow
  • Align evaluators with human criteria
  • Lab Exercise – Automated evaluation with cloud evaluators

Module 13: Implement observability and monitoring for generative AI workloads

  • Explore monitoring options
  • Complement monitoring with tracing
  • Implement monitoring
  • Lab Exercise – Monitor and trace your generative AI agent

Module 14: Optimize and fine-tune AI agents for production

  • Select a fine-tuning method
  • Validate data format for SFT
  • Validate data format for RFT
  • Validate data format for DPO
  • Lab Exercise – Optimize AI agents with fine-tuning
Class Schedule

Instructor‑Led Training

  • 32 Hours of Instructor‑Led Training
  • One‑to‑one doubt‑resolution sessions
  • Microsoft Official Lab Access

Learning Objectives - Azure Machine Learning MLOps Training (AI-300)

After completing the AI-300 course, learners will be able to:

  • Design and implement scalable MLOps and GenAIOps solutions on Azure for production environments
  • Automate AI workflows using CI/CD, infrastructure-as-code, and Azure-native tools
  • Deploy, monitor, and optimize machine learning models and generative AI applications at scale
  • Build and manage end-to-end AI lifecycle pipelines including data, models, and environments
  • Implement observability, governance, and responsible AI practices for reliable AI systems

About AI-300 Certification Exam

To help you understand the assessment better, here are a few important details about the exam.


Exam Name AI-300: Operationalizing Machine Learning and Generative AI Solutions
Who should Apply AI Engineer
Duration of Exam 100 Minutes
Fees Rs. 4,865 (India), $165 USD (United States)
Level of Difficulty Intermediate
Type of Credential Microsoft Certification
Languages English, Japanese, Chinese (Simplified), German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia)
Exam Retake Exam retake allowed after 24 hours
Quality Check during Assessment The online exam is proctored

The table below represents the weightage of each study area in the exam. Areas with higher percentages are expected to have more questions.

Study Area Percentage
Design and implement an MLOps infrastructure 15-20%
Implement machine learning model lifecycle and operations 25-30%
Design and implement a GenAIOps infrastructure 20-25%
Implement generative AI quality assurance and observability 10-15%
Optimize generative AI systems and model performance 10-15%

FAQ's About Azure Machine Learning MLOps Training (AI-300) Course

This course focuses on designing, implementing, and operating production‑grade AI solutions using MLOps and GenAIOps practices on Azure. It covers the full lifecycle of machine learning and generative AI applications, including deployment, monitoring, automation, and optimization.

This course is ideal for AI Engineers, Machine Learning Engineers, Data Scientists transitioning to production roles, and DevOps professionals who want to operationalize AI solutions and build scalable, enterprise-ready machine learning and generative AI systems.

Participants should have experience with Python, a foundational understanding of machine learning concepts, and basic knowledge of DevOps practices such as CI/CD, source control, and command-line tools. Familiarity with Azure Machine Learning is recommended.

The course covers designing MLOps and GenAIOps solutions, automating workflows using CI/CD and infrastructure as code, managing the machine learning lifecycle, deploying and monitoring AI models, and optimizing generative AI applications using Azure AI technologies.

Yes, the training is highly hands-on and includes practical labs where participants implement CI/CD pipelines, deploy AI models, monitor performance, and work with generative AI workflows in real-world scenarios.

Yes, this training is fully aligned with the AI‑300 certification objectives and prepares you for the Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate credential, covering all required skills for operationalizing AI systems on Azure.

This course is delivered by a Microsoft Certified Trainer (MCT) with extensive experience in Azure Machine Learning, MLOps, and enterprise AI implementation, ensuring a practical and industry-relevant learning experience.

AI‑300 emphasizes operationalizing AI solutions covering deployment, automation, monitoring, governance, and scalability in real-world production environments.

You will gain hands-on experience with Azure Machine Learning, Azure AI Foundry, GitHub Actions, MLflow, infrastructure-as-code tools like Bicep and Azure CLI, and monitoring and observability tools used in enterprise AI systems.

By the end of the course, you will be able to design, deploy, automate, monitor, and optimize machine learning and generative AI solutions at scale using MLOps and GenAIOps practices, ensuring reliability, scalability, and business value.

The course is delivered over 4 days as instructor-led training (ILT), combining conceptual learning, guided demonstrations, and hands-on labs led by an experienced MCT.

Yes, this course is fully aligned with the AI‑300 certification objectives. It prepares participants to design, implement, and operate machine learning and generative AI solutions using MLOps and GenAIOps practices on Azure.

After completing this course and successfully passing the exam, you will earn the Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate certification, validating your ability to operationalize AI solutions on Azure.

Yes, the course covers all major exam domains, including MLOps infrastructure, machine learning lifecycle management, GenAIOps workflows, monitoring and observability, and optimization of AI systems in production.

The AI‑300 certification demonstrates your ability to operationalize AI solutions in enterprise environments, making you highly valuable for roles such as AI Engineer, MLOps Engineer, and Cloud AI Architect responsible for delivering production-ready AI systems.

The AI‑300 exam is an intermediate-level certification that evaluates real-world, scenario-based skills such as deploying, automating, monitoring, and optimizing AI solutions on Azure. It focuses more on practical implementation than theoretical concepts.

Yes, it is recommended to have hands-on experience with machine learning, Azure services, and basic DevOps practices such as CI/CD and automation to successfully clear the certification.

AI‑300 focuses on operationalizing AI systems (MLOps & GenAIOps), while DP‑100 focuses on data science and model development. AI‑300 is more aligned with production, automation, and enterprise-scale deployment.
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