AI-300 Operationalize Machine Learning and Generative AI Solutions - ITEL

AI-300 Operationalize Machine Learning and Generative AI Solutions

AI & IoT

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AI-300 Operationalize Machine Learning and Generative AI Solutions

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Course Overview

This course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by 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 AI systems aligned with modern MLOps and GenAIOps best practices.

Learning Outcomes
Course Prerequisite
Who Should Attend?
Course Outline
Course Fee

Learning Outcomes

This course will equip cybersecurity professionals with the knowledge, skills, and abilities to lead an organization’s information security program.

Who Should Attend?

This course is intended for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure. It is suited for learners with experience in Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using Azure-native services.

Course Prerequisite

  • Experience in Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools

Potential Roles

CISO, Security Architect, Security Manager, Security Consultant, and Security Auditor

Course Outline

Lesson 1: Operationalize machine learning models (MLOps)
  • Experiment with Azure Machine Learning
    • Introduction
    • Preprocess data and configure featurization
    • Run an automated machine learning experiment
    • Evaluate and compare models
    • Configure MLflow for model tracking in notebooks
    • Train and track models in notebooks
    • Evaluate models with the Responsible AI dashboard
    • Exercise – Find the best classification model with Azure Machine Learning
  • Perform hyperparameter tuning with Azure Machine Learning
    • Introduction
    • Define a search space
    • Configure a sampling method
    • Configure early termination
    • Use a sweep job for hyperparameter tuning
    • Exercise – Run a sweep job
  • Run pipelines in Azure Machine Learning
    • Introduction
    • Create components
    • Create a pipeline
    • Run a pipeline job
    • Exercise – Run a pipeline job
  • Trigger Azure Machine Learning jobs with GitHub Actions
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Use GitHub Actions for model training
    • Exercise
  • Trigger GitHub Actions with feature-based development
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Trigger a workflow
    • Exercise
  • Work with environments in GitHub Actions
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Set up environments
    • Exercise
  • Deploy a model with GitHub Actions
    • Introduction
    • Understand the business problem
    • Explore the solution architecture
    • Model deployment
    • Exercise
Lesson 2: Operationalize generative AI applications (GenAIOps)
  • Plan and prepare a GenAIOps solution
    • Introduction
    • Explore use cases for GenAIOps
    • Select the right generative AI model
    • Understand the development lifecycle of a language model application
    • Explore available tools and frameworks to implement GenAIOps
    • Exercise – Compare language models from the model catalog
  • Manage prompts for agents in Microsoft Foundry with GitHub
    • Introduction
    • Apply version control to prompts
    • Understand Microsoft Foundry agents and prompt versioning
    • Organize prompts in GitHub repositories
    • Develop safe prompt deployment workflows
    • Exercise – Develop prompt and agent versions
  • Evaluate and optimize AI agents through structured experiments
    • Introduction
    • Design evaluation experiments
    • Apply Git-based workflows to optimization experiments
    • Apply evaluation rubrics for consistent scoring
    • Exercise – Evaluate and compare AI agent versions
  • Automate AI evaluations with Microsoft Foundry and GitHub Actions
    • Introduction
    • Understand why automated evaluations matter
    • Align evaluators with human criteria
    • Create evaluation datasets
    • Implement batch evaluations with Python
    • Integrate evaluations into GitHub Actions
    • Exercise – Set up automated evaluations
  • Monitor your generative AI application
    • Introduction
    • Why do you need to monitor?
    • Understand key metrics to monitor
    • Explore how to monitor with Azure
    • Integrate monitoring into your app
    • Interpret monitoring results
    • Exercise – Enable monitoring for a generative AI application
  • Analyze and debug your generative AI app with tracing
    • Introduction
    • Why do you need to use tracing?
    • Identify what to trace in generative AI applications
    • Implement tracing in generative AI applications
    • Debug complex workflows with advanced tracing patterns
    • Make informed decisions with trace data analysis
    • Exercise – Enable tracing for a generative AI application

Course Fee (inclusive of 9% GST)

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Individual
Criteria Price inclusive of GST

Full Course Fee

$3,270.00

Singapore Citizens (SCs) and Permanent Residents (PRs)
Funding Up to 50% of course fees

$981.00

SCs aged ≥ 40 years old ( SkillsFuture Mid-career Enhanced Subsidy )
Funding Up to 70% of course fees

$381.00
Criteria Price inclusive of GST

Full Course Fee

$3,270.00

Singapore Citizens (SCs) and Permanent Residents (PRs)
Funding Up to 50% of course fees

$981.00

SCs aged ≥ 40 years old ( SkillsFuture Mid-career Enhanced Subsidy )
Funding Up to 70% of course fees

$381.00
Criteria Price inclusive of GST

Full Course Fee

$3,270.00

Singapore Citizens (SCs) and Permanent Residents (PRs)
Funding Up to 50% of course fees

$381.00

SCs aged ≥ 40 years old ( SkillsFuture Mid-career Enhanced Subsidy )
Funding Up to 70% of course fees

$381.00

Course Dates

Course Dates Mode of Delivery Duration

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Certification

Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion by ITEL

Course Code: TGS-2023021373
Course Support Period: 19 May 2023 – 31 Jan 2027

Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of ITEL.

Learner's Testimonials

"The CEH materials provided are very useful."
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AI-300 Operationalize Machine Learning and Generative AI Solutions

Instructor-led Training (ILT)

4 Days

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