“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Microsoft Official Curriculum
Role-Based Certification PrepTrack: AI-300T00Official Source: Microsoft Learn
MicrosoftIntermediate
Operationalize machine learning and generative AI solutions
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.
Duration
4 days
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Microsoft
AIIT
Operationalize machine learning and generative AI solutions
Azure
AI
On this page
Ideal for
AI EngineerAICertification ReadinessTailored Team Delivery
How VNode delivers this
Who this is for, and how we run it
VNode ITeS delivers Operationalize machine learning and generative AI solutions as an MCT-led Microsoft program for Ai Engineer, Data Scientist. Typical duration is 12 days (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Certification prep maps to AI-300 without turning the week into a dump of Learn modules. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
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.
Overview
Executive 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.
Readiness
Prerequisites
- Relevant foundational experience in the target technology area.
- Comfort with hands-on labs in a cloud or GPU-accelerated environment.
Program Outcomes
Capabilities your teams will gain
Strengthen capability in ai scenarios
Strengthen capability in azure scenarios
Strengthen capability in role-based scenarios
Curriculum
Curriculum roadmap
1
Operationalize machine learning models (MLOps)
2
Operationalize generative AI applications (GenAIOps)
1Module 1
Operationalize machine learning models (MLOps)
+
Module 1
Operationalize machine learning models (MLOps)
Learn how to design, train, optimize, automate, deploy, and monitor machine learning models with Azure Machine Learning and GitHub Actions.
- Get started with machine learning in Azure
- Experiment with Azure Machine Learning
- Run training scripts and track models with MLflow in Azure Machine Learning
- Perform hyperparameter tuning with Azure Machine Learning
- Run pipelines in Azure Machine Learning
- Design a machine learning operations solution (MLOps)
- Automate model training with GitHub Actions
- Deploy and monitor a model in Azure Machine Learning
2Module 2
Operationalize generative AI applications (GenAIOps)
+
Module 2
Operationalize generative AI applications (GenAIOps)
Learn the full GenAIOps lifecycle for generative AI applications, from planning and prompt management to evaluation, automated testing, monitoring, and tracing in production.
- Plan and prepare a GenAIOps solution
- Manage prompts for agents in Microsoft Foundry with GitHub
- Evaluate and optimize AI agents through structured experiments
- Automate AI evaluations with Microsoft Foundry and GitHub Actions
- Monitor your generative AI application
- Analyze and debug your generative AI app with tracing
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Certification readinessImplementation-focused labsPrivate cohort deliveryIntermediate practitioner depth
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Supports more reliable production ML by building stronger MLOps discipline across teams.
- •Align labs to your production environment and platform priorities
- •Add focused exam-readiness reviews and instructor-led practice sessions
- •Extend into project-specific architecture or delivery coaching
Credentials
Certification & official source
- •AI-300T00
Aligned to the official Microsoft Learn course and learning path for this program.
View Official Microsoft Learn PageResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios