Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300)

70 Lessons

40 hours

Microsoft AI-300 Training is designed for professionals who want to build and operate production-ready machine learning and generative AI solutions on Azure. The training covers topics like MLOps and GenAIOps, including machine learning model lifecycle management with Azure Machine Learning, AI infrastructure, automation, deployment, evaluation, monitoring, and optimization of generative AI applications and agents using Microsoft Foundry. It also helps build the practical skills covered by the Microsoft Certified: Machine Learning Operations Engineer Associate certification and prepare for the AI-300 exam.

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Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300)

About Course

Prerequisites

Basic Python programming skills
Foundational machine learning knowledge
Experience with Azure Machine Learning
Experience with Microsoft Foundry
Understanding of the machine learning lifecycle
Basic DevOps and CI/CD knowledge
Familiarity with GitHub Actions
Basic Azure CLI knowledge
Familiarity with Bicep and Infrastructure as Code (IaC)
Basic understanding of generative AI applications and agents

Who Should Enroll?

Machine Learning Engineers
Data Scientists
MLOps Engineers
AI Engineers
DevOps Professionals
Generative AI Developers
Professionals preparing for the AI-300 certification

What Will You Learn?

MLOps Infrastructure
Azure Machine Learning
ML Model Lifecycle Management
MLflow and Model Training
Model Deployment and Monitoring
GenAIOps with Microsoft Foundry
AI Model Selection and Versioning
Prompt Engineering
Generative AI Evaluation
AI Observability and Monitoring
RAG Optimization
Generative AI Fine-Tuning
Synthetic Data
Model Performance Optimization

Tools & Technologies

Azure Machine Learning
Microsoft Foundry
GitHub Actions
Azure CLI
Bicep
Git
Infrastructure as Code (IaC)
Machine Learning
Generative AI / GenAIOps

Course Curriculum

Course Content

Lesson 1 – Design and Implement an MLOps Infrastructure

Create and Manage Machine Learning Workspaces
Create and Manage Datastores
Create and Manage Compute Targets
Configure Identity and Access Management
Create and Manage Data Assets
Create and Manage Environments
Create and Manage Components
Share Assets Across Workspaces Using Registries
Configure GitHub Integration
Deploy Machine Learning Resources Using Bicep and Azure CLI
Automate Resource Provisioning with GitHub Actions
Restrict Network Access
Manage Source Control with Git

Lesson 2 – Implement Machine Learning Model Lifecycle and Operations

Configure MLflow Experiment Tracking
Use Automated Machine Learning
Use Notebooks for Experimentation
Automate Hyperparameter Tuning
Run Model Training Scripts
Manage Distributed Training
Implement Training Pipelines
Compare Model Performance Across Jobs
Register MLflow Models
Manage Model Lifecycle and Versions
Evaluate Models Using Responsible AI Principles
Deploy Models to Real-Time Endpoints
Deploy Models to Batch Endpoints
Test and Troubleshoot Model Endpoints
Implement Progressive Rollouts and Safe Rollbacks
Detect and Analyze Data Drift
Monitor Model Performance Metrics
Configure Retraining and Alert Triggers

Lesson 3 – Design and Implement a GenAIOps Infrastructure

Create and Configure Microsoft Foundry Resources and Projects
Configure Managed Identity and Role-Based Access Control (RBAC)
Implement Network Security and Private Networking
Deploy Infrastructure Using Bicep and Azure CLI
Deploy Foundation Models Using Serverless APIs
Deploy Foundation Models Using Managed Compute
Select Models for Specific Use Cases
Implement Model Versioning
Implement Production Deployment Strategies
Configure Provisioned Throughput Units
Design and Develop Prompts
Create Prompt Variants
Compare Prompt Performance
Implement Prompt Version Control with Git

Lesson 4 – Implement Generative AI Quality Assurance and Observability

Create Test Datasets and Data Mapping
Apply AI Quality Metrics
Evaluate Groundedness
Evaluate Relevance
Evaluate Coherence
Evaluate Fluency
Configure Risk and Safety Evaluations
Configure Automated Evaluation Workflows
Implement Continuous Monitoring in Microsoft Foundry
Monitor Latency, Throughput, and Response Time
Track Token Consumption and Resource Usage
Optimize Cost Metrics

Lesson 5 – Optimize Generative AI Systems and Model Performance

Optimize RAG Performance and Accuracy
Tune Similarity Thresholds
Optimize Chunk Size
Optimize Retrieval Strategies
Select and Fine-Tune Embedding Models
Implement Hybrid Search
Evaluate RAG Performance
Use Relevance Metrics
Implement A/B Testing
Design and Implement Advanced Fine-Tuning
Create and Manage Synthetic Data
Monitor and Optimize Fine-Tuned Model Performance
Manage Fine-Tuned Models Through Production Deployment

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Self Paced Learning

₹47,940.00

✓ Refund Policy

Duration: 40 hrs
70 Lessons & Practical Labs
Lifetime Full Access & Free Upgrades
Downloadable Study Materials & Code Labs
Recognized Certification of Completion
24x7 Online Support & Learner Forum

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Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) Certification Exam

Upon completing the Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300), you will receive a globally recognized certification that validates your expertise in data science, machine learning, and AI model development. This certification is a testament to your practical knowledge, hands-on skills, and professional readiness.

The AI & Data Science certification exam assesses your ability to apply real-world concepts, tools, and techniques learned during the course. Certified professionals are in high demand across industries, opening doors to exciting career opportunities and higher salary potential.

Our certification is recognized by top employers and organizations worldwide. Whether you are looking to advance your current career, switch to a new role, or demonstrate your expertise to clients, this certification gives you the competitive edge you need in today’s fast-paced technology landscape.

Frequently Asked Questions

The Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) is designed to provide comprehensive, in-depth training in Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300). Prerequisites Basic Python programming skills Foundational machine learning knowledge Experience with Azure Machine Learning Experience with Microsoft Foundry Understanding of the machine learning lifecycle Basic DevOps and CI/CD knowledge Familiarity with GitHub Actions Basic Azure CLI knowledge Familiarity with Bicep and Infrastructure as Code (IaC) Basic understanding of generative AI applications and agents Who Should Enroll? The program covers foundational concepts through to advanced techniques to ensure you gain job-ready expertise.

This course is ideal for beginners, IT enthusiasts, and working professionals who want to build or advance their career in Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) and qualify for engineer positions. There are no stringent prerequisites—basic computer proficiency and a willingness to learn are all that is required. Foundational topics are thoroughly covered during onboarding.

The course is delivered through interactive, live online sessions led by industry experts. You will also have 24/7 access to LMS resources, study materials, and session recordings so you can catch up or revise at your convenience if you miss any live classes.

Yes, hands-on learning is a core component of this course. You will work on practical assignments, real-life use cases, and capstone projects using Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) tools and best practices, helping you build a portfolio to showcase to hiring managers.

Upon successful completion of the course curriculum and project assessments, you will be awarded an industry-recognized Certificate of Completion from Kodestree. We also offer career assistance, including resume-building workshops, mock technical interviews, and job opportunities to help you transition into engineer positions.

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Call:
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WhatsApp:
+91 7204614489

Email:
admissions@kodestree.com

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Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300)

About Course

Prerequisites

Who Should Enroll?

What Will You Learn?

Tools & Technologies

Course Curriculum

Course Content

Lesson 1 – Design and Implement an MLOps Infrastructure

Create and Manage Machine Learning Workspaces

Create and Manage Datastores

Create and Manage Compute Targets

Configure Identity and Access Management

Create and Manage Data Assets

Create and Manage Environments

Create and Manage Components

Share Assets Across Workspaces Using Registries

Configure GitHub Integration

Deploy Machine Learning Resources Using Bicep and Azure CLI

Automate Resource Provisioning with GitHub Actions

Restrict Network Access

Manage Source Control with Git

Lesson 2 – Implement Machine Learning Model Lifecycle and Operations

Configure MLflow Experiment Tracking

Use Automated Machine Learning

Use Notebooks for Experimentation

Automate Hyperparameter Tuning

Run Model Training Scripts

Manage Distributed Training

Implement Training Pipelines

Compare Model Performance Across Jobs

Register MLflow Models

Manage Model Lifecycle and Versions

Evaluate Models Using Responsible AI Principles

Deploy Models to Real-Time Endpoints

Deploy Models to Batch Endpoints

Test and Troubleshoot Model Endpoints

Implement Progressive Rollouts and Safe Rollbacks

Detect and Analyze Data Drift

Monitor Model Performance Metrics

Configure Retraining and Alert Triggers

Lesson 3 – Design and Implement a GenAIOps Infrastructure

Create and Configure Microsoft Foundry Resources and Projects

Configure Managed Identity and Role-Based Access Control (RBAC)

Implement Network Security and Private Networking

Deploy Infrastructure Using Bicep and Azure CLI

Deploy Foundation Models Using Serverless APIs

Deploy Foundation Models Using Managed Compute

Select Models for Specific Use Cases

Implement Model Versioning

Implement Production Deployment Strategies

Configure Provisioned Throughput Units

Design and Develop Prompts

Create Prompt Variants

Compare Prompt Performance

Implement Prompt Version Control with Git

Lesson 4 – Implement Generative AI Quality Assurance and Observability

Create Test Datasets and Data Mapping

Apply AI Quality Metrics

Evaluate Groundedness

Evaluate Relevance

Evaluate Coherence

Evaluate Fluency

Configure Risk and Safety Evaluations

Configure Automated Evaluation Workflows

Implement Continuous Monitoring in Microsoft Foundry

Monitor Latency, Throughput, and Response Time

Track Token Consumption and Resource Usage

Optimize Cost Metrics

Lesson 5 – Optimize Generative AI Systems and Model Performance

Optimize RAG Performance and Accuracy

Tune Similarity Thresholds

Optimize Chunk Size

Optimize Retrieval Strategies

Select and Fine-Tune Embedding Models

Implement Hybrid Search

Evaluate RAG Performance

Use Relevance Metrics

Implement A/B Testing

Design and Implement Advanced Fine-Tuning