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

SKU: 3923
5 Lesson
|
30 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

Overview

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

Key Features

Course Curriculum

1. Create and Manage Machine Learning Workspaces
2. Create and Manage Datastores
3. Create and Manage Compute Targets
4. Configure Identity and Access Management
5. Create and Manage Data Assets
6. Create and Manage Environments
7. Create and Manage Components
8. Share Assets Across Workspaces Using Registries
9. Configure GitHub Integration
10. Deploy Machine Learning Resources Using Bicep and Azure CLI
11. Automate Resource Provisioning with GitHub Actions
12. Restrict Network Access
13. Manage Source Control with Git
1. Configure MLflow Experiment Tracking
2. Use Automated Machine Learning
3. Use Notebooks for Experimentation
4. Automate Hyperparameter Tuning
5. Run Model Training Scripts
6. Manage Distributed Training
7. Implement Training Pipelines
8. Compare Model Performance Across Jobs
9. Register MLflow Models
10. Manage Model Lifecycle and Versions
11. Evaluate Models Using Responsible AI Principles
12. Deploy Models to Real-Time Endpoints
13. Deploy Models to Batch Endpoints
14. Test and Troubleshoot Model Endpoints
15. Implement Progressive Rollouts and Safe Rollbacks
16. Detect and Analyze Data Drift
17. Monitor Model Performance Metrics
18. Configure Retraining and Alert Triggers
1. Create and Configure Microsoft Foundry Resources and Projects
2. Configure Managed Identity and Role-Based Access Control (RBAC)
3. Implement Network Security and Private Networking
4. Deploy Infrastructure Using Bicep and Azure CLI
5. Deploy Foundation Models Using Serverless APIs
6. Deploy Foundation Models Using Managed Compute
7. Select Models for Specific Use Cases
8. Implement Model Versioning
9. Implement Production Deployment Strategies
10. Configure Provisioned Throughput Units
11. Design and Develop Prompts
12. Create Prompt Variants
13. Compare Prompt Performance
14. Implement Prompt Version Control with Git
1. Create Test Datasets and Data Mapping
2. Apply AI Quality Metrics
3. Evaluate Groundedness
4. Evaluate Relevance
5. Evaluate Coherence
6. Evaluate Fluency
7. Configure Risk and Safety Evaluations
8. Configure Automated Evaluation Workflows
9. Implement Continuous Monitoring in Microsoft Foundry
10. Monitor Latency, Throughput, and Response Time
11. Track Token Consumption and Resource Usage
12. Optimize Cost Metrics
1. Optimize RAG Performance and Accuracy
2. Tune Similarity Thresholds
3. Optimize Chunk Size
4. Optimize Retrieval Strategies
5. Select and Fine-Tune Embedding Models
6. Implement Hybrid Search
7. Evaluate RAG Performance
8. Use Relevance Metrics
9. Implement A/B Testing
10. Design and Implement Advanced Fine-Tuning
11. Create and Manage Synthetic Data
12. Monitor and Optimize Fine-Tuned Model Performance
13. Manage Fine-Tuned Models Through Production Deployment
Talk To Us

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1-800-7430-173 (US Toll Free)
+91 7240740740 (India)
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Course Fees

Online Class Room Program

US $ 799.00
100% Money Back Guarantee
  • Duration : 30 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 02 Sep 2026
  • Weekday Batch 07 Sep 2026
  • Weekend Batch 05 Sep 2026

1 ON 1 Training

US $ 899.00
100% Money Back Guarantee
  • Duration : 30 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 02 Sep 2026
  • Weekday Batch 07 Sep 2026
  • Weekend Batch 05 Sep 2026

Corporate Training

Corporate Training
  • Customized Training Delivery Model
  • Flexible Training Schedule Options
  • Industry Experienced Trainers
  • 24x7 Support
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Microsoft AI-300 Certification

After completing the training, you will receive an igmGuru Course Completion Certificate. The course also prepares you for the AI-300 certification exam. Upon successfully passing the AI-300 exam, learners can earn the Microsoft Certified: Machine Learning Operations Engineer Associate certification. The certification validates skills in operationalizing machine learning and generative AI solutions on Azure, including MLOps, GenAIOps, AI quality assurance, observability and AI system optimization.

  • Official Certification: Microsoft Certified: Machine Learning Operations Engineer Associate
  • Exam: AI-300
  • Exam Title: Operationalizing Machine Learning and Generative AI Solutions
  • Level: Intermediate
  • Exam Duration: 120 minutes
  • Exam Language: English
  • Passing Score: 700 or higher
  • Exam Delivery: Proctored
  • Certification Validity: 1 year
  • Renewal: Free online renewal assessment through Microsoft Learn
Microsoft AI-300 Certification

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