Overview
Build the skills needed to deploy, manage, monitor and optimise machine learning and generative AI solutions on Microsoft Azure. Educora’s AI-300 training covers Azure Machine Learning, Azure AI Foundry, MLOps, GenAIOps, model deployment, monitoring and AI governance, from development through production.
Hands-on labs cover machine learning infrastructure, automated pipelines, model deployment, prompt flows, AI agents, retrieval-augmented generation (RAG), evaluation, safety and performance optimisation. The programme supports preparation for AI-300; completing the training does not itself confer Microsoft certification.
Prerequisites
Participants should have:
- Basic Python Programming
- Machine Learning Fundamentals
- Cloud Computing Concepts
- REST APIs and Web Services
- Git & GitHub Fundamentals
Curriculum
Module 1: Introduction to MLOps & GenAIOps
Topics Covered
- Understanding MLOps
- MLOps vs DevOps
- Generative AI Operations (GenAIOps)
- Azure AI Ecosystem
- Azure Machine Learning Overview
- Azure AI Foundry Overview
- Enterprise AI Lifecycle
- AI Solution Architecture
Practical lab
- Azure ML Workspace Setup
- Exploring Azure AI Foundry
Module 2: Azure Machine Learning Foundations
Topics Covered
- Azure ML Workspaces
- Compute Instances & Clusters
- Data Assets & Datastores
- Environment Management
- Components and Registries
- Workspace Security
- Access Management (RBAC)
Practical lab
- Creating ML Infrastructure
- Registering Data Assets
- Configuring Compute Resources
Module 3: Machine Learning Lifecycle Management
Topics Covered
- Data Preparation
- Feature Engineering
- Model Training
- Automated Machine Learning (AutoML)
- Hyperparameter Tuning
- MLflow Tracking
- Experiment Management
- Model Registry
- Model Version Control
Practical lab
- Develop and Train ML Models
- Track Experiments with MLflow
- Register Models in Azure ML
Module 4: Building Production ML Pipelines
Topics Covered
- ML Pipelines
- Component Reusability
- Pipeline Orchestration
- Scheduled Training Pipelines
- Automated Retraining
- CI/CD for ML
- GitHub Actions Integration
- Azure DevOps Integration
Practical lab
- Build Automated ML Pipeline
- Configure CI/CD Workflow
Module 5: Deploying Machine Learning Solutions
Topics Covered
- Online Endpoints
- Batch Endpoints
- Real-Time Inference
- Endpoint Authentication
- Secure Deployments
- Traffic Management
- Canary Deployment
- Blue-Green Deployment
Practical lab
- Deploy Production Model
- Test and Validate Endpoints
Module 6: Monitoring & Model Governance
Topics Covered
- Model Monitoring
- Data Drift Detection
- Prediction Drift Analysis
- Alerting & Notifications
- Logging & Diagnostics
- Governance Policies
- Responsible AI Practices
Practical lab
- Configure Monitoring Dashboard
- Implement Drift Detection
Module 7: Generative AI Operations (GenAIOps)
Topics Covered
- Azure AI Foundry Projects
- Foundation Models
- Prompt Engineering
- Prompt Flow
- Prompt Evaluation
- Prompt Versioning
- Model Fine-Tuning
- AI Agent Development
Practical lab
- Create Prompt Flow Application
- Deploy AI Agents
Module 8: Retrieval-Augmented Generation (RAG)
Topics Covered
- Embeddings
- Vector Databases
- Knowledge Grounding
- Semantic Search
- Retrieval Strategies
- Enterprise Knowledge Assistants
- RAG Architecture Design
Practical lab
- Build Enterprise RAG Solution
- Implement Semantic Search
Module 9: AI Observability & Responsible AI
Topics Covered
- AI Evaluation Frameworks
- LLM Evaluation
- Hallucination Detection
- Content Safety
- Explainability
- Fairness & Transparency
- OpenTelemetry Integration
- AI Quality Assurance
Practical lab
- Evaluate AI Responses
- Implement AI Safety Controls
Module 10: Performance & Cost Optimization
Topics Covered
- Inference Optimization
- Latency Reduction
- Throughput Optimization
- Cost Monitoring
- Resource Optimization
- Fine-Tuning Strategies
- Scaling AI Workloads
Practical lab
- Optimize Production AI Deployment
Features
- Instructor-Led Live Training
- Hands-On Labs
- Real-World AI Projects
- Azure AI Foundry Exposure
- MLOps & GenAIOps Focus
- Industry Use Cases
- Certification Preparation
Course Fees
Virtual Instructor-Led Training
| One-to-One Private Batch | Public Batch |
|---|---|
| Fees : ₹90,000 + 18% GST | Fees : ₹20,000 + 18% GST |
| 2hrs : Mon - Fri | 2hrs : Mon - Fri |
| 4hrs : Sat - Sun | 4hrs : Sat - Sun |
FAQs
1Do you accept card or online payments?
Yes, we do.
2Are course fees refundable if I cancel my enrolment?
No.
3Do you provide placement support after training?
We provide placement support, but do not guarantee employment or placement.
4Do you offer corporate training?
Yes, we offer corporate training.
5How will I receive my certificate after completing the course?
Our team will provide a digital certificate after you complete the training.
6Can I see the trainer’s profile before enrolling?
Yes, you can.
7Can I request a demo session before enrolling?
Yes, you can.
