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Batch Date: July 27th @6:00AM

Faculty: Mr. Sekhar Reddy (15+ Yrs of Exp,.. & Real Time Expert)

Duration: 60 Days

Venue :
DURGA SOFTWARE SOLUTIONS,
Flat No : 202, 2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038

Ph.No: +91- 8885252627, 9246212143, 80 96 96 96 96

Syllabus:

Mastering AI300 - MLOPS

Module 1: Introduction to AI-300

• AI-300 overview and course expectations
• AI, Machine Learning, Generative AI overview
• MLOps vs GenAIOps
• End-to-end AI solution lifecycle
• Azure AI ecosystem overview

Module 2: Azure Machine Learning Fundamentals

• Azure Machine Learning workspace
• Compute instances and compute clusters
• Data assets and environments
• Jobs, experiments, models, and endpoints
• Basic Azure ML architecture

Module 3: Data Management for Machine Learning

• Datasets and data assets
• Data preparation and validation
• Feature engineering basics
• Data versioning concepts
• Governance and security considerations

Module 4: Machine Learning Model Training

• Classification and regression overview
• Training jobs in Azure ML
• Training scripts and environments
• Train/test split and evaluation concept
• Running experiments in Azure ML

Module 5: MLflow Experiment Tracking

• MLflow purpose and workflow
• Track metrics, parameters, and artifacts
• Compare model runs
• Reproducibility in ML experiments
• Selecting best model run

 Module 6: Hyperparameter Tuning

• Why hyperparameter tuning is required
• Sweep jobs overview
• Search spaces and sampling methods
• Early termination and optimization
• Selecting best-performing model

Module 7: Model Registry and Versioning

• Model registry concepts
• Registering trained models
• Model versions and model lineage
• Model lifecycle management
• Governance and approval considerations

Module 8: Deploy Machine Learning Models

• Online endpoints vs batch endpoints
• Inference basics
• Deployment configuration
• Testing deployed models
• Deployment strategy overview

Module 9: Model Monitoring

• Model performance monitoring
• Prediction and operational logs
• Data drift and quality monitoring concepts
• Alerts and retraining triggers
• Production model health checks

Module 10: MLOps Fundamentals

• CI/CD for machine learning
• Git and GitHub integration
• Automation for training and deployment
• Pipeline concepts
• MLOps best practices

Module 11: Generative AI Fundamentals

• Large language models overview
• Prompts, tokens, and context
• Generative AI use cases
• Responsible AI basics
• Limitations and risks of GenAI systems

Module 12: Microsoft Foundry Fundamentals

• Microsoft Foundry projects
• Model catalog overview
• Resources and deployments
• Security and access control concepts
• GenAI development lifecycle

Module 13: Prompt Engineering

• Prompt design basics
• System prompts and user prompts
• Prompt templates
• Prompt versioning
• Prompt testing and optimization

Module 14: AI Agents

• What is an AI agent?
• Agent architecture overview
• Tools, actions, and orchestration
• Agent workflow design
• Single-agent and multi-agent concepts

Module 15: Knowledge Bases and RAG

• Knowledge base concept
• Embeddings and vector search
• Retrieval-Augmented Generation (RAG)
• Grounding model responses
• RAG quality improvement techniques

Module 16: AI Evaluations

• Why evaluations are required
• Relevance, groundedness, fluency, and similarity
• Safety and quality checks
• Evaluation datasets
• Interpreting evaluation results

Module 17: Automated Evaluations

• Cloud evaluators overview
• Evaluation pipelines
• GitHub Actions for automation
• Automated quality gates
• Continuous evaluation workflow

Module 18: Monitoring and Observability

• Tracing and telemetry
• Token usage and performance tracking
• Cost monitoring
• Application Insights / logs overview
• Operational health monitoring

Module 19: Fine-Tuning and Optimization

• Fine-tuning concepts
• When to use fine-tuning vs RAG
• Dataset preparation
• Model performance optimization
• Production readiness and cost optimization