MLOps Engineering AWS

Module 1: Introduction to MLOps

• Machine Learning Operations
• MLOps fundamentals
• Goals and benefits of MLOps
• DevOps vs MLOps
• Machine learning workflow
• Data, code, and model lifecycle
• MLOps maturity
• Collaboration between teams
• MLOps use cases
• Communication and organizational considerations

Module 2: MLOps Development and Experimentation

• ML experimentation environments
• Amazon SageMaker
• SageMaker Studio
• Building ML models
• Training and evaluation
• Model experimentation
• Data and model versioning
• Model artifacts
• Reproducibility
• Security and governance
• Multi-account MLOps strategies

Module 3: Automating ML Workflows

• ML workflow automation
• Automated build processes
• Automated training
• Automated testing
• Automated model packaging
• Automated deployment
• Amazon SageMaker Pipelines
• AWS CodeBuild
• CI/CD for machine learning
• Apache Airflow
• Kubernetes integration
• Infrastructure automation

Module 4: MLOps Deployment

• ML model packaging
• Model registries
• Amazon SageMaker model deployment
• Real-time inference
• Batch inference
• Serverless inference
• Model endpoints
• Production variants
• Deployment strategies
• Blue/green deployments
• Canary deployments
• A/B testing
• Traffic shifting

Module 5: Model Monitoring and Operations

• Importance of ML monitoring
• Model performance monitoring
• Data drift
• Model drift
• Concept drift
• Model bias monitoring
• Resource utilization
• Inference latency
• Prediction quality
• Amazon SageMaker Model Monitor
• CloudWatch monitoring
• Production troubleshooting

Module 6: Automated Retraining and Continuous Improvement

• Model performance degradation
• Retraining triggers
• Automated retraining
• New data integration
• Continuous training
• Model validation
• Automated testing
• Model redeployment
• ML pipeline troubleshooting
• Continuous improvement

Module 7: MLOps Security and Governance

• ML security fundamentals
• IAM for ML workloads
• Data security
• Model security
• Encryption
• Access control
• Governance requirements
• Compliance considerations
• Security threats in ML workflows
• Mitigation strategies
• Secure MLOps architecture

Module 8: Production ML Operations

• Production model lifecycle
• Scaling ML workloads
• Inference optimization
• Model resource management
• Human-in-the-loop reviews
• Operational workflows
• Incident management
• Model rollback
• Reliability considerations
• MLOps action planning

Module 9: Hands-On MLOps Workshop

• Set up an MLOps environment
• Build an ML pipeline
• Train and evaluate a model
• Package the model
• Deploy the model to production
• Implement automated testing
• Configure model monitoring
• Detect data drift
• Conduct A/B testing
• Troubleshoot an ML pipeline
• Automate model retraining
• Develop an organizational MLOps action plan

• MLOps Engineers
• Machine Learning Engineers
• DevOps Engineers
• ML Data Platform Engineers
• Data Engineers
• Data Scientists involved in production ML
• Software Developers working with ML applications
• Cloud Engineers
• Cloud Operations Professionals
• Developers and Operations teams responsible for operationalizing ML models
• Professionals responsible for deploying and maintaining machine learning models on AWS

• Understand the principles and benefits of MLOps.
• Differentiate MLOps from traditional DevOps.
• Understand the complete machine learning lifecycle.
• Apply DevOps practices to ML model development and deployment.
• Use Amazon SageMaker for MLOps workflows.
• Build automated pipelines for model building, training, testing, and deployment.
• Implement ML CI/CD practices.
• Automate model packaging and deployment.
• Implement production deployment strategies including A/B testing and traffic shifting.
• Monitor model performance, resource consumption, and inference latency.
• Detect data drift and model performance degradation.
• Implement automated model retraining.
• Apply security and governance practices to ML workloads.
• Troubleshoot ML pipelines and production model deployments.
• Implement human-in-the-loop processes where appropriate.
• Develop an actionable MLOps strategy for organizational adoption.

Required

• AWS Technical Essentials or equivalent experience.
• DevOps Engineering on AWS or equivalent experience.
• Practical Data Science with Amazon SageMaker or equivalent experience.

Recommended

• Basic understanding of machine learning concepts and workflows.
• Familiarity with data science processes.
• Understanding of software development and CI/CD.
• Experience with cloud computing and AWS services.
• Familiarity with containers and Kubernetes is beneficial.
• Basic understanding of Python or another programming language is helpful.
• Knowledge of data engineering and model lifecycle management is advantageous.

Course: MLOps Engineering on AWS
Level: Intermediate
Duration: 3 Days
Delivery: Instructor-led training
Hands-On: Yes
Technology: Amazon Web Services (AWS)
Primary Platform: Amazon SageMaker