AWS Certified Machine Learning – SpecialtyMachine Learning Implementation and OperationsMedium
A data science team is developing a new machine learning model for a critical real-time application. They need to ensure that the model can be deployed rapidly and reliably, with automated testing and versioning. The team also wants to maintain a clear audit trail of all model changes and deployments. Which AWS MLOps service provides a managed workflow orchestration for building, training, and deploying ML models, including automated steps for testing and versioning?
- AAWS Step Functions
- BAWS CodeDeploy
- CAmazon EventBridge
- DAmazon SageMaker Pipelines
Show answer & explanationAnswer & explanation
Correct answer: D. Amazon SageMaker Pipelines
Amazon SageMaker Pipelines is a purpose-built MLOps service for SageMaker that allows data scientists and ML engineers to create, automate, and manage end-to-end machine learning workflows. It provides steps for data processing, model training, evaluation, registration, and deployment, ensuring automated testing, versioning, and an audit trail.
Why the other options are wrong
- A. AWS Step Functions can orchestrate workflows, but it's a general-purpose orchestration service and doesn't offer ML-specific steps or direct integration with SageMaker model registry/versioning like SageMaker Pipelines.
- B. AWS CodeDeploy is a deployment service for application code, not specifically designed for ML model lifecycle management with built-in versioning and testing for models.
- C. Amazon EventBridge is a serverless event bus for connecting applications with data from various sources, not an MLOps workflow orchestration service.
Amazon SageMaker Pipelines
A managed service for orchestrating and automating end-to-end machine learning workflows on SageMaker, providing CI/CD capabilities for ML models.
- Defines ML workflows as directed acyclic graphs (DAGs)
- Supports automated steps for training, evaluation, registration, deployment
- Provides model versioning and lineage tracking
- Integrates with SageMaker MLOps services
Memory trick: Pipeline your ML, from data to deploy, with full control.