AWS Certified Machine Learning – SpecialtyMachine Learning Implementation and OperationsMedium
A data science team needs to implement a robust MLOps practice where new model versions are automatically trained, evaluated, and deployed based on new incoming data or schedule. They want to define a series of steps, including data preprocessing, model training, model evaluation, and conditional deployment, as an automated workflow. Which AWS service is best suited for building and managing these end-to-end ML pipelines?
- AAmazon SageMaker Pipelines
- BAmazon EventBridge
- CAWS CodePipeline
- DAWS Step Functions
Show answer & explanationAnswer & explanation
Correct answer: A. Amazon SageMaker Pipelines
Amazon SageMaker Pipelines is a purpose-built MLOps service for building, automating, and managing end-to-end machine learning workflows. It allows data scientists and ML engineers to define each step of their ML process (data preparation, model training, evaluation, registration, and deployment) as a pipeline, enabling automation, reproducibility, and versioning of ML workflows.
Why the other options are wrong
- B. Amazon EventBridge is a serverless event bus that connects application components, primarily for event-driven architectures, not for defining sequential ML workflows.
- C. AWS CodePipeline is a general-purpose CI/CD service for software development, not specifically optimized for the unique steps and artifacts of ML workflows.
- D. AWS Step Functions can orchestrate workflows, but it's a general-purpose orchestration service and requires more custom integration for ML-specific steps compared to SageMaker Pipelines.
Amazon SageMaker Pipelines
A purpose-built MLOps service within Amazon SageMaker that allows users to create, automate, and manage end-to-end machine learning workflows, from data preparation to model deployment.
- Automates ML workflow steps (data prep, training, evaluation, deployment).
- Ensures reproducibility and versioning of ML processes.
- Provides visibility into pipeline execution.
- Integrates deeply with other SageMaker services.
Memory trick: SageMaker Pipelines: The ML assembly line, from raw data to deployed model.