Designing Your MLOps Framework

A robust ML pipeline is more than just a sequence of scripts. It's a well-designed, automated workflow that handles everything from data ingestion to model deployment. Google Cloud offers several tools for building and orchestrating ML pipelines. The three main options are Kubeflow Pipelines (via Vertex AI Pipelines), TensorFlow Extended (TFX), and Cloud Composer.

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1. Kubeflow Pipelines (KFP) on Vertex AI

Vertex AI Pipelines is a fully managed service for running Kubeflow Pipelines. KFP is a flexible and powerful platform for building ML pipelines.

• Key Concepts:
  • Components: Self-contained pieces of code that perform a single task (e.g., data preprocessing, model training). Components are packaged as Docker containers.
  • Pipeline: A Directed Acyclic Graph (DAG) that defines the relationships between components.
• Best For:
  • Custom ML workflows.
  • Pipelines that involve a mix of different frameworks and languages.
  • Teams that want a flexible and extensible platform.

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2. TensorFlow Extended (TFX)

TFX is an end-to-end platform for building ML pipelines with TensorFlow. It provides a set of pre-built components for common ML tasks, such as data validation, feature engineering, and model analysis.

• Key Concepts:
  • Standard Components: TFX provides a library of pre-built components that are designed to work together.
  • Metadata Store: TFX automatically tracks the metadata for all your pipeline runs, which is useful for governance and reproducibility.
• Best For:
  • TensorFlow-based ML workflows.
  • Teams that want a more structured and opinionated platform.
  • Pipelines that require strong data validation and model analysis capabilities.

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3. Cloud Composer (Apache Airflow)

Cloud Composer is a fully managed workflow orchestration service based on Apache Airflow. While it's not specifically designed for ML, it can be used to orchestrate ML pipelines.

• Key Concepts:
  • DAGs: Airflow uses DAGs to define workflows.
  • Operators: Airflow provides a library of pre-built operators for interacting with various services, including BigQuery, Dataflow, and Vertex AI.
• Best For:
  • Teams that are already using Airflow for other data engineering tasks.
  • Complex workflows that involve a mix of ML and non-ML tasks.

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4. Choosing the Right Tool

Feature	Vertex AI Pipelines (KFP)	TFX	Cloud Composer (Airflow)
Primary Use Case	Custom ML Workflows	TensorFlow-based ML Workflows	General Purpose Workflow Orchestration
Flexibility	High	Medium	High
Ease of Use	Medium	Medium	Low
Data Validation	Custom	Strong	Custom
Model Analysis	Custom	Strong	Custom

Exam Tip: For most ML pipeline scenarios on the exam, Vertex AI Pipelines (KFP) is the preferred choice due to its flexibility and tight integration with other Vertex AI services.

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Knowledge Check