MLOps using AWS Sagemaker Session Slides - Session 5 - Governance and Monitoring
Meetup Link
https://www.meetup.com/dataopslabs/events/292989494/
Summary
The presentation provides a comprehensive exploration of machine learning (ML) governance principles, focusing on accountability, ethics, risk mitigation, fairness, and transparency. These principles form the foundation for responsible development, deployment, and use of ML systems, addressing ethical, legal, and technical considerations.
Within the context of Amazon SageMaker, the presentation introduces key tools for governance. Sagemaker Role Manager simplifies the management of AWS IAM roles for secure resource access. Model Cards streamline documentation of critical ML model details, while the Model Dashboard acts as a centralized portal for tracking and exploring all models, incorporating real-time performance through Model Monitoring.
Model Monitoring and Governance Features take center stage, showcasing capabilities such as monitoring model behavior, ensuring data and model quality, and configuring alerts based on specific thresholds. The Model Dashboard, as a central hub, offers users a comprehensive view of model metrics, including data from various SageMaker features like model cards and endpoint performance.
The presentation also highlights the importance of specifying a model's intended uses to ensure responsible development. Risk ratings, categorized as unknown, low, medium, or high, facilitate compliance with rules and regulations, particularly when deploying models with varying risk levels. The structured JSON schema for model cards ensures standardised documentation, incorporating metrics from SageMaker Clarify or Model Monitor for a consistent approach across models.
Previous Sessions
Session 1: Recorded as Video
Session 2: Slides only
MLOps using AWS Sagemaker Session Slides - Session 2 - Studio and MLOps Template
Previous Session are Recorded. AWS Sagemaker - Studio and MLOps Template In this presentation, we'll be diving into the fascinating realm of Amazon SageMaker Studio—a game-changer for anyone navigating the intricate landscape of machine learning. We'll kick things off with an insightful overview of SageMaker Studio, exploring its capabilities in building,…
Session 3: Slides only
MLOps using AWS Sagemaker Session Slides - Session 3 - DataWrangler and Feature Store
Summary The presentation will delve into the dynamic capabilities of Amazon SageMaker Data Wrangler and Feature Store, offering a comprehensive understanding of their functionalities and seamless integration into the machine learning pipeline. Through a live demonstration, attendees will witness the Data Wrangler's ability to transform and publish data t…
Session 4: Slides only
MLOps using AWS Sagemaker Session Slides - Session 4 - Canvas
Summary Embark on an exploration of the transformative capabilities of Sagemaker Canvas in our upcoming presentation. This visual interface redefines machine learning model development, offering accessibility to a broader audience, including business analysts and data scientists without coding expertise. Discover the practical use cases for Sagemaker Can…