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sagemaker

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Bring Your Own Container Made Easy: Introducing AWS ml-container-creator

2026-01-27T18:51:07+00:00

If you’ve ever struggled to package your ML model in a custom Docker image for SageMaker, the new ml-container-creator tool is here to help. This friendly open-source wizard guides you through building a SageMaker-compatible container without all the usual Docker headaches. It’s like having an assistant that writes your Dockerfile, server code, and config files for you, so you can focus on your model. What is BYOC on SageMaker? BYOC stands for Bring Your Own Container. In SageMaker, BYOC means you supply your own Docker image with everything needed to serve your ML model (the code, libraries, dependencies, etc.). AWS [...]

Bring Your Own Container Made Easy: Introducing AWS ml-container-creator2026-01-27T18:51:07+00:00

Amazon SageMaker Model Monitor Cheat Sheet

2026-01-12T09:02:21+00:00

Bookmarks Features How It Works Implementation Use Cases Integration Best Practices Pricing    A fully-managed, automated service within Amazon SageMaker that continuously monitors the quality of machine learning (ML) models in production. It automatically detects data drift and model performance decay, sending alerts so you can maintain model accuracy over time without building custom monitoring tools. Features Automated Data Capture & Collection Configures your SageMaker endpoints to capture a specified percentage of incoming inference requests and model predictions. This data, enriched with metadata (timestamp, endpoint name), is automatically stored in your [...]

Amazon SageMaker Model Monitor Cheat Sheet2026-01-12T09:02:21+00:00

Amazon Sagemaker Jumpstart Cheat Sheet

2026-01-12T07:23:56+00:00

Bookmarks Features How It Works Implementation Use Cases Integration Best Practices Pricing    A centralized machine learning hub within Amazon SageMaker AI designed to drastically reduce the time and expertise required to build, train, and deploy models. It provides instant access to a curated catalog of production-ready assets.   Features Foundation Models Hub Access a broad selection of state-of-the-art foundation models from providers like AI21 Labs, Cohere, Meta, Mistral AI, and Stability AI, alongside hundreds of open-source models from Hugging Face. You can evaluate, compare, and perform tasks like text summarization, [...]

Amazon Sagemaker Jumpstart Cheat Sheet2026-01-12T07:23:56+00:00

Amazon SageMaker Clarify

2025-12-12T05:19:31+00:00

Bookmarks Key Capabilities Core Components Configuration Components Bias Metrics Overview SHAP Explainability Validate Your Knowledge Amazon SageMaker Clarify Cheat Sheet Amazon SageMaker Clarify is a SageMaker AI feature for detecting bias and explaining model predictions. Supports both pre-training and post-training bias analysis. Provides feature attribution to explain how input features influence predictions. Can monitor deployed models for bias drift and feature attribution drift over time. Key Capabilities Bias Detection Pre-training bias: Analyzes datasets before model training. Post-training bias: Evaluates model predictions for fairness across facets. Supports binary, multiclass, and regression tasks. Interpreting [...]

Amazon SageMaker Clarify2025-12-12T05:19:31+00:00

Amazon AI Fairness and Explainability with Amazon SageMaker Clarify

2023-12-02T01:24:03+00:00

Introduction In the rapidly evolving domain of machine learning, ensuring fairness and explainability in model predictions has become crucial. With Amazon SageMaker Clarify, these critical aspects are not just an afterthought but integral components of the model development and deployment process. This article delves into the world of SageMaker Clarify, offering a comprehensive guide to its capabilities and practical applications. We commence our journey with a high-level understanding of what SageMaker Clarify is and its importance in the day-to-day tasks of machine learning modeling. Our exploration is anchored in a hands-on example, utilizing a specially crafted dataset that simulates loan [...]

Amazon AI Fairness and Explainability with Amazon SageMaker Clarify2023-12-02T01:24:03+00:00

AWS re:Invent 2022 Announcements for AWS Machine Learning Engineers and Data Scientists

2023-04-27T03:58:46+00:00

Every year, professionals all around the world attend the most transformative tech event —  AWS re:Invent. Here, a LOT of new AWS services and capabilities are announced and discussed. In this post, we will focus on the major announcements relevant to data scientists and ML engineers! A Gentle Introduction to Amazon SageMaker The major announcements discussed in this post focus on SageMaker, so we will spend a paragraph or two quickly talking about the service.  Amazon SageMaker is the machine learning platform of AWS that helps solve a variety of requirements of data scientists, developers, and machine learning practitioners in [...]

AWS re:Invent 2022 Announcements for AWS Machine Learning Engineers and Data Scientists2023-04-27T03:58:46+00:00

Scalable Data Processing and Transformation using SageMaker Processing (Part 1 of 2)

2023-08-14T02:56:21+00:00

Amazon SageMaker is the machine learning platform of AWS which helps solve the different requirements of data scientists and machine learning practitioners. It has several features and capabilities that assist in the different stages of the machine learning process. Here is a simplified list of the capabilities of SageMaker mapped to some of the stages of the ML lifecycle. SageMaker Processing Data Preparation and Processing  SageMaker Training Model Training SageMaker Automatic Hyperparameter Tuning Model Training SageMaker Debugger Model Training SageMaker Hosting Services Deployment and Monitoring SageMaker Model Monitor Deployment and Monitoring   There’s definitely more into this list which we [...]

Scalable Data Processing and Transformation using SageMaker Processing (Part 1 of 2)2023-08-14T02:56:21+00:00

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