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Amazon SageMaker cheat sheet

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Amazon Sagemaker Model Registry Cheat Sheet

2026-01-23T03:30:10+00:00

Bookmarks Core Concepts Features Implementation Integration Best Practices Pricing    A dedicated, fully-managed metadata store and governance hub within Amazon SageMaker designed to catalog, version, track, audit, and deploy machine learning (ML) models throughout their entire lifecycle. It serves as the single source of truth for model inventory, lineage, and approval states, enabling collaboration between data scientists, ML engineers, and governance teams while enforcing consistency and compliance in model deployment workflows. Amazon SageMaker Model Registry Core Concepts Model Package Group A logical container that organizes all iterations of a single model solving [...]

Amazon Sagemaker Model Registry Cheat Sheet2026-01-23T03:30:10+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 Ground Truth Cheat Sheet

2026-01-07T05:39:07+00:00

Bookmarks Features How It Works Implementation Use Cases Integration Best Practices Pricing    A fully managed data labeling service that uses a combination of human workers and machine learning to build high-quality datasets for training machine learning models. It provides built-in workflows, multiple workforce options, and automated labeling to reduce cost and time.   Features Automated Data Labeling (Active Learning) Uses a machine learning model to pre-label datasets and continuously learns from human feedback. It sends only low-confidence data to human reviewers, reducing labeling costs by up to 70% compared to [...]

Amazon Sagemaker Ground Truth Cheat Sheet2026-01-07T05:39:07+00:00

Streamline your SageMaker Environments using Terraform

2026-01-07T20:22:21+00:00

Bookmarks Advantages of using Terraform in SageMaker AI Disadvantages of using Terraform in SageMaker AI Solution Overview Solution The high-level steps to implement this solution with Terraform Pre-requisites for provisioning SageMaker AI Studio with Terraform Create the S3 Bucket and DynamoDB for Remote State Let's implement the Terraform Project Structure for your SageMaker AI Make the required IAM Resources. Put in place Storage Resources (S3) Build Network Infrastructure via VPC. Deploy your SageMaker AI Resources using Terraform. Make the Outputs. To delete your SageMaker AI with [...]

Streamline your SageMaker Environments using Terraform2026-01-07T20:22:21+00:00

Amazon SageMaker AI

2025-12-18T11:15:08+00:00

Bookmarks Concepts Common Training Data Formats For Built-in Algorithms Input modes for transferring training data Two methods of deploying a model for inference SageMaker features Optimization Amazon SageMaker Monitoring Amazon SageMaker Pricing Validate Your Knowledge Amazon SageMaker AI Cheat Sheet A fully managed service that allows data scientists and developers to easily build, train, and deploy machine learning models at scale. Provides built-in algorithms that you can immediately use for model training. Also supports custom algorithms through docker containers. One-click model deployment. Concepts Hyperparameters It refers to a [...]

Amazon SageMaker AI2025-12-18T11:15:08+00:00

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