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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 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 Bedrock’s LLM-as-a-Judge: Automate AI Evaluation with Nova Lite + Claude

2026-01-23T09:21:49+00:00

Evaluating your LLM’s quality should not cost you too much money or even weeks of your time.  You’re probably  stuck in a limbo of choosing between two options that have their own drawbacks: Automated metrics like BLEU, ROUGE and accuracy scores? Sure, they are quite fast and cheap, but they ultimately fall short in judging real conversations. Since they simply match word patterns, they're not a good fit for open ended responses simply because they can't tell  if they actually understood the question or its tone. As for human reviewers, they do get it right. They captured the context/subtlety and [...]

Amazon Bedrock’s LLM-as-a-Judge: Automate AI Evaluation with Nova Lite + Claude2026-01-23T09:21:49+00:00

Amazon Bedrock Runtime

2026-01-07T13:42:57+00:00

Bookmarks Amazon Bedrock Runtime Actions Key Data Types Inference Concepts Security Pricing Amazon Bedrock Runtime Cheat Sheet Amazon Bedrock Runtime is a high-performance, serverless API that enables developers to make inference requests to Foundation Models (FMs) available in Amazon Bedrock. It serves as the primary runtime interface for building generative AI applications, supporting use cases including text generation, multi-turn conversations, real-time streaming, image generation, embeddings, and more. The API is optimized for low latency and high throughput and provides unified access across multiple model providers.   Amazon Bedrock [...]

Amazon Bedrock Runtime2026-01-07T13:42:57+00:00

Amazon Bedrock API Reference

2026-01-07T13:33:19+00:00

Bookmarks Amazon Bedrock API Reference Common Parameters Amazon Bedrock API Reference Common Errors API Endpoint Structure Best Practices Amazon Bedrock API Reference Sheet Amazon Bedrock API Reference is the master specification for the Amazon Bedrock service. It encompasses protocols, authentication methods, endpoints, common parameters, and error-handling standards used across the entire Bedrock ecosystem (both the Control Plane and the Data Plane).   Amazon Bedrock API Reference Common Parameters Action: (String) Specifies the particular API action to be performed. Version: (String) Indicates the API version used for the request, formatted as [...]

Amazon Bedrock API Reference2026-01-07T13:33:19+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

A Beginner’s Guide to the Machine Learning Pipeline on GCP

2026-01-08T13:02:44+00:00

When people hear the term "machine learning," they often imagine complex math, advanced algorithms, or mysterious "AI magic" happening behind the scenes. In reality, machine learning on the cloud is far more practical and structured than it may sound. At its core, an ML pipeline is a series of steps that transform raw data into useful predictions. Think of it like a typical software workflow: You prepare your code You build the application You deploy it Users interact with it An ML pipeline follows the same idea, just with different building blocks. Instead of starting with source code, you begin [...]

A Beginner’s Guide to the Machine Learning Pipeline on GCP2026-01-08T13:02:44+00:00

Amazon Bedrock Prompt Management

2025-12-30T14:14:17+00:00

Bookmarks Core Concepts Supported Regions and Models Prerequisites Creating a Prompt Viewing Prompt Information Modifying a Prompt Testing a Prompt Optimizing a Prompt Deployment using Versions Deleting a Prompt Security Best Practices Pricing Amazon Bedrock Prompt Management Cheat Sheet Amazon Bedrock Prompt Management is a centralized prompt management system for generative AI, enabling easy creation, testing, versioning, and deployment of structured prompts while separating prompt engineering from application code. It offers key [...]

Amazon Bedrock Prompt Management2025-12-30T14:14:17+00:00

Amazon Titan

2025-12-30T14:40:34+00:00

Bookmarks Features Use Cases Amazon Titan Text Models Amazon Titan Text Embeddings Models Amazon Titan Multimodal Embeddings G1 Model Amazon Titan Image Generator G1 Models Pricing Amazon Titan Cheat Sheet Amazon Titan models are a family of powerful, general-purpose models pre-trained by AWS on massive datasets. They are designed to be used out of the box or fine-tuned with your own data, allowing you to adapt them for specific tasks without the need to annotate large volumes of training data. The Titan family consists [...]

Amazon Titan2025-12-30T14:40:34+00:00

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