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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 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

Amazon Bedrock Flows Cheat Sheet

2025-12-30T05:34:25+00:00

Bookmarks Features Use Cases Implementation Security Best Practices Pricing    Amazon Bedrock Flows is a core feature for implementing production-ready, complex generative AI applications. It abstracts the heavy lifting of coding integrations, state management, and deployment pipelines into a drag-and-drop visual interface or API. This allows teams—from developers to subject-matter experts—to collaborate and rapidly iterate on AI workflows, moving from prototyping to scalable, versioned deployments in minutes.   Amazon Bedrock Flows Features Visual, Low-Code/No-Code Builder Design workflows using a drag-and-drop interface in Amazon Bedrock Studio. Link nodes representing Prompts, Foundation Models (FMs), Knowledge [...]

Amazon Bedrock Flows Cheat Sheet2025-12-30T05:34:25+00:00

Amazon Bedrock Knowledge Bases Cheat Sheet

2025-12-23T08:18:07+00:00

Bookmarks Features Use Cases Implementation Security Best Practices Pricing   A fully managed Retrieval-Augmented Generation (RAG) service that securely connects foundation models to your company's private data sources. It automates the entire pipeline—from ingestion and indexing to retrieval and source attribution—enabling accurate, contextual, and verifiable AI responses without building custom data pipelines.   Amazon Bedrock Knowledge Bases Features Fully Managed RAG Pipeline Automates the end-to-end workflow from data ingestion to indexed storage. Handles parsing of complex documents (text, tables, images), intelligent chunking, vector embedding generation, and storage in your chosen vector database. Broad Data [...]

Amazon Bedrock Knowledge Bases Cheat Sheet2025-12-23T08:18:07+00:00

Amazon Bedrock AgentCore Runtime Cheat Sheet

2025-12-20T06:21:59+00:00

Bookmarks Features Use Cases Implementation Security Best Practices Pricing Amazon Bedrock AgentCore Runtime is the execution engine within the Bedrock AgentCore platform, providing a low-latency, serverless environment to run AI agents. It handles the complex infrastructure of scaling, security, and session management, allowing you to focus on developing agent logic. The service supports everything from rapid prototyping to production-scale deployments. Amazon Bedrock AgentCore Runtime Features Framework Agnostic Runtime lets you transform any local agent code to cloud-native deployments with a few lines of code. It works seamlessly with popular frameworks like LangGraph, [...]

Amazon Bedrock AgentCore Runtime Cheat Sheet2025-12-20T06:21:59+00:00

Amazon Bedrock AgentCore Identity Cheat Sheet

2025-12-18T08:10:29+00:00

Bookmarks Features How It Works Implementation Integration Security Best Practices Pricing A specialized identity and credential management service for AI agents and automated workloads. It provides secure authentication, authorization, and credential management, enabling agents to access AWS and third-party services on behalf of users while maintaining security controls and audit trails. Agent identities are implemented as dedicated workload identities. Amazon Bedrock AgentCore Identity Features Centralized Agent Identity Management Provides a unified directory to create, manage, and organize unique workload identities for every AI agent. Each identity includes specialized metadata and [...]

Amazon Bedrock AgentCore Identity Cheat Sheet2025-12-18T08:10:29+00:00

Amazon Bedrock AgentCore Observability Cheat Sheet

2025-12-18T09:15:12+00:00

Bookmarks Features Use Cases Implementation Integration Security Best Practices Pricing Amazon Bedrock AgentCore Observability Cheat Sheet Amazon Bedrock AgentCore Observability delivers complete visibility into AI agent operations, enabling developers to monitor, analyze, and optimize agent performance, understand decision patterns, and troubleshoot issues across complex multi-agent workflows. It provides insights into agent reasoning, tool usage, and conversation flows. Amazon Bedrock AgentCore Observability Features Comprehensive Metrics and Monitoring Collect detailed performance metrics including response times, token usage, success rates, and error patterns. Monitor agent health and availability with configurable thresholds and alerts. [...]

Amazon Bedrock AgentCore Observability Cheat Sheet2025-12-18T09:15:12+00:00

Amazon Bedrock AgentCore Memory Cheat Sheet

2025-12-08T06:13:27+00:00

Bookmarks How Memory Works Memory Types  Implementation Integration Security Best Practices Pricing A managed service that enables AI agents to store, retrieve, and maintain context across conversations, allowing them to remember user information, preferences, and interaction history for more coherent and personalized responses. How Memory Works Memory Storage and Retrieval The Memory service automatically captures relevant information from agent-user conversations and stores it for future use. When an agent needs context, it queries the memory to retrieve past interactions, user details, or learned facts. The system uses semantic search to [...]

Amazon Bedrock AgentCore Memory Cheat Sheet2025-12-08T06:13:27+00:00

AWS, Azure, and GCP Certifications are consistently among the top-paying IT certifications in the world, considering that most companies have now shifted to the cloud. Upskill and earn over $150,000 per year with an AWS, Azure, or GCP certification!

Follow us on LinkedIn, Facebook, or join our Slack study group. More importantly, answer as many practice exams as you can to help increase your chances of passing your certification exams on your first try!