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Reserved Instance (RI) Reporting

2026-03-26T04:51:20+00:00

Reserved Instance (RI) Reporting Cheat Sheet AWS provides built‑in tools to help you understand and manage your Reserved Instances (RIs). You can visualize RI data at an aggregate level, inspect individual subscriptions, access the most detailed usage information, and set custom utilization targets with alerts.   Reserved Instance (RI) Reporting Utilization and Coverage  The RI Utilization and RI Coverage reports are available in AWS Cost Explorer. They let you see your RI data at an aggregate level or drill into a specific RI subscription. RI Utilization – How much of your purchased RI capacity you are actually using. RI Coverage – How much of your instance [...]

Reserved Instance (RI) Reporting2026-03-26T04:51:20+00:00

AWS Transform

2026-03-19T09:25:04+00:00

AWS Transform Cheat Sheet AWS Transform is an agentic AI service designed to accelerate enterprise modernization of full-stack Windows, mainframe, and VMware workloads, as well as custom transformations of code, APIs, and frameworks. Built on 20 years of AWS migration experience, it uses specialized AI agents to automate complex tasks such as assessments, code analysis, refactoring, decomposition, dependency mapping, validation, and transformation planning. The service enables teams to modernize hundreds of applications in parallel through a natural language chat experience and shared workspaces.   Key Benefits of AWS Transform Accelerate modernization – Modernize Windows, mainframe, and VMware applications up to 5x [...]

AWS Transform2026-03-19T09:25:04+00:00

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 Data Automation Cheat Sheet

2025-12-30T06:07:02+00:00

Bookmarks Features Use Cases Implementation Security Best Practices Pricing    Amazon Bedrock Data Automation is a purpose-built service for transforming complex, unstructured content—such as invoices, contracts, forms, and research papers—into structured data. It handles the entire pipeline, from document parsing and classification to advanced information extraction using natural language and computer vision, enabling you to build scalable document workflows integrated directly with Knowledge Bases, databases, and analytics tools.   Amazon Bedrock Data Automation Features Multimodal Document Understanding Processes a wide range of document types and formats, including scanned PDFs, digital PDFs, JPEG/PNG images, [...]

Amazon Bedrock Data Automation Cheat Sheet2025-12-30T06:07:02+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

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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!