AWS AI Tooling: The Cheat Sheet Nobody Asked For (But Everyone Needs)

TL;DR: AWS offers numerous AI services, making it challenging to keep them all straight. This is your no-nonsense reference for actually figuring out which one solves your problem, without having to read a near-unlimited number of pages of documentation first. Note that your system can be built using one or more of these AWS components.

Note: This document will evolve as AWS releases new services and I discover more effective ways to explain (and utilize) the existing ones.

Generative AI

Bedrock - Your "Just Make It Work" Solution

Remember when deploying ML models meant provisioning infrastructure, managing versions, and dedicating your weekends and paying tribute to <insert-divinity>, who might grant you success? Bedrock is the opposite of that. It provides plug-and-play access to foundation models via the SDK. Think of it as the AWS-ification of AI; they handle the hard parts, you handle the business logic.

The stuff you actually care about

Bedrock AgentCore - Managing AI agents at enterprise scale without losing your mind. Covers most use cases, handles security, and won't wake you up at 03:00 due to deployment-related issues. If you're running AI for a big organization, this is your toolkit.

Bedrock Guardrails - Use this when "move fast and break things" doesn't work and when your AI is customer-facing. This is your safety net for content moderation. Nobody wants their chatbot to become a PR disaster on a Friday afternoon.

Cross-region inference - Routes requests based on current load. It's like having an intelligent load balancer that actually understands your models aren't just stateless functions. Your models become truly plug-and-play across regions. You can use this service when you want AI capabilities without needing to become an ML infrastructure expert, which is, let's be honest, most of the time.

Machine Learning

SageMaker - The Swiss Army Knife

SageMaker is the answer to "what if we made machine learning... manageable?" It's comprehensive, fully managed, and eliminates most of the operational headaches.

The usual workflow:

Build - Spin up a Jupyter notebook, load your data, and start experimenting. You know, the fun part.

Train - Run training jobs with built-in algorithms or your custom code. SageMaker handles infrastructure scaling, so you can focus on whether your model actually works.

Deploy - Push your model live with no adjustments required. Integration with the rest of AWS happens when you're ready, not when AWS decides it's time.

Use it when Bedrock's pre-trained models aren't enough and you need something tailored. It's more effort, but you get precisely what you want.

Computer Vision & Language

The "AI But Make It Useful" Services

AWS offers a suite of services that are basically "we trained the models so you don't have to." They're surprisingly good, and you can implement them without a PhD.

The lineup

Rekognition - Deep learning for images and video. Detects faces, objects, text, and that weird thing in the corner of your security footage. It just works, and you don't need to understand convolutional neural networks to use it.

Textract - OCR on steroids. Pulls text, handwriting, layout elements, and structured data from documents. Goes way beyond "here's some text" into "here's the actual data you wanted."

Transcribe - Speech-to-text that handles 100+ languages and both real-time streaming and batch processing. Great for when you have audio and need text, without manually setting up inference pipelines.

Comprehend - NLP service that finds entities, key phrases, language, sentiment, and other insights in text. Uses ML to understand documents so you don't have to write regex for the 47th time.

Translate - Real-time translation using deep learning. Fast, affordable, and customizable. Beats the old "translate via Google Sheets" workaround most teams start with.

Polly - Text-to-speech with actual personality. Uses deep learning to sound less robotic and more human. Perfect for accessibility features or voice interfaces that don't make users want to throw their devices.

Use one or more of these tools when you have a specific, well-defined problem (transcribe this, translate that, find entities in documents) and don't want to become a specialist in that domain.

BI & Recommendations

Personalize - Netflix-Style Recommendations

Helps you build custom recommendation engines with real-time personalization and user segmentation. It's the same technology that powers the "customers who bought this also bought..." feature everywhere on the internet. Getting recommendations right is more challenging than it appears. Personalize handles the ML complexity so you can focus on the business logic of what to recommend.

Forecast - Time-Series Predictions

Statistical and ML algorithms for forecasting. Built on the same tech Amazon uses internally for demand prediction, which means it's battle-tested at scale. Use this when you need to predict future values based on historical data and don't want to spend hours developing a forecasting model.

Kendra - Enterprise Search That Doesn't Suck

Intelligent search using NLP and ML to help people find content across your organization's repositories. Think Google, but for your internal systems. Keeps employees from spending half their day searching for documents. Kendra searches actually work using natural language instead of boolean operators nobody remembers.

Model Context Protocol (MCP)

The Integration Layer You Didn't Know You Needed

What is MCP?

MCP is the connective tissue between your LLM and your actual data. It lets AI access external data and trigger actions. AWS has built several MCP servers that provide deep access to AWS APIs, enabling natural language input and outputting AWS actions.

Why this matters: Most AI implementations fail not because the models are bad, but because they can't access the correct data. MCP solves the "last mile" problem of AI integration.

Useful MCP Servers

If you're working with AWS, these MCPs are worth exploring. At a minimum, check out the repo https://awslabs.github.io/mcp/.

Documentation MCP - Real-time access to official AWS docs, API references, What's New posts, Getting Started guides, Builder Library, blog posts, and architectural references. No more having 30+ tabs open.

Infrastructure & Deployment - Build, deploy, and manage cloud infrastructure through conversation instead of clicking through consoles.

AI & Machine Learning - Enhance AI applications with knowledge retrieval and ML capabilities. No custom integration code required.

Data & Analytics - Work with databases, caching systems, and data processing through natural interfaces.

MCP servers turn your systems from "things you have to learn" into "things you can just ask." That's a big win.

The Strategy View

The AWS catalog is overwhelming (probably by design). AWS builds tools for every use case, which means you need to know which tool fits the problem you’re trying to solve.

The decision tree:

• Need AI capabilities fast with minimal setup? → Bedrock

• Need custom models for specific problems? → SageMaker

• Have a well-defined AI task (transcribe, translate, etc.)? → Specialized services (Rekognition, Textract, etc.)

• Do you need to connect AI to your existing systems? → MCP

Ready to Transform Your AI Infrastructure?

At Elva, we help organizations move past AI PoC and MVP theater. Our team's expertise with the Model Context Protocol and AWS lets companies:

• Implement robust infrastructure for integrating Generative AI.

• Transform legacy systems into AI-accessible resources without costly replacements.

• Build once, deploy AI everywhere across your entire ecosystem.

Make your business easy to talk to. Contact Elva to implement MCP and gain a competitive edge.

LinkedIn / Hashnode / Contact

Note: This document will evolve as AWS releases new services and I discover more effective ways to explain (and utilize) the existing ones.