Chapter 54: AWS AI/ML Services
AWS AI & Machine Learning services — often just called AWS AI/ML).
I’m going to teach this the way I wish someone had explained it to me when I was starting — no 40-service list in the first minute, no buzzword salad, but a clear story with real structure, everyday analogies, actual examples from Indian startups (especially Hyderabad/Telangana 2026 reality), and honest guidance on where most people actually begin.
Let’s go step-by-step — like we’re sitting together with a whiteboard and a second cup of filter coffee.
1. First — Understand the two completely different worlds inside “AWS AI/ML” in 2026
Almost all the hype and 90 % of the questions you see right now belong to World A.
But World B is still powering huge parts of real businesses — especially in India.
| World | What it is (2026 reality) | Typical questions people ask | Typical monthly spend (small–medium startup) | Hype level right now |
|---|---|---|---|---|
| A — Generative AI / Foundation Models | Prompting large language models (like ChatGPT), image generation, code assistants, RAG chatbots, voice agents… | “How do I build a Telugu chatbot?” “Can AWS do Gemini/Claude/GPT?” | ₹0 – ₹1,00,000+ | Extremely high |
| B — Classic / Traditional ML | Training & deploying your own models (fraud detection, recommendation, forecasting, churn, computer vision…) | “How to build a delivery time predictor?” “How to do image classification on AWS?” | ₹5,000 – ₹2,00,000+ | Medium (steady, not hyped) |
Both worlds are important. But in 2025–2026, World A (Generative AI) is what 85–90 % of learners, students, freshers, and early-stage startups are asking about.
So let’s start there — and then we’ll look at World B at the end.
2. World A — Generative AI on AWS (The 2025–2026 Main Stage)
Right now the center of gravity is one flagship service:
Amazon Bedrock (launched 2023, massively expanded by 2026)
What Bedrock actually is:
A managed gateway that gives you secure, private, serverless access to ~25 high-quality foundation models from different companies — all through one single API.
You don’t need separate accounts with Anthropic, Meta, Stability AI, Cohere, Mistral, AI21, etc. You get them all in one place, with:
- Enterprise-grade security & privacy (your prompts & data never train the base models)
- Responsible AI tools (guardrails, content filters, PII redaction)
- Evaluation & fine-tuning capabilities
- Knowledge Bases (managed RAG)
- Agents (multi-step reasoning & tool use)
The most popular models in India right now (mid-2026):
- Anthropic Claude 3.5 Sonnet / 3.7 → best reasoning & code
- Meta Llama 3.1 70B / 405B → open-weight favorite
- Amazon Nova family (Micro, Lite, Pro, Premier) → fast & cheap, strong multilingual (including Indian languages)
- Mistral Large 2 / Pixtral → very strong code & multimodal
- Cohere Command R+ / Aya → excellent multilingual, especially Indic languages
- Stability AI Stable Diffusion 3 / SDXL → image generation
- AI21 Jamba / Jurassic → long-context specialist
Real Hyderabad examples (very common right now):
- Customer support chatbot for Telugu + English users Company: Edtech startup in Kukatpally → Bedrock + Claude 3.5 Sonnet → Knowledge Base connected to S3 (uploaded syllabus PDFs + FAQs) → Student asks: “ఇంటర్ ఫిజిక్స్లో కెప్లర్ చట్టాలు ఏమిటి?” → Claude answers in Telugu using only the uploaded content → Monthly cost: ₹6,000–18,000 (depending on student queries)
- Marketing content generator Company: Digital agency in Hi-Tech City → Bedrock + Nova Pro / Claude 3.5 → Prompt: “Write a 150-word Instagram post in Telugu about Sankranti special biryani offer for our restaurant client” → Generates post + hashtags + calls-to-action → Monthly cost: ₹1,000–5,000
- Code assistant for developers → Amazon Q Developer (powered by Bedrock models) inside VS Code → Developer asks: “Write a Lambda function in Python that resizes images uploaded to S3 and saves thumbnails” → Q generates code + explains it + suggests improvements → Saves 30–50 % coding time
3. Quick Map of Generative AI Services on AWS (2026 – What Actually Gets Used)
| You want to… | First Choice Service(s) | Why most Hyderabad teams pick this one right now |
|---|---|---|
| Prompt powerful LLMs (chat, generation, RAG) | Amazon Bedrock | One API, many top models, strong privacy & guardrails |
| Get AI coding help inside IDE / Console | Amazon Q Developer | Best integration + context-aware (sees your code & AWS resources) |
| Chat on internal company documents | Amazon Q Business | Connects to Confluence, SharePoint, S3, Gmail, Slack… |
| No-code playground for beginners | PartyRock | Free, fun, great for students & non-coders |
| Build AI agents (multi-step reasoning + tools) | Bedrock Agents | Connects to Lambda, Knowledge Bases, APIs |
4. World B — Classic / Traditional Machine Learning (Still Powers Huge Parts of Business)
Even though generative AI gets all the headlines, classic ML is still running:
- Fraud detection
- Recommendation engines
- Demand forecasting
- Churn prediction
- Computer vision (helmet detection, quality inspection)
- Predictive maintenance
- Sentiment analysis (non-LLM version)
Main service here: Amazon SageMaker
| SageMaker Feature | What it does | Typical Hyderabad use-case |
|---|---|---|
| SageMaker Studio | JupyterLab notebooks + collaboration | Data scientists building models |
| SageMaker JumpStart | 600+ pre-trained models + solutions (one-click) | Quick POC: Telugu sentiment, helmet detection |
| Built-in algorithms | XGBoost, DeepAR, BlazingText, Object Detection… | Fast training without bringing your own code |
| Autopilot / Canvas | AutoML — no-code / low-code ML | Business analyst predicts churn without code |
| Clarify | Bias detection & model explainability | Fintech proving loan model is not biased |
Real example – 2026 Hyderabad logistics startup
They want to predict delivery time for every pin-code in Telangana + Andhra.
- Data in S3 (historical orders + traffic + weather)
- Use SageMaker Studio → Jupyter notebook
- Train XGBoost with SageMaker built-in algorithm
- Deploy model as SageMaker endpoint
- Lambda calls endpoint for every new order → predicted ETA shown to customer
- Monthly cost: training once a week (~₹2,000–5,000) + inference (~₹3,000–10,000)
5. Quick Decision Tree – Where Should You Start in 2026?
- Do you want to prompt large language models (ChatGPT-style) or generate images/code/content? → Amazon Bedrock (most powerful & popular option right now)
- Do you want AI coding help right inside your IDE or AWS Console? → Amazon Q Developer (huge time-saver)
- Do you want ChatGPT-like search on your internal company documents? → Amazon Q Business
- Do you want to train your own machine learning model from data? → Amazon SageMaker (or JumpStart for faster start)
- Do you want ready-made AI with one API call (no training)? → Rekognition (images/videos), Comprehend (NLP), Textract (documents), Transcribe (speech), Translate, Polly (TTS)
Summary – AWS AI/ML Quick Map (Feb 2026 – India Focus)
| You want to… | First Choice Service(s) | Why this one wins in Hyderabad right now? |
|---|---|---|
| Build chatbot / content generation / RAG | Amazon Bedrock | Claude 3.5, Llama 3.1, Nova — strong Indic language support |
| Get coding help / explain AWS resources | Amazon Q Developer | Integrated in VS Code & Console — saves 30–50 % time |
| Chat on internal company documents | Amazon Q Business | Connects to Confluence, SharePoint, S3, Gmail, Slack… |
| Train your own ML model | SageMaker | Full control + JumpStart for fast POC |
| Add image/video analysis | Rekognition | Helmet detection, face comparison, text in image |
| Extract text from scanned documents | Textract | PAN/Aadhaar/KYC form processing |
| Analyze customer reviews / sentiment | Comprehend | Telugu + English sentiment & entity detection |
Teacher’s final note (real talk – Hyderabad 2026):
Most beginners & startups do NOT start with SageMaker training from scratch. They start with:
- Amazon Bedrock (for chatbots, content, RAG)
- Amazon Q Developer (to become 30–50 % faster at coding)
- Rekognition / Textract / Comprehend (quick AI features with zero training)
Only when they have real data scientists and large datasets do they move to full SageMaker training pipelines.
Got it? This is your first clear map of AI/ML on AWS — no 35-service list, just the things that actually matter right now.
Next lesson?
- Deep dive: Build a Telugu customer support chatbot with Bedrock + Knowledge Bases
- Step-by-step: Add Rekognition helmet detection to your bike delivery app
- Or full comparison: Bedrock vs SageMaker JumpStart vs PartyRock vs OpenAI/Gemini?
Tell me — next class ready! 🚀🤖
