Chapter 100: AWS Machine Learning
AWS Machine Learning
When someone asks “What is AWS Machine Learning?”, they usually want one of three things:
- A clear map of the entire AWS ML ecosystem in 2026
- Understanding which service to choose for which real problem
- Honest examples of what actual Indian companies (especially in Hyderabad/Bengaluru) are doing with AWS ML right now
So let’s do all three — slowly, honestly, whiteboard style — like I’m your favorite teacher who wants you to actually understand, not just memorize service names.
1. The Big Picture – Three Completely Different Worlds Inside “AWS ML” in 2026
Almost all confusion comes from mixing up these three layers. They have very different audiences, skill levels and pricing.
| Layer (2026 name) | What you actually do | Skill level needed | Who uses it most in Hyderabad right now? | Typical monthly cost range |
|---|---|---|---|---|
| 1. Generative AI / Foundation Models | Prompt large language models, build chatbots, RAG, image generation, code assistants | Beginner to intermediate | Startups building support bots, content teams, marketing, developers | ₹0 – ₹80,000+ |
| 2. Ready-made AI/ML APIs (no training) | Call pre-built models with one API (zero training) | Beginner | Almost every app — fraud, text, image, speech, translation | ₹500 – ₹30,000 |
| 3. Classic / Custom Machine Learning | Train your own models from data (classification, regression, forecasting, computer vision) | Intermediate to advanced | Data science teams in fintech, edtech, logistics, retail | ₹5,000 – ₹3 lakh+ |
2. Layer 1 — Generative AI / Foundation Models (The 2025–2026 Explosion Layer)
This is what 85–90 % of people mean when they say “AWS AI” or “AWS Machine Learning” in 2026.
Flagship service: Amazon Bedrock
What Bedrock really is:
A managed gateway that gives you secure, private, serverless access to ~25 high-quality foundation models through one single API.
You don’t need separate accounts with Anthropic, Meta, Stability AI, Cohere, Mistral, etc. Everything is in one place with enterprise privacy, guardrails, content filters, knowledge bases (managed RAG), agents, evaluation and fine-tuning.
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 Indic language support
- Mistral Large 2 / Pixtral → excellent code & multimodal
- Cohere Command R+ / Aya → very strong multilingual (especially Indic)
Real Hyderabad example – EdTech startup in Kukatpally
They have 800+ PDF notes, 300 video transcripts, 1,200 FAQs in Telugu & English.
Goal: students ask questions in Telugu → get accurate answers from their own content.
How they built it:
- Upload all PDFs & transcripts to S3
- Create Knowledge Base for Amazon Bedrock → points to S3 → auto-chunks & embeds
- Simple web app (React + API Gateway + Lambda)
- Student asks: “ఇంటర్ ఫిజిక్స్లో కెప్లర్ చట్టాలు ఏమిటి?”
- Bedrock → Knowledge Base → Claude 3.5 Sonnet → answer in Telugu with exact page reference
Monthly cost: ~₹6,000–22,000 (depends on student queries)
3. Layer 2 — Ready-made AI/ML APIs (Zero training needed)
These are “call one API → get answer” services — very popular for quick wins.
| Service | What it does | Typical Hyderabad use-case | Pricing style |
|---|---|---|---|
| Amazon Rekognition | Image & video analysis (face detection, labels, celebrities, text in image) | Detect helmet in bike photos for insurance / delivery apps | Per image / minute |
| Amazon Comprehend | NLP — sentiment, entities, language detection, custom classifiers | Analyze customer reviews in Telugu + English | Per text unit |
| Amazon Textract | Extract text/tables from PDFs, scanned documents | KYC document processing (Aadhaar, PAN extraction) | Per page |
| Amazon Transcribe | Speech-to-text (supports Telugu) | Transcribe customer support calls | Per minute |
| Amazon Translate | Real-time translation (22 Indian languages) | Show product descriptions in Telugu / Hindi / English | Per character |
| Amazon Polly | Text-to-speech (includes Telugu voice) | Voice assistant reading menu items | Per character |
Real example – Fintech KYC flow (Financial District 2026):
- User uploads PAN card photo → Rekognition detects text & face
- Textract extracts name, PAN number, DOB
- Comprehend checks sentiment of uploaded selfie note (fraud check)
- Translate converts English terms to Telugu for user confirmation
- All in one Lambda function → whole flow < 3 seconds
Monthly cost: ~₹8,000–25,000
4. Layer 3 — Classic / Custom Machine Learning (When you need to train your own models)
This is the older (but still massive) world: fraud detection, recommendation, demand forecasting, churn prediction, computer vision, etc.
Main service: 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 + one-click solutions | 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 – Logistics startup in Uppal
They want to predict delivery time for every pin-code in Telangana + Andhra.
- Use SageMaker Studio → Jupyter notebook
- Load historical data from S3
- Train XGBoost with SageMaker built-in algorithm
- Deploy model as 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)
Summary Table — AWS Machine Learning 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 |
| Extract text from scanned documents | Amazon Textract | PAN/Aadhaar/KYC form processing |
| Analyze images/videos | Amazon Rekognition | Helmet detection, face match |
| Analyze customer reviews / sentiment | Amazon Comprehend | Telugu + English sentiment & entity detection |
| Real-time personalized recommendations | Amazon Personalize | “You may also like” dishes / products |
| Train your own ML model | Amazon SageMaker | Full control + JumpStart for fast POC |
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 AWS Machine Learning — 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 whiteboard ready! 🚀🤖
