Chapter 53: AI/ML on AWS
AI & Machine Learning on AWS.
I will not throw 35 service names at you in the first minute like many videos do. Instead we will build the understanding step by step — like a real teacher who wants you to actually get it, not just memorize a list.
1. First — Very Honest Context: Where are we in 2026?
Right now (early 2026) almost every conversation about “AI on AWS” is actually about two completely different worlds that live side by side:
World A — Generative AI / Foundation Models / LLM era (what 85–90 % of people mean when they say “AI” in 2025–2026)
World B — Classic / Traditional Machine Learning (the world of fraud detection, recommendation systems, demand forecasting, churn prediction, computer vision, etc. — still extremely important, just less hyped right now)
Both worlds exist on AWS. Both are growing fast. But they use almost completely different services.
So let’s separate them clearly from the beginning.
2. World A — Generative AI / Foundation Models (The 2025–2026 Hype Layer)
This is the part everyone talks about: ChatGPT-style models, image generation, code assistants, voice cloning, RAG chatbots, etc.
| Flagship Service (2026) | What it actually is | What a normal person in Hyderabad uses it for right now | Pricing style (very rough) |
|---|---|---|---|
| Amazon Bedrock | Managed gateway to ~25 high-quality foundation models (Claude 3.5 Sonnet, Llama 3.1 405B, Mistral Large 2, Cohere Command R+, Stability SD3, Titan, Nova family, Jurassic-2, AI21 Jamba, etc.) | Telugu customer support chatbot, content generation (blog + social media), code assistant, document Q&A with RAG | Pay-per-token (input + output) |
| Amazon Q Developer | AI coding assistant + chat inside VS Code, JetBrains, AWS Console, Slack, etc. | “Write me a Lambda to resize images”, “explain this CDK code”, “convert this bash script to Python” | Included in AWS Free Tier + very cheap per query |
| Amazon Q Business | Enterprise ChatGPT — connects to your internal data (Confluence, SharePoint, S3, Google Drive, Jira, Gmail, Slack…) | HR asking “what is our 2026 leave policy?”, finance asking “show me last quarter Telangana revenue” | Per user/month (~₹3,000–5,000/user) |
| Amazon Q in QuickSight | Natural language questions on your dashboards & datasets | “Show me sales trend in Hyderabad last 90 days” → auto-generates chart | Included in QuickSight Pro |
| PartyRock | No-code playground to play with Bedrock models (very beginner-friendly) | Students & non-coders testing “generate Telugu wedding invitation poem” or “create cartoon version of my photo” | Free tier generous |
Most common 2026 Hyderabad startup pattern right now:
- Build a customer support / FAQ chatbot → Bedrock + Claude 3.5 Sonnet or Nova Pro + Knowledge Bases for Amazon Bedrock (managed RAG)
- Give developers Amazon Q Developer in VS Code → 30–50 % faster coding
- Give non-technical people (marketing, HR, ops) Amazon Q Business → they ask questions in natural language instead of opening 12 tabs
Monthly cost example (small–medium startup, 5–15 people actively using):
- Bedrock chatbot (5,000 conversations/day) → ₹4,000–18,000
- Amazon Q Developer (team of 8) → ₹0–2,000
- Amazon Q Business (5 users) → ₹15,000–25,000
- Total typical AI bill → ₹20,000–50,000/month (very manageable)
3. World B — Classic / Traditional Machine Learning (Still Extremely Important)
This is the older (but still massive) world: fraud detection, recommendation engines, demand forecasting, churn prediction, image classification, NLP without LLMs, etc.
| Flagship Service (2026) | What it really is | Typical Hyderabad use-case (2026) | Pricing style |
|---|---|---|---|
| Amazon SageMaker | Full end-to-end ML platform (notebooks, training, inference, pipelines, Studio, Autopilot, JumpStart…) | Build & deploy fraud detection, delivery time prediction, product recommendation | Pay for instances used |
| SageMaker JumpStart | 600+ pre-trained models + one-click solutions | Quick POC: “I want a Telugu sentiment analyzer” or “vehicle number plate detector” | Free tier + pay for inference |
| SageMaker Canvas | No-code ML for business analysts | Marketing person predicts “which customers will churn next month” without code | Per hour of usage |
| SageMaker Clarify | Bias detection & model explainability | Fintech / lending company must prove model is not biased by gender/caste/region | Pay for processing time |
Real example – 2026 Hyderabad logistics / delivery startup
They want to predict delivery time for every pin-code in Telangana + Andhra.
- Use SageMaker Studio (JupyterLab environment)
- Load historical data from S3 (order timestamp, pin-code, traffic, weather, vehicle type…)
- Train XGBoost / DeepAR model with SageMaker built-in algorithms
- Deploy model as SageMaker endpoint
- Lambda / ECS service calls the 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) = ₹5,000–15,000
4. Quick Decision Tree – Where Should You Start in 2026?
- Do you want to prompt large language models (ChatGPT-style) or generate images/code/content? → Start with 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 (Confluence, Google Drive, S3, Jira…)? → 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 Table – AWS AI/ML Quick Map (Feb 2026 – India Focus)
| You want to… | First Choice Service(s) | Why this one in Hyderabad 2026? |
|---|---|---|
| Build chatbot / content generation / RAG | Amazon Bedrock + Knowledge Bases | Access to Claude 3.5, Llama 3.1, Nova — Telugu support |
| Get coding help / explain AWS resources | Amazon Q Developer | Integrated in VS Code & AWS Console — huge time-saver |
| Chat on internal company documents | Amazon Q Business | Connects to Confluence, SharePoint, S3, Gmail, Slack |
| Train your own ML model (fraud, recommendation) | 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 |
| Transcribe calls / voice notes | Transcribe | Telugu support + speaker separation |
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! 🚀🤖
