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AWS AI Fundamentals Aurora and DynamoDB as feature and metadata stores

AWS AI Fundamentals

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M1 — What AI and ML Are and Where They Fit

Machine learning, deep learning and generative AI without the hype

Where AI creates value and where it destroys it

Supervised, unsupervised and reinforcement learning in plain terms

The training, inference and evaluation cycle

Why most AI projects fail before the model is chosen

Reading an AI vendor claim critically

M2 — The AWS AI Service Catalogue

Rekognition, Comprehend, Textract and Translate: vision, text and documents

Transcribe and Polly: speech in and speech out

Personalize and Forecast: recommendation and demand

SageMaker: when a managed notebook is not enough

Bedrock: foundation models behind a single API

Choosing a service against a written requirement

M3 — Working with Amazon Bedrock

Account setup, model access and regional availability

Making the first call through console and CLI

Model selection: capability, context length and cost

Inference parameters: temperature, top-p, stop sequences

Streaming responses and handling timeouts

Reading token accounting on a real bill

M4 — Data for AI on AWS

What data an AI use case needs before it is worth building

S3 as the landing zone for training and retrieval data

Aurora and DynamoDB as feature and metadata stores

Data quality, labelling and the cost of getting it wrong

Privacy, consent and DPDP obligations on Indian customer data

Documenting lineage so a result can be defended

M5 — Evaluation, Guardrails and Responsible AI

Building an evaluation set before building the feature

Measuring accuracy, refusal and hallucination rates

Guardrail models and content filtering in Bedrock

Bias testing and where it genuinely matters

Human review loops and escalation paths

Writing an AI system card a non-engineer can read

M6 — Cost, Capacity and Service Limits

Reading AI pricing: per call, per token, per minute

Provisioned throughput versus on-demand pricing

Service quotas and what happens when they are hit

Latency budgets and their effect on user experience

Building a monthly cost model for a real feature

Where AI spend usually runs away, and how to stop it

M7 — Use-Case Selection and Business Framing

Ranking candidate use cases by value and feasibility

Writing a one-page AI proposal that survives scrutiny

The make, buy and integrate decision

Risk classification of an AI use case

Stakeholder expectations versus model capability

Killing a bad AI idea early and cheaply

M8 — Capstone: An AI Feature End to End

Take a written brief from framing to a working Bedrock call

Add retrieval over a document set in S3

Install guardrails and an evaluation set

Produce a cost model and a monthly budget alert

Document the failure modes honestly

Present the feature and its risk position

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