AWS AI Practitioner case studies
AWS AI Practitioner · Put your knowledge to work in context.
The AWS Certified AI Practitioner is an entry-level credential from Amazon Web Services that validates foundational knowledge of artificial intelligence, machine learning, and generative AI on AWS. It is designed for people who work with AI-enabled tools and services rather than build models from scratch - product managers, business analysts, sales and marketing staff, developers new to AI, and anyone translating AI capabilities into business outcomes. The AWS AI Practitioner case studies are 5 cases and about 2,605 words.
1The Recommendation Engine That Learned the Wrong Lesson
A retail team has trained a binary classifier on 18 months of purchase data to predict whether a customer will buy a premium subscription. The dataset contains 1,000,000 rows; only 2% of customers ever subscribed.
556 words, about 3 min2The Support Assistant That Invented a Refund Policy
A subscription software company deploys a generative AI assistant that answers customer questions using a foundation model with a retrieval layer over its help-center articles. In the first month, the assistant handles 40,000 conversations.
551 words, about 3 min3Choosing Between Fine-Tuning and Retrieval for a Legal Team
A legal department wants a generative AI system that drafts first-pass summaries of 200-page contracts. Requirements: summaries must cite the exact clause text they rely on; the firm's internal style guide must be followed; the model must not reveal one client's contract details when working on another's; and the system must run in the firm's AWS account with no data leaving its control.
486 words, about 2 min4The Hiring Screener Under Regulatory Scrutiny
A company uses a machine learning model to rank job applicants for interviews. The model was trained on five years of historical hiring decisions.
500 words, about 3 min5Guardrails Versus Access Control in a Multi-Tenant Assistant
A healthcare SaaS provider runs a generative AI assistant for clinic staff across 60 tenant organizations. Each tenant's patient notes are stored in its own S3 prefix and indexed into a shared vector store with tenant metadata.
512 words, about 3 min