Bootcamps
Structured learning paths with tutorials, videos, and hands-on assignments
ML Fundamentals Bootcamp: Housing Price Prediction
Master the core concepts of machine learning by building a housing price prediction model from scratch. This bootcamp covers supervised and unsupervised learning, model evaluation, and practical implementation with scikit-learn and Python — all through a hands-on housing dataset scenario.
RAG Bootcamp: Legal Document Q&A System
Master Retrieval-Augmented Generation by building a legal document Q&A system from scratch. Across 14 video lessons with bilingual content (English + 中文), you'll ingest legal contracts, chunk and embed them, and deploy a production RAG pipeline that answers natural-language questions about your documents.
Palantir Hands-On Bootcamp: Property Maintenance Operations
Build an AI-powered property maintenance scheduling system from scratch using Palantir Foundry. In 47 lessons spanning a Foundation Track and an Advanced Track, you will upload data, build pipelines, model an Ontology, create Workshop dashboards, deploy AIP agents, build external apps with OSDK, and tie it all together with predictive maintenance — covering the full Foundry stack with bilingual content (English + 中文).
Terraform Bootcamp: AWS Infrastructure for Web Apps & ML/AI
Build production-ready AWS infrastructure from scratch using Terraform. In 12 hands-on lessons, deploy a complete web application stack — VPC, EC2, RDS, S3, IAM — then extend it with ML/AI infrastructure including SageMaker, GPU instances, and model serving endpoints. Bilingual content (English + 中文). No prior Terraform or cloud experience required.
MCP Bootcamp: Build an AI-Powered Customer Support Agent
Master the Model Context Protocol by building a complete AI customer support system. In 13 hands-on lessons, you'll create MCP servers for ticketing, knowledge base search, CRM integration, and email — then orchestrate them into one intelligent support agent powered by Claude. Bilingual content (English + 中文).
AI Chatbot Bootcamp
Build a grounded conversational AI chatbot from scratch. Over ~25 hands-on lessons you'll create 'Atlas' — an assistant that answers your team's questions from the codebase and docs with real citations, remembers the conversation, runs in a web UI and in Slack, and is protected by guardrails and evaluation. Teaches RAG end to end, plus the chatbot-specific craft: memory, persona/system design, streaming, chat UX, and deployment. Part 1 of a path — the AI Agents Bootcamp is Part 2, where Atlas learns to take actions.
AI Agents Bootcamp
Build Atlas — an autonomous AI agent that answers your team's questions from the codebase (the source of truth) plus Foundry, Salesforce, Confluence and observability data, then triages issues on Slack and email, files and prioritizes Jira tickets, reports a daily digest, and escalates only what it can't handle. 45 hands-on lessons covering agent architecture, RAG over code, MCP integrations, evaluation, and production deployment — built on the Claude Agent SDK with LangGraph and OpenAI Agents SDK comparisons. Part 2 of a path: the AI Chatbot Bootcamp (Part 1) builds the grounded conversational foundation, and here the same Atlas learns to take action.
TypeScript for Foundry: From First Script to OSDK
Learn TypeScript from the ground up — variables, types, functions, classes, generics, async/await — then put it to work in Palantir Foundry: typed OSDK queries and actions, Code Repository Functions you actually test and invoke, and how to debug all of it. Act 4 takes that code to production: types and Zod validation at the boundary, a production Foundry client with retries and error classes, safe idempotent writes, querying at scale, testing, shipping a Node service, and a capstone Next.js app on the OSDK. No TypeScript experience required. Some Palantir Foundry background is assumed — if Foundry itself is new to you, start with the Palantir Hands-On Bootcamp first. Bilingual (English + 中文).
Jev × Foundry: Calibrated AI Decisions on the Ontology
Jev (TypeSafe AI, Sept 2026) is a System One model: it returns typed answers with calibrated probabilities instead of generated text. Learn how it works, how to design questions it answers well, how to earn confidence thresholds on your own data, and how to wire it into Palantir Foundry — Ontology in, Action out, with a Workshop review queue — ending with a technician-dispatch capstone. Written against the official jev-1.13.0 docs while Jev is in early access; assumes the Palantir and TypeScript for Foundry bootcamps. Bilingual slides and notes (English + 中文).
AI Service Assistant: Build a Production AI App with Bedrock, Salesforce & Foundry
Build one realistic AI application from an empty folder to production: a service assistant that turns a customer's chat and photos into a triaged service case and a booked technician, for a fictional commercial kitchen-equipment maker. Beginner to advanced across 12 modules: Next.js, the Vercel AI SDK and Claude on Amazon Bedrock, Postgres, Redis and S3, Salesforce, Palantir Foundry, event-driven integration on AWS, LLM evals and server-side guardrails, CDK and CI/CD, and production monitoring, plus the engineering lessons real teams learned the hard way. Bilingual slides and notes (English + 中文).
AWS Cloud Bootcamp: Build and Run a Real App on AWS, from First Account to Production
Build one realistic application on AWS from an empty account to production: TrailPost, a fictional outdoor-gear rental marketplace with customers, partner shops and ops staff. Beginner to advanced across 12 modules and 64 lessons: a safe account and budgets, IAM, the CLI and SDK, CDK in TypeScript, S3, DynamoDB and Aurora, Lambda, API Gateway and Cognito, SQS, SNS, EventBridge and Step Functions, containers on Fargate, networking and WAF, security and governance, CI/CD with OIDC, observability, data and analytics, AI with Bedrock, and cost engineering, plus the mistakes real teams made on the way. Designed for one AWS Free-plan account. Bilingual slides and notes (English + 中文).