5 Weeks · No Coding
Learning, Using, Building, Scaling, and Optimizing AI.
8 Weeks · Claude Cowork & Plugins
Hands-on with Claude Cowork, skills, connectors & no-code agents.
16 Weeks · Claude Code & Production Stack
Build with Claude Code, LangChain, NVIDIA vLLM, Cloud & K8s.
1 Foundation
+ Choose Your Track
Fully Online
Live Classes + Recorded
Harvard Grad
Expert Trainers
Hands-On
Real Projects
Start with Everyone AI Foundation → Then Choose: Embedded AI or Embrace AI
Learn every AI buzzword. Understand, use, build, scale, and optimize AI. Required before either track.
Topics: AI, ML, DL, GenAI, LLMs, Transformers, Encoder-Decoder, Embeddings, Multimodal AI, Training vs Inference, Reasoning Models, Agents & MCP. Tokens & context windows. Self-attention & multi-head attention. Three types of language models. Parameters.
Tools: ChatGPT, Claude, Gemini, Perplexity AI
Outcome: Understand every core AI concept — from tokens to transformers to agents.
Topics: Prompt Engineering, Context Engineering, Models (Claude 4.6, Gemini 3.1, GPT-5, DeepSeek R1, LLaMA 4), APIs, Model Pricing, Hugging Face, Vibe Coding, Claude Code & Cowork, Computer Use & Browser Agents. Proprietary vs open source. Multimodal AI: images, video, voice, music.
Tools: ChatGPT, Claude, Gemini, Midjourney, Hugging Face, Claude Code, Cowork
Outcome: Navigate the full AI ecosystem — know what models exist, how to prompt them, and how to use AI tools hands-on.
Topics: RAG Pipelines, Text to SQL, AI Orchestration (LangChain, Google ADK, CrewAI), Agents, Benchmarks (MMLU, MTEB, SWE-bench, RAGAS, Chatbot Arena), AI Safety and Guardrails. Copyright & legal. Function calling & tool use.
Tools: Claude Code, Cursor, Flowise, Dify
Outcome: Understand RAG, agents, orchestration, and the safety guardrails that matter.
Topics: GPUs (A100 → H100 → Blackwell Ultra), NVIDIA, Cloud (AWS, Azure, GCP), Kubernetes, Running AI Locally (Ollama, LM Studio, GPT4All), Hosting Open Source Models (vLLM, TGI), Inference Servers (NVIDIA Triton, TensorRT-LLM), Model Deployment, Real-World Applications, Scale of Modern AI.
Tools: Ollama, LM Studio, vLLM
Outcome: Understand the full deployment landscape — from local to cloud to enterprise-scale inference.
Topics: Fine-Tuning (SFT, RLHF), Quantization (GPTQ, AWQ, GGUF), Optimization techniques, Hyperparameters (temperature, top-k, top-p), GPU Optimization (mixed precision, batching, KV caching). When to fine-tune vs RAG.
Tools: Hugging Face Transformers, PEFT/LoRA, Ollama quantized models
Outcome: Know when and how to optimize AI models for cost, speed, and quality.
Pick the track that matches your goals. No need to do both.
Master Claude Cowork, Plugins, Skills & No-Code Agents
Prerequisite: Everyone AI Foundation (5 Weeks)
Topics: Mastering Claude chat for work. Prompt engineering specific to Claude. Artifacts: building interactive dashboards, tools, games — sharing them. Deep Research for long-form analysis. Custom inline charts & visualizations. Projects for organizing work. Models overview: Opus 4.6 vs Sonnet 4.6 vs Haiku 4.5 — when to use which.
Outcome: Build and share 3 artifacts (a dashboard, an interactive tool, and a research report).
Topics: The Chat → Code → Cowork evolution. Setting up Cowork on desktop (macOS/Windows). Folder permissions & access controls. The task loop: describe goal → Claude plans → you steer → deliverable. File reading, editing, creation. Report assembly, expense tracking from receipt photos, file organization & renaming. Customize section in Desktop.
Outcome: Complete 5+ real tasks end-to-end using Claude Cowork.
Topics: MCP Connectors: Google Drive, Slack, Gmail, Zoom, Notion. Claude in Chrome: browser reading, clicking, form-filling alongside Cowork. Plugin marketplace: installing, configuring, bundling. Agent Skills: pre-built (docx, pptx, xlsx, pdf). Creating & uploading custom skills. Enterprise private marketplace. Building specialist agents with plugin bundles (skills + connectors + sub-agents).
Outcome: Set up Cowork with 3+ connectors & create your first custom skill.
Topics: Computer Use in Cowork: Claude opens apps, clicks through UI, navigates your screen. Scheduled/recurring tasks: daily email check, weekly metrics pull, Friday digest. Persistent agent thread: assign tasks from mobile. On-demand vs recurring task design. Building always-on workflows.
Outcome: Set up 3 automated workflows (daily, weekly, event-triggered) using Cowork + computer use.
Topics: Claude for Excel add-in: data analysis, formulas, pivot tables, charts, cleaning messy data. Claude for PowerPoint add-in: slide creation, design, formatting from raw notes. Shared context between Excel & PowerPoint. End-to-end: data in Excel → analysis → presentation in PowerPoint, all AI-assisted.
Outcome: Build a complete data-to-presentation pipeline using Claude add-ins.
Topics: Claude Code for non-developers: describe what you want → get working software. VS Code + Claude Code extension setup. Building personal tools, simple web apps, internal dashboards. Vibe coding workflow: iterate by describing changes in plain English. Key difference from Embrace AI: here you use Claude Code as a tool, in Embrace you engineer production systems.
Outcome: Build a working personal tool or internal app using Claude Code without writing code manually.
Topics: End-to-end design and implementation using Claude products. Example projects: "Build a weekly reporting workflow with Cowork + Excel + PowerPoint", "Create a research digest using scheduled tasks + Chrome + artifacts", "Build a domain-specific plugin bundle for your team", "Vibe-code a custom internal tool with Claude Code".
Outcome: Deliver a working MVP combining 3+ Claude products.
Topics: Project presentation, ROI evaluation, risk discussion, lessons learned.
Outcome: Present final solution, document a reuse-ready runbook. Certificate ceremony.
Build, Deploy & Scale Full-Stack AI Apps with Claude Code
Prerequisite: Everyone AI Foundation (5 Weeks)
Topics: VS Code setup, Git, Conda. Claude Code: terminal-based agentic coding, CLAUDE.md project files, skills system (SKILL.md format), bundled skills (/simplify, /batch, /debug), subagent architecture (Explore, Plan agents). Python refresher: NumPy, Pandas, scikit-learn. Vibe coding workflow.
Outcome: Build a simple ML pipeline using Claude Code with vibe coding.
Topics: OpenAI API deep dive (chat completions, structured outputs, function calling — the universal baseline). Anthropic SDK (Claude tool use, prompt caching, streaming, extended thinking). Gemini API overview. REST/GraphQL, async I/O. SSE streaming responses. Building your first MCP server.
Outcome: Build data loaders + a multi-provider function-calling demo.
Topics: LangChain: chains, prompts, output parsers, tool calling, document loaders. LangGraph: stateful graphs, nodes, edges, conditional routing, checkpointing, human-in-the-loop. LangSmith: tracing, debugging, evaluation, prompt versioning.
Outcome: Build a multi-step LangGraph agent with full tracing in LangSmith.
Topics: Routes, Pydantic models, error handling, testing. Streaming LLM responses (SSE patterns). Redis for caching & session state. Kong API gateway introduction.
Outcome: Ship a clean, modular AI-ready REST API with Redis caching.
Topics: PostgreSQL & SQLAlchemy ORM. pgvector for embedding storage & similarity search. Apache AGE for graph queries & knowledge graphs. Embedding model selection (MTEB leaderboard).
Outcome: Implement semantic search + graph-based knowledge retrieval.
Topics: Chunking strategies, retrieval, re-ranking (Cohere, cross-encoders). Build RAG with LangChain + pgvector. Agentic RAG with LangGraph. Evaluation with RAGAS + LangSmith.
Outcome: Build a production RAG system with citations.
Topics: Advanced LangGraph patterns: multi-agent, human-in-the-loop, tool orchestration. Claude Managed Agents API (sessions, environments, SSE events). Agent Skills authoring (SKILL.md). RAG + Agents integration. When to use LangGraph agents vs Managed Agents.
Outcome: Build a multi-agent system with RAG using LangGraph.
Topics: Chat UIs, streaming display with LangChain, state management, auth flows. Real-time AI response rendering.
Outcome: Build a real-time AI chat frontend.
Topics: FastAPI + React + PostgreSQL + Redis + agents. CORS, SSE/WebSockets, E2E testing. pytest for backend.
Outcome: Deliver a working full-stack AI app with RAG & agents, fully tested.
Topics: GPU fundamentals (A100 → H100 → Blackwell). CUDA basics. PyTorch model loading & inference. Mixed precision, KV caching, batching. NVIDIA ecosystem overview: NeMo, Triton, TensorRT-LLM.
Outcome: Run PyTorch inference on GPU, benchmark performance.
Topics: vLLM: serving LLaMA, Mistral, Qwen, Phi. Benchmarking throughput & latency. NVIDIA TensorRT-LLM: optimized inference engine. NVIDIA Triton Inference Server: production serving, multi-model, dynamic batching. vLLM vs TensorRT-LLM comparison.
Outcome: Serve the same model on vLLM and TensorRT-LLM, compare performance.
Topics: When to fine-tune vs RAG vs prompt engineering (decision framework). Fine-tuning small language models: Qwen 7B, NVIDIA Nemotron, Mistral 7B, Phi-3, LLaMA 8B. LoRA & QLoRA with PEFT/Hugging Face. Dataset preparation & formatting. NVIDIA NeMo Framework for fine-tuning. MLflow for experiment tracking & model registry. Deploy your fine-tuned model on vLLM.
Outcome: Fine-tune a small model on custom data, evaluate, and serve it.
Topics: Dockerfiles, multi-stage builds, Compose, Vault for secrets. Containerize full stack + model serving. Cloud AI services overview (Vertex AI, SageMaker, Azure AI). Cloud-managed K8s, PostgreSQL, Redis. Deploy to cloud.
Outcome: Full stack containerized and deployed to cloud.
Topics: Managed K8s setup, deployments, services, ingress. GPU node pools for model serving. ArgoCD for GitOps. Terraform for IaC. GitHub Actions CI/CD (lint → test → build → deploy).
Outcome: Full CI/CD: push → test → build → ArgoCD deploys to K8s.
Topics: NeMo Guardrails: input/output rails, topical rails. Kong rate limiting & auth. OAuth/JWT. Prompt injection defense. LangSmith + OpenTelemetry monitoring in production.
Outcome: Harden and monitor your production AI app.
Topics: Architecture planning, build sprint, staging deploy. Final deploy, load test, demo. Runbook documentation.
Outcome: Launch a production-grade, cloud-ready AI product. Certificate ceremony.
Build real projects and applications from day one
Personalized attention with limited class sizes
Industry-recognized certification upon completion
Comprehensive AI/ML solutions for every need
Comprehensive AI training: foundation + your choice of track
Strategic AI implementation and transformation for enterprises
End-to-end AI application development and deployment
Transform your business with strategic AI implementation
We help organizations leverage AI/ML technologies to drive innovation, improve efficiency, and gain competitive advantage. Our expert consultants bring years of experience in implementing AI solutions across industries.
Define your AI roadmap aligned with business objectives
Build tailored AI solutions for your specific needs
Seamlessly integrate AI into existing systems
Upskill your team and provide ongoing support
Ready to transform your business?
Schedule ConsultationWorld-class training from Harvard graduates and leading AI professionals
Expert Trainers
Harvard Graduates
Lead instructors with specialization in Data Science and AI Engineering
20+ Years Industry Experience
AI/ML product development at leading tech companies and Fortune 500s
Growing Team of Experts
Specialists across Claude, LangChain, NVIDIA, cloud engineering, and more
Classes fully online · Live sessions · All lectures recorded
Monday - Thursday: 7:00 PM - 11:00 PM EST
Saturday & Sunday: 7:00 AM - 2:00 PM EST
✔ Classes fully online — attend from anywhere
✔ Online attendance available
✔ All sessions recorded for review
✔ Dedicated Slack community
13 Weeks Total
5 weeks foundation + 8 weeks Track A
21 Weeks Total
5 weeks foundation + 16 weeks Track B
Next Cohort Starts Soon
Reserve Your SpotContact us for training enrollment or consultancy services
Response time: Within 24 hours
Next sessions starting July 2026 — Limited Seats! Contact us to discuss your training needs.
Next sessions starting July 2026 — Limited Seats! Reach out to block your spot NOW.
Everyone starts with the 5-week Everyone AI foundation (required). After that, you choose one track: Embedded AI (8 weeks, no-code, focused on the Claude ecosystem — Cowork, artifacts, plugins, Excel/PowerPoint, and vibe coding) or Embrace AI (16 weeks, full-stack engineering with Claude Code, LangChain, NVIDIA, SLM fine-tuning & cloud). You don't need to do both.
You can decide on your track after completing the 5-week foundation. However, once you begin either track, we recommend completing it for the best learning experience.
No coding experience is required. The Embedded AI track focuses entirely on the Claude ecosystem — Cowork, artifacts, plugins, skills, Excel/PowerPoint add-ins, and Claude Code as a vibe coding tool — all designed for non-developers.
Basic programming knowledge is helpful but not required. We'll teach Python from scratch, though familiarity with any programming language will help you progress faster.
The Embrace AI track covers cloud-agnostic concepts (Kubernetes, Terraform, Docker, CI/CD) with hands-on labs on a major cloud provider. The patterns you learn apply across AWS, GCP, and Azure.
All classes are fully online with live instruction. Attend from anywhere in the world. All sessions are recorded so you can review at your own pace.
Yes, we offer customized corporate training programs tailored to your organization's specific needs. Contact us for details.
Upon successful completion, you'll receive an industry-recognized certificate from AIMLEngineers that you can add to your LinkedIn profile and resume.