Start with AI foundations, then choose your path: Low-Code/No-Code tools mastery or Full-Stack AI Engineering
3 Modules
Foundation + 2 Tracks
Flexible Format
Long Island, NY or Online/Live Classes
Harvard Grad
Expert Trainers
Hands-On
Real Projects
Comprehensive AI/ML solutions for every need
Comprehensive AI/ML training from foundations to advanced engineering
Strategic AI implementation and transformation for enterprises
End-to-end AI application development and deployment
Start with foundations, then choose your specialization path
Master AI/ML concepts before diving into hands-on work
Topics:
Artificial Intelligence vs Machine Learning vs Deep Learning, NLP Basics, Introduction to Generative AI, Business Applications.
Key Focus:
Popular Tools:
Key Outcome:
Establish a strong foundation in AI and Generative AI concepts, understanding terminology and key business implications.
Topics:
Generative AI landscape, prompt engineering, context engineering, RAG overview, and AI accelerator tools.
Popular Generative AI Accelerator Tools (2025):
Key Outcome:
Get familiar with the Generative AI ecosystem and understand how accelerator tools enable rapid AI-driven creativity and productivity.
Topics:
Low-code/no-code AI platforms, workflow automation, and creative GenAI applications.
Popular Tools:
Key Outcome:
Gain hands-on familiarity with accessible AI platforms and creative tools for prototyping, automation, and design with minimal code.
Topics:
AI Agents & Agentic AI, Retrieval-Augmented Generation (RAG), AI Orchestration (LangChain, LangGraph, LlamaIndex), Guardrails, Inference & Optimization, Model Context Protocol (MCP), Agent-to-Agent (A2A).
Popular Tools:
Key Outcome:
Understand how modern AI systems are built, orchestrated, and governed — from RAG pipelines to agentic systems — using industry-grade developer and engineering tools.
Master Generative AI Tools Without Coding
Prerequisite: Completion of AI Overview Module (4 Weeks Foundation)
Topics: ChatGPT, Claude, Gemini, Perplexity basics; system vs user prompts; structured output design; prompt libraries.
Tools: ChatGPT, Gemini, Claude, Perplexity, PromptPerfect.
Outcome: Create high-quality prompts; compare model behaviors; produce repeatable and auditable outputs.
Topics: Text-to-Image and Text-to-Video tools; creative prompt structures; editing & remixing; licensing & ethics.
Tools: Midjourney, DALL·E, Adobe Firefly, Runway ML, Pika Labs, Leonardo AI.
Outcome: Design on-brand images and short videos using structured prompt patterns.
Topics: Workflow automation using triggers, actions, and APIs; connecting LLM steps via webhooks; end-to-end pipelines.
Tools: Zapier AI, Make (Integromat), n8n, Slack/Notion integrations.
Outcome: Build end-to-end no-code workflows that use AI for summarization, content generation, or tagging.
Topics: AI inside productivity suites; prompt templates for writing, summarization, and planning.
Tools: Microsoft Copilot (Word/Excel/Outlook), Google Workspace AI, Notion AI, Coda AI.
Outcome: Integrate AI into daily operations and create repeatable productivity templates.
Topics: Visual builders for chatbots and domain-specific agents; retrieval from docs; simple tools.
Tools: Flowise, LangFlow, Microsoft Copilot Studio, Cognosys AI Studio, Replit Agents.
Outcome: Deploy a knowledge-base chatbot using PDFs or URLs with web and calculator tools.
Topics: Data prep for LLMs; chunking basics; testing prompt accuracy; privacy and evaluation methods.
Tools: Excel/Sheets for data prep, PromptLayer, Promptfoo, Red-teaming frameworks.
Outcome: Improve response accuracy, reduce hallucinations, and ensure safe, private workflows.
Topics: End-to-end design and implementation of a real no-code AI workflow or app (e.g., marketing, HR, support).
Outcome: Deliver a working MVP integrating 2–3 AI tools (chat + automation + retrieval).
Topics: Project presentation, ROI evaluation, risk discussion, and lessons learned.
Outcome: Present the final solution, communicate impact, and document a reuse-ready runbook.
Build & Deploy Full-Stack AI Applications
Prerequisite: Completion of AI Overview Module (4 Weeks Foundation)
Setup, Git, Conda, NumPy/Pandas, scikit-learn refresher.
Outcome: Productive dev environment; implement a simple ML pipeline.
REST/GraphQL, async I/O, ETL patterns, cleaning.
Outcome: Build robust data loaders from APIs or local files.
Routes, Pydantic models, error handling, testing.
Outcome: Ship a clean, modular AI-ready REST API.
Schema design, SQLAlchemy ORM, CRUD ops, migrations.
Outcome: Persist users, sessions, configs, and logs.
OpenAI/HuggingFace embeddings, pgvector, Chroma, Pinecone.
Outcome: Implement semantic search over documents.
Chunking, retrieval, re-ranking, and context injection via LangChain/LlamaIndex.
Outcome: Ground LLM answers with retrieval and citations.
Chat UIs, streaming, state management, authentication flows.
Outcome: Build usable AI frontends with React + API integration.
Connect FastAPI + React; CORS, SSE/WebSockets, E2E testing.
Outcome: Deliver a full-stack, real-time AI chat interface.
Dockerfiles, multi-stage builds, environment secrets, Compose orchestration.
Outcome: Reproducible containers for every service.
Deployments, Services, Ingress, autoscaling, configs.
Outcome: Deploy scalable AI apps on Kubernetes.
EKS/GKE/Cloud Run, GitHub Actions pipelines, secrets management.
Outcome: One-click deploys to the cloud.
OpenTelemetry, Prometheus/Grafana, model registry, data drift detection.
Outcome: Monitor, alert, and iterate AI models and pipelines.
OAuth/JWT, rate limits, input validation, prompt-injection defense, PII handling.
Outcome: Build secure, policy-compliant AI systems.
Caching, batching, quantization, autoscaling strategies.
Outcome: Improve latency, throughput, and cost efficiency.
Architecture planning, milestones, and staging deploy.
Outcome: Deliver a production-grade RAG or agentic app.
Final deploy, load test, demo, and runbook documentation.
Outcome: Launch a cloud-ready, observable, maintainable AI product.
Build real projects and applications from day one
Personalized attention with limited class sizes
Industry-recognized certification upon completion
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
Our lead instructors are Harvard University graduates with specialization in Data Science and AI Engineering
Industry Experience
20+ years of combined experience in AI/ML product development at leading tech companies
Real-World Expertise
Currently leading AI transformation initiatives at Fortune 500 companies
Dedicated Support
Personalized mentorship and career guidance throughout your learning journey
Our Mission: Bridge the gap between academic theory and real-world AI implementation
Join from Long Island, NY or attend online • Live sessions • Recorded lectures
Monday - Thursday: 7:00 PM - 11:00 PM EST
Saturday & Sunday: 7:00 AM - 2:00 PM EST
✔ In-person option in Long Island, NY
✔ Online attendance available
✔ All sessions recorded for review
✔ Dedicated Slack community
12 Weeks Total
4 weeks foundation + 8 weeks hands-on
20 Weeks Total
4 weeks foundation + 16 weeks hands-on
Next Cohort Starts January 2026
Reserve Your SpotContact us for training enrollment or consultancy services
Response time: Within 24 hours
Contact us today to discuss your training needs or consultancy requirements. We'll help you choose the perfect path for your goals.
Next cohort starts soon • Limited seats available
Yes, the 4-week AI Overview module is mandatory for all students. It provides essential conceptual understanding needed for both the low-code and pro-code tracks.
You can decide on your track after completing the 4-week foundation course. However, once you begin either the low-code or pro-code track, we recommend completing it for the best learning experience.
No coding experience is required for the low-code/no-code track. It's designed to teach you how to leverage AI tools without programming.
Basic programming knowledge is helpful but not required. We'll teach Python from scratch in the pro-code track, though familiarity with any programming language will help you progress faster.
Yes! Students can flexibly switch between in-person attendance in Long Island, NY and online participation based on their schedule and preferences. All sessions are recorded regardless of format.
All sessions are recorded and available within 24 hours. You can watch at your convenience and ask questions in our community forum.
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.