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Human Infrastructurefor the AI Era

The curriculum for builders at the frontier. Learn what the industry actually needs — and get placed.

94%
Placement Rate
$185k
Avg. Starting Salary
4.9★
Student Rating
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01 — Problem

AI is moving.
Education isn't.

Artificial intelligence is reshaping industries faster than traditional education can adapt. While demand for AI-native talent continues to rise, most learning pathways remain theoretical, outdated, or fragmented. As a result, many capable individuals lack the practical experience needed to build and deploy real-world AI systems.

02 — Our Solution

Hands-on.
Real systems.
Real jobs.

Our academy provides a structured, hands-on pathway to reskill for the AI era. Through project-based learning, industry mentorship, and real-world deployment, participants gain practical experience building AI systems — not just studying them — and graduate prepared to contribute in an AI-driven economy.

Featured Programs

Three tracks. One mission.

From AI engineering fundamentals to solopreneur product launches — find the track that fits where you are right now.

Flagship Program

Machine Learning Engineer in the Generative AI Era

An intensive, career-ready machine learning engineering program — from fundamentals to production deployment, with direct referrals to 120+ AI companies.

PyTorchMLOpsLLMs & RAGVector DBsModel ServingCloud Deploy
15 WeeksLive CohortCertificateCareer Support
LIMITED TIME: LIVE LECTURES
Entry Program

Fundamentals of AI Engineering

The complete beginner-to-builder track. Certificate-backed with graded exams and live instruction.

PythonNeural NetsTransformersGraded Exams
Self-PacedLive LecturesCertificate~60 Hours
New Program

AI Solopreneur

Design, build, and launch a monetizable AI product in 8 weeks. No co-founder, no funding, no waiting — just you and your idea.

AI ProductsMonetizationDemo DayLaunch Strategy
8 WeeksProject-BasedLive CohortDemo Day
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Our Edge

Built different.

We obsess over outcomes, not just education.

Built for the engineers — and by the engineers — who ship real AI. Every pillar is designed to take you from learner to builder, fast.

How We Help05
01

Industry-Led Curriculum

Real production problems, not textbook theory. Every lesson is extracted from what top AI teams actually build.

02

Ship Real Projects

Deploy live RAG pipelines and fine-tuned models. Your portfolio proves you can build — not just talk about it.

03

Career Placement

Interview prep, resume sharpening, and a hiring partner network of 120+ AI-first companies.

04

Live Cohort Format

Weekly sessions, open office hours, and peer accountability that keeps you moving when motivation dips.

05

Cutting-Edge Stack

LangChain, vLLM, Pinecone, Ray — the exact tools appearing on job descriptions right now.

Meet TAi

Your AI coaching agent.

TAi doesn't just answer questions — it evaluates how you think, asks the questions back, and nudges you with targeted feedback that turns practice into real, lasting capability.

🎓

2,400+

Students Coached

💬

48K+

Questions Answered

<3s

Avg. Response Time

inference-academy — live session
Week 4 · Module 2

Building RAG Pipelines
with LangChain + Pinecone

from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings
 
# Initialize embeddings + vector store
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone.from_existing_index(
index_name="rag-pipeline",
embedding=embeddings
)
 
# Create a retrieval chain
retriever = vectorstore.as_retriever(
search_kwargs={"k": 4}
)
Live Question

How does .as_retriever() know which similarity metric to use?

Slide 4 / 1243:21
T

TAi — Coaching Agent

● responds instantly

T
Before I explain, I want to hear your intuition first 🤔 What do you think determines which document chunks the retriever actually returns?
Maybe similarity to the query? Like... some kind of score?
T
That's exactly the right instinct. Now — what kind of similarity is typically used here, and why does it handle documents of different lengths better than a raw dot product?
Cosine similarity? Because it normalizes for vector length?
T
You got it 🎯 Cosine similarity normalizes for magnitude, so a short and long doc can score equally if semantically aligned. Now — what does setting k=4 actually guarantee about the results you get back?
Ask TAi anything...Send ↵
Student Stories

Hear it from them.

Real outcomes from real engineers who committed, shipped, and landed.

Kevin Li

GenAI MLE

American Express

Cohort 1

I didn't want surface-level tutorials. I wanted real technical depth, projects I'd be proud to put on a resume, and mentorship that actually meant something. Ten weeks in, I wasn't just learning — I was shipping. Now I'm working in Gen AI at American Express and I genuinely couldn't have gotten here without this program.

Our Community

Built by builders.
For builders.

We believe the future of AI depends on well-trained, hands-on engineers. Join a growing network of aspiring and early-career AI builders.

Community

We believe the future of AI depends on well-trained, hands-on engineers. Our community brings together aspiring and early-career builders to learn, ship projects, and grow together through live workshops, competitions, and peer collaboration. For those seeking structured, high-impact training, our Academy programs offer a direct pathway to becoming industry-ready.

Join the Community
Begin Today

The future runs on
people who build it.

Take the first step toward becoming an AI engineer. Your journey starts here.