INFERENCELAB
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Engineering education

Developing deployment-ready Applied AI Engineers.

A complete, deployment-focused curriculum across 6 phases. Every concept is taught through live coding, every week ends with a GitHub submission, and every phase ends with a capstone that proves you can build the real thing.

12.5 months 6 phases Sat + Sun live · 1.5h
Every week, without exception

The session structure.

Saturday introduces a new concept with a live coding walkthrough. Sunday is a deeper dive into edge cases where you attempt problems and we debug together. Monday–Friday is self-paced assignment work, submitted on GitHub by Friday night.

  1. 0:00–0:10Recap + last week’s assignment review
  2. 0:10–0:55Core concept teaching with live coding
  3. 0:55–1:10Guided exercise — code alongside the mentor
  4. 1:10–1:30Assignment briefing + Q&A
Phase 02 months

Engineering Foundations

From a working terminal to production database architecture. Writing Python that does not break.

Capstone

Student Record Management System on PostgreSQL with full CRUD.

  1. W01Developer Environment & Professional Python Setup
  2. W02Python Beyond Basics: Code That Doesn't Break
  3. W03File I/O: Reading and Writing Everything
  4. W04Git & GitHub: Engineering Collaboration
  5. W05Clean Code & Project Architecture
  6. W06Object Oriented Programming for Engineers
  7. W07SQL & SQLite: Databases From First Principles
  8. W08PostgreSQL & SQLAlchemy: Production Databases
Phase 12 months

Data Engineering & Visualization

NumPy through publication-quality visualization, grounded in real statistical thinking.

Capstone

Full data analysis project — EDA, statistical tests, PostgreSQL storage, notebook report.

  1. W01NumPy: How Computers Actually Handle Numbers
  2. W02Pandas Part 1: Data Loading & Cleaning
  3. W03Pandas Part 2: Transformation & Analysis
  4. W04Matplotlib: Visualization From First Principles
  5. W05Descriptive Statistics & Probability
  6. W06Inferential Statistics & Hypothesis Testing
  7. W07Correlation, Regression & Statistical Thinking
  8. W08Seaborn & Publication-Quality Visualization
Phase 22.5 months

Machine Learning Engineering

Classical ML the engineering way: pipelines, leakage-free evaluation, and experiment tracking.

Capstone

End-to-end ML project with CV, tuning, MLflow tracking, and a deployable predict() function.

  1. W01The ML Mindset & Scikit-learn Architecture
  2. W02Supervised Learning: Classification
  3. W03Regression & Feature Engineering
  4. W04Evaluation, Cross-Validation & Tuning
  5. W05Unsupervised Learning & Dimensionality Reduction
  6. W06Imbalanced Data & Production-Ready ML Code
  7. W07MLflow: Experiment Tracking & Model Registry
Phase 32.5 months

Deep Learning & NLP

Neural networks from scratch to fine-tuned Transformers and speech AI pipelines.

Capstone

Complete NLP system with a fine-tuned Transformer pushed to the HuggingFace Hub.

  1. W01Neural Networks From Scratch
  2. W02PyTorch: The Engineering Way
  3. W03CNNs & Computer Vision Basics
  4. W04Recurrent Networks & Sequence Modeling
  5. W05Text Processing & Classical NLP
  6. W06Transformers: Architecture & Intuition
  7. W07HuggingFace: Transformers in Practice
  8. W08Speech AI Engineering
Phase 42 months

AI Systems & LLM Engineering

Production AI APIs, disciplined LLM engineering, RAG systems, and agents that actually work.

Capstone

Complete AI application — RAG backend, LLM generation, FastAPI, frontend, deployed to cloud.

  1. W01Building Production AI APIs with FastAPI
  2. W02LLM Engineering: OpenAI & Anthropic APIs
  3. W03Vector Databases & RAG Systems
  4. W04AI Agents & Tool Use
Phase 51.5 months

MLOps & Deployment

Containerize, automate, ship and monitor. The difference between a notebook and a product.

Capstone

Dockerized, CI/CD-driven deployment of a monitored production AI service.

  1. W01Docker for AI Systems
  2. W02GitHub Actions: CI/CD for AI Projects
  3. W03Cloud Deployment & Monitoring

Ready to build, deploy, and maintain real AI systems?

The first online cohort is live. Join the next intake or apply for the Engineering Fellowship.

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