INFERENCELAB
Back home
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.This is a paid, limited-capacity mentorship engagement — fee details are shared directly during the application conversation.

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
Program investment

Pay as you progress.

Each phase is billed independently — you commit one phase at a time, not the full 12.5 months upfront. Fees are set per phase based on depth, tooling, and mentorship intensity. Continuation into the next phase is confirmed after capstone review.

All fees are in PKR. Payment plans are available — discussed during the application conversation.

  1. Phase 0
    2 months
    Engineering Foundations
    PKR 6,000/moPKR 12,000
  2. Phase 1
    2 months
    Data Engineering & Visualization
    PKR 6,000/moPKR 12,000
  3. Phase 2
    2.5 months
    Machine Learning Engineering
    PKR 8,000/moPKR 20,000
  4. Phase 3
    2.5 months
    Deep Learning & NLP
    PKR 8,000/moPKR 20,000
  5. Phase 4
    2 months
    AI Systems & LLM Engineering
    PKR 10,000/moPKR 20,000
  6. Phase 5
    1.5 months
    MLOps & Deployment
    PKR 10,000/moPKR 15,000
  7. Full programPKR 99,000

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.

Get in touchApply now