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AI ENGINEER ROADMAP

From ML Fundamentals to Production AI Systems

Level: Beginner → Advanced Duration: 6-12 Months Projects: 5+ Portfolio Projects Skills: ML, DL, LLMs, MLOps

LEARNING PATH

01

Foundations

Mathematics for ML (linear algebra, calculus, probability), Python programming, data manipulation with Pandas/Numpy, and basic statistics.

4-6 weeks

Linear Algebra Calculus Probability Python Pandas NumPy Statistics Data Visualization Environment Setup

02

Machine Learning

Supervised/unsupervised learning, model evaluation, feature engineering, and classic algorithms (regression, trees, SVMs, clustering).

4-6 weeks

Regression Classification Clustering Feature Eng Model Eval SVM Decision Trees Ensemble Methods Cross Validation

03

Deep Learning

Neural networks, CNNs, RNNs, transformers, and frameworks like TensorFlow/PyTorch.

6-8 weeks

Neural Nets CNNs RNNs Transformers PyTorch TensorFlow Keras Autoencoders GANs

04

MLOps & Production

Model deployment, monitoring, scaling, CI/CD for ML, and cloud AI services (AWS SageMaker, GCP AI, Azure ML).

4-6 weeks

Model Deploy Docker Kubernetes CI/CD ML Monitoring AWS SageMaker MLflow Model Versioning A/B Testing

05

LLMs & Advanced AI

Large language models, prompt engineering, fine-tuning, embeddings, and AI agents.

6-8 weeks

LLM Basics Prompt Eng Fine-tuning Embeddings LangChain Hugging Face Vector DBs AI Agents RLHF

RESOURCES & TOOLS

Foundations & Math

Essence of Linear Algebra

Beautiful visual explanations of vectors, matrices, and transformations.

Statistics & Probability

Complete free course covering all statistics needed for ML.

Interactive Course

Pandas Tutorial

Official pandas tutorials for data manipulation and analysis.

Learn array operations, broadcasting, and numerical computing.

Machine Learning

Scikit-learn Tutorial

Official guide to classic ML algorithms in Python.

Documentation/Tutorial

Machine Learning by Andrew Ng

Classic introduction to ML (note: uses Octave, but concepts are valuable).

Matrix Map for ML

Visual guide to understanding when to use different ML techniques.

Reference Guide

Deep Learning

PyTorch Tutorials

Official tutorials covering basics to advanced deep learning.

Tutorial Series

TensorFlow Tutorials

End-to-end examples for beginners and experts.

Tutorial Series

Deep Learning Book (Goodfellow et al.)

Free online version of the deep learning bible.

MLOps & Deployment

Free MLOps course covering deployment, monitoring, and scaling.

Comprehensive guide to MLOps practices and tools.

MLflow Tutorial

Track experiments, package code, and share models.

Specialized Courses

DeepLearning.AI Specializations

Andrew Ng's courses on neural networks, CNNs, NLP, and ML systems.

Machine Learning A-Z (Udemy)

Hands-on Python & R in data science.

Books

ML Interviews Book

By Eugene Chen - essential for AI engineering interviews.

LLMs from Scratch

Build a large language model from scratch with Raschka.

Portfolio Projects

Medical Diagnosis AI

Build a model to detect diseases from X-rays or MRI scans.

Computer Vision

Customer Support Chatbot

Create an LLM-powered chatbot for business support.

Stock Prediction System

Time series forecasting with LSTM and attention mechanisms.

ML Pipeline Automation

End-to-end automated ML pipeline with model monitoring.

Create an AI that learns to play games using reinforcement learning.

Reinforcement Learning

Quick References

ML Algorithms Cheatsheet

When to use which algorithm and their tradeoffs.

Deep Learning Cheatsheet

Layer types, activation functions, and architectures.

SQL for Data Science

Essential queries for data extraction and manipulation.

Linux Commands for ML

Essential shell commands for ML workflows.

YOUR PROGRESS

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