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AI / ML Engineering

AI / ML Engineering

From data science basics to deploying LLMs in production

180h total9 courses4 stages
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What you'll be able to do

  • Train and evaluate machine-learning models
  • Work with data using NumPy, pandas, and scikit-learn
  • Build and fine-tune neural networks
  • Deploy a model behind an API

Before you start

  • Python fundamentals
  • High-school level math (algebra); some statistics helps
  • Curiosity about data and models

Level 1 ·Math & Python Foundations

Python for Data Science

beginner20h

NumPy, Pandas, Matplotlib: the data stack every ML engineer uses daily.

  • NumPy array operations & broadcasting
  • Pandas groupby & merge
  • Matplotlib + Seaborn EDA plots
  • EDA on a real Kaggle dataset

Linear Algebra & Probability for ML

beginner18h

Vectors, matrices, dot products, probability distributions: the math underneath every model.

  • Matrix multiplication by hand & in NumPy
  • Bayes theorem applied to a real problem
  • Gradient descent visualised

Level 2 ·Classical Machine Learning

ML Fundamentals with Scikit-learn

intermediate24h

Regression, classification, clustering, model evaluation, and feature engineering.

  • Train/val/test split strategy
  • Cross-validation & hyperparameter tuning
  • Feature importance & selection
  • Titanic Kaggle competition (top 20%)

Feature Engineering & Data Pipelines

intermediate14h

Imputation, encoding, scaling, and building reproducible sklearn pipelines.

  • Pipeline with ColumnTransformer
  • Target encoding vs. one-hot
  • Outlier detection & treatment

Level 3 ·Deep Learning & LLMs

Deep Learning with PyTorch

advanced28h

Neural networks, CNNs, RNNs, training loops, and GPU acceleration.

  • Train a CNN on CIFAR-10
  • Custom training loop with gradient clipping
  • Transfer learning with ResNet

HuggingFace Transformers & Fine-tuning

advanced22h

Use pre-trained LLMs, fine-tune with LoRA/PEFT, and build text/embedding pipelines.

  • Text classification with BERT
  • Fine-tune with LoRA on a custom dataset
  • Sentence embeddings with sentence-transformers

RAG Systems & LangChain

advanced18h

Retrieval-augmented generation: vector stores, embeddings, and multi-step chains.

  • Document ingestion + chunking pipeline
  • Semantic search with pgvector
  • RAG chatbot over custom docs

Level 4 ·MLOps & Deployment

FastAPI for ML APIs

advanced12h

Serve ML models as REST APIs with FastAPI, async loading, and background tasks.

  • Model loading on startup (lifespan)
  • Async prediction endpoint
  • Response validation with Pydantic

MLflow, Modal & Model Deployment

advanced14h

Experiment tracking, model registry, and serverless GPU deployment with Modal.

  • Log experiments + compare runs in MLflow UI
  • Deploy model to Modal serverless GPU
  • Gradio demo on HuggingFace Spaces

Frequently asked

Is the AI / ML Engineering roadmap free?+

Yes. The entire AI / ML Engineering roadmap and every curated resource is free to follow on Commit. You can track your progress, keep a daily streak, and earn a shareable certificate at no cost — there is no paywall.

How long does the AI / ML Engineering roadmap take to complete?+

About 180 hours of focused study across 9 courses and 4 stages. At roughly one hour a day that is about 6 months; you can move faster by studying more each day.

Do I get a certificate for finishing the AI / ML Engineering roadmap?+

Yes. When you complete the roadmap on Commit you receive a verifiable certificate of completion that you can add to LinkedIn and your public Commit profile as proof of what you finished.

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