ML Engineer

An engineering-focused learning path for ML engineers covering ML fundamentals, model training, MLOps, deployment, monitoring, and production systems for reliable ML at scale.

1. 1

   ML Fundamentals and Mathematics

   Master linear algebra, calculus, and ML theory foundations

   Completion criteria: Implement gradient descent from scratch for linear regression

   Next step: Learn Python ML libraries

   ⏱ 14h

2. 2

   Python ML Libraries

   Master NumPy, Pandas, scikit-learn for ML workflows

   Prerequisites: mle-01

   Completion criteria: Build an end-to-end ML pipeline with scikit-learn

   Next step: Study deep learning frameworks

   ⏱ 12h

3. 3

   Deep Learning with PyTorch

   Build and train neural networks with PyTorch

   Prerequisites: mle-02

   Completion criteria: Train a CNN for image classification with PyTorch

   Next step: Learn model optimization techniques

   ⏱ 16h

4. 4

   Model Optimization and Tuning

   Optimize models with regularization, dropout, and hyperparameter tuning

   Prerequisites: mle-03

   Completion criteria: Improve model accuracy through systematic optimization

   Next step: Study feature engineering pipelines

   ⏱ 12h

5. 5

   Feature Engineering Pipelines

   Build reproducible feature engineering and data pipelines

   Prerequisites: mle-04

   Completion criteria: Create a versioned feature pipeline with feature store

   Next step: Learn model versioning and registry

   ⏱ 10h

6. 6

   Model Versioning and Registry

   Manage model versions, artifacts, and registries with MLflow

   Prerequisites: mle-05

   Completion criteria: Set up MLflow tracking and model registry

   Next step: Study model deployment

   ⏱ 10h

7. 7

   Model Deployment and Serving

   Deploy models as APIs with FastAPI, TorchServe, or KServe

   Prerequisites: mle-06

   Completion criteria: Deploy a trained model as a scalable inference API

   Next step: Learn model monitoring

   ⏱ 12h

8. 8

   Model Monitoring and Drift Detection

   Monitor model performance and detect data/concept drift

   Prerequisites: mle-07

   Completion criteria: Set up drift detection and alerting for a deployed model

   Next step: Study MLOps and CI/CD

   ⏱ 10h

9. 9

   MLOps and CI/CD for ML

   Build CI/CD pipelines for ML training and deployment

   Prerequisites: mle-08

   Completion criteria: Create an automated ML pipeline from training to deployment

   Next step: Learn distributed training

   ⏱ 12h

10. 10

    Distributed Training

    Train models at scale with distributed computing frameworks

    Prerequisites: mle-09

    Completion criteria: Set up distributed training across multiple GPUs

    Next step: Study special topics in ML

    ⏱ 12h

11. 11

    Special Topics: LLMs and Transformers

    Work with large language models and transformer architectures

    Prerequisites: mle-10

    Completion criteria: Fine-tune a pre-trained transformer model for a task

    Next step: Build a capstone ML engineering project

    ⏱ 14h

12. 12

    Capstone ML Engineering Project

    Build a complete ML system from training to production monitoring

    Prerequisites: mle-11

    Completion criteria: Deliver a production ML system with full MLOps pipeline

    Next step: Specialize in NLP, CV, or recommender systems

    ⏱ 16h