Machine Learning Engineer Roadmap

The path to take Artificial Intelligence (AI) and Machine Learning (ML) models from the research phase to real-world operation.

🧭 Overview: Who is a Machine Learning Engineer?

A Machine Learning Engineer (MLE) is the bridge between a Data Scientist and a Software Engineer. Their main task is to deploy, optimize, and maintain ML models in a production environment, ensuring they operate efficiently, stably, and scalably.

Roadmap by Stages

Stage 1: Math & Programming Fundamentals 0-6 months

Goal: Build a solid foundation

Stage 2: Classical Machine Learning 6-12 months

Goal: Understand and apply basic algorithms
  • Types of Machine Learning: Clearly understand Supervised, Unsupervised, and Reinforcement Learning.
  • Common Algorithms: Linear Regression, Logistic Regression, SVM, Decision Trees, K-Means Clustering.
  • Scikit-learn Library: Proficiently use Scikit-learn to build, train, and evaluate models.
  • Feature Engineering: Learn techniques for selecting and creating the best features for the model.

Stage 3: Deep Learning 1-2 years

Goal: Build complex neural network models
  • Neural Networks: Understand the structure and operation of artificial neural networks.
  • Frameworks: Master one of the two main frameworks: TensorFlow (with Keras) or PyTorch.
  • Common Architectures: Learn about Convolutional Neural Networks (CNN) for computer vision and Recurrent Neural Networks (RNN/LSTM) for natural language processing (NLP).

Stage 4: Deployment & Operations (MLOps)2-3 years

Goal: Put models into a production environment
  • API for Models: Package models into an API using Flask or FastAPI.
  • Containerization: Use Docker to package the application and model, ensuring consistency.
  • ML Lifecycle Management: Use tools like MLflow to track experiments, manage models, and reproduce results.
  • CI/CD for ML: Build an automated pipeline for training, testing, and deploying models with GitHub Actions.
  • Cloud Platforms: Get familiar with ML services on AWS (SageMaker), GCP (Vertex AI), or Azure ML.

Stage 5: Optimization & Scaling 3+ years

Goal: Build high-load ML systems
  • Big Data Processing: Use Spark (PySpark) to process and train models on large datasets.
  • Model Monitoring: Track performance, detect "model drift" (the degradation of model quality over time).
  • Inference Optimization: Speed up model prediction using techniques like quantization, pruning, or using TensorRT/ONNX.
  • ML System Architecture: Design scalable, fault-tolerant, and efficient ML systems.

🧩 Specialization Paths

Computer Vision Engineer

Specializes in image and video processing problems, such as object recognition, image classification.

NLP Engineer

Focuses on language processing problems, such as chatbots, sentiment analysis, machine translation.

MLOps Engineer

Specializes in building infrastructure and automated pipelines for the ML model lifecycle.

Research Scientist

Focuses on researching and developing new algorithms and model architectures.