Machine Learning Engineering

Welcome to the ML Engineering documentation. This section covers the fundamental concepts, techniques, and best practices for developing machine learning solutions.

What is Machine Learning Engineering?

Machine Learning Engineering combines software engineering with data science to build and deploy machine learning systems. ML engineers design, build, and maintain the infrastructure for data collection, model training, and deployment.

Core Areas

Machine learning can be categorized into three main paradigms:

• Supervised Learning: Learning from labeled data
• Unsupervised Learning: Finding patterns in unlabeled data
• Reinforcement Learning: Learning through interaction with an environment

Getting Started

Browse the sections in the navigation to learn more about specific ML engineering topics. Each section provides detailed explanations, code examples, and best practices.

If you're new to machine learning, we recommend starting with the Supervised Learning section, as it introduces the foundational concepts that apply across all ML domains.

def function():
    print("hello, world")

npx create-next-app@latest my-next-app