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Intro

Let's discover the End-to-End Machine Learning Cycle.

What is End-to-End Machine Learning?​

End-to-End Machine Learning refers to the complete process of building, deploying, and maintaining a machine learning model. It encompasses everything from data collection and preparation to model deployment and monitoring.

Key Stages​

  1. Data Preparation: Exploratory data analysis and data cleaning
  2. Modeling: Feature engineering, model training, and evaluation
  3. Deployment: Containerization, CI/CD, and serving models
  4. Monitoring: Performance visualization, data drift detection, and feedback loops

In this documentation, we'll explore each of these stages in detail, providing you with a comprehensive understanding of the end-to-end machine learning process.

Why is End-to-End Machine Learning Important?​

Understanding the entire lifecycle of a machine learning project is crucial for several reasons:

  1. It ensures that your models are not just accurate, but also practical and maintainable in real-world scenarios.
  2. It helps you anticipate and address challenges at each stage of the process.
  3. It enables you to create more robust and reliable machine learning systems.

Let's dive in and explore each stage of the end-to-end machine learning cycle!