Made-With-ML

Introducing Made-With-ML

Overview

Made-With-ML is an educational initiative that guides developers through the comprehensive process of delivering machine learning (ML) applications. It emphasizes the principles of designing, developing, deploying, and iterating to create reliable production-grade ML systems. Used by over 40,000 developers, the project aims to illustrate the responsible delivery of value with machine learning technologies.

Key Highlights

• Learning Path: Made-With-ML provides a structured course that takes participants through fundamental principles before diving into code, ensuring a solid understanding of ML concepts. Accessible lessons are available on their website.
• Open Source Code: The practical coding aspect is based on open-source implementation available on GitHub.
• Practical Implementation: It walks through combining ML with software engineering to build and optimize ML applications, focusing on best practices and real-world application.

Features

1. First Principles Understanding: Learn the foundational principles of machine learning before moving into practical coding.

2. Best Practices in Software Development: Emphasizes best practices from software engineering for effective model development and deployment.

3. Scalable Systems: Facilitates scaling of ML workloads in Python, ensuring ease of extension to larger projects.

4. MLOps Integration: Involves the integration of MLOps components including tracking, testing, serving, and orchestrating ML systems.

5. Smooth Transition from Development to Production: Learns techniques to rapidly transition models from development to production environments without altering code.

6. Continuous Integration/Continuous Deployment (CI/CD): Builds robust CI/CD workflows to continuously improve and deploy models.

Audience

Made-With-ML is targeted towards a wide array of users:

• Developers: Whether working in software, infrastructure, or data science, developers can incorporate ML into their products.
• College Graduates: The project bridges the gap between academic knowledge and industry needs, equipping graduates with practical skills.
• Product Leaders and Managers: It aids leaders in building technically sound products backed by machine learning.

Setup and Environment

• Cluster Setup: Users can either utilize local resources or scalable solutions like Anyscale clusters. Anyscale provides a structured environment for quick setup and accelerated learning.
• Version Control and Credentials: Instructions on setting up version control and handling credentials to enable collaborative and secure development.
• Virtual Environment Support: Ready-made setups for both local systems and Anyscale solutions to streamline the development process.

Notebook and Scripts

• Interactive Jupyter Notebooks: For hands-on exploration and understanding of core ML workloads.
• Well-structured Python Scripts: Refactoring notebook code into organized scripts to uphold coding standards and encourage learning.

Testing and Production

• Comprehensive Testing: Detailed testing procedures for code, data, and models, ensuring robustness and reliability.
• Production Readiness: Guidance on deploying applications into production, emphasizing stability and reliability, particularly with scalable cloud and on-premises solutions.

Made-With-ML is not just a course but a community-driven initiative helping developers and teams gain a deeper understanding and practical skills in the field of machine learning, turning ideas into valuable solutions. For those looking to engage further, joining live cohorts could provide more tailored support and access to sophisticated infrastructure.