Welcome to the Rust Time Series Edge ML Project – your gateway to mastering Rust in the rapidly evolving world of edge machine learning, with a focus on time series data. This repository is designed as an educational resource, providing a practical, hands-on approach to learning Rust through the development of a time series machine learning model suitable for edge computing.
Our mission is to create a learning platform that demystifies Rust programming and its applications in edge ML, particularly for handling time series data. The project encompasses everything from reading and processing data, to implementing and training a machine learning model using Rust's powerful ecosystem.
- Comprehensive Time Series Analysis: Learn to process and analyze time series data using Rust's robust data handling libraries.
- Edge ML Model Development: Step-by-step guide to developing a machine learning model suitable for edge deployment.
- Rust Best Practices: Dive deep into Rust programming idioms, understanding memory safety, concurrency, and more.
- Hands-on Learning: Engaging examples and challenges that offer real-world applications of Rust in time series edge ML scenarios.
- Rust Basics: Start with the fundamentals of Rust programming.
- Data Handling: Learn how to read, process, and manipulate time series data in Rust.
- Machine Learning Model: Step into the world of machine learning with a focus on creating models optimized for edge computing.
- Optimization for Edge Deployment: Understand the intricacies of deploying ML models on edge devices, focusing on efficiency and performance.
- Project Challenges: Solidify your learning with practical challenges and exercises.
- Aspiring Rust Developers: Who wish to explore Rust in the context of machine learning and data processing.
- Data Scientists and ML Enthusiasts: Looking to understand how Rust can be leveraged for edge ML solutions.
- Students and Educators: Seeking a structured approach to introducing Rust and ML concepts.git branch --set-upstream-to=origin/main main