This comprehensive Rust Specialization from specialists and professors from Data Science and AI programs, provides software engineers, data scientists, and technical professionals with applied skills to leverage Rust’s speed, safety, and versatility for robust systems programming. Over five courses spanning foundational syntax to advanced machine learning integrations, master Rust programming through hands-on lessons and 20+ coding projects, then tackle real-world challenges like building a database migration tool, optimizing an ML pipeline, or creating a Linux automation script. Upon completion, you'll have portfolio-ready Rust abilities to apply for roles like Platform Engineer, MLOps Engineer, Data Engineer, Embedded Engineer, or Backend Developer.
Overview
Syllabus
Course 1: Rust Fundamentals
Master Rust programming from scratch to build efficient applications.
Course 2: Rust Data Engineering
Use Rust's power for high-performance, concurrent data pipelines - from core principles to real-world deployments.
Course 3: Python and Rust with Linux Command Line Tools
Applied command line tool automation with Python and Rust for efficient task management.
Course 4: Rust for DevOps
Streamline DevOps with Rust for efficient application delivery.
Course 5: Rust for Large Language Model Operations (LLMOps)
Develop efficient Large Language Model solutions using Rust.
Courses
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Rust for Data Engineering: Efficient, Safe, and Concurrent Data Processing
- Learn to build robust data processing systems using Rust
- Explore Rust's performance, safety, and concurrency for data tasks
This 4-week course dives deep into leveraging Rust for efficient and reliable data engineering workflows:
- Mastering Rust data structures and collections for data processing
- Leveraging Rust's safety/security features in data engineering context
- Using Rust libraries like Diesel, async, Polars, Apache Arrow
- Interfacing with data stores, REST/gRPC APIs, AWS SDK
- Designing full-fledged data pipelines and processing systems in Rust
- Hands-on projects for building data ingestion tools, ETL pipelines
- Best practices for handling large datasets, optimizing performance
- Techniques for writing safe, concurrent, and lock-free code
- Deploying and maintaining Rust-based data engineering solutions
By the end, you'll gain practical experience building high-performance, secure data systems using Rust - preparing you for real-world data challenges.
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From Basics to Mastery
- Comprehensive course for beginners and experienced programmers
- No prior Rust knowledge required - start coding from scratch
- Learn core language concepts, syntax, tooling, best practices
Get a solid foundation in the powerful and efficient Rust programming language:
- Rust basics - variables, data types, control flow, ownership rules
- Advanced concepts - structs, enums, traits, lifetimes, concurrency
- Using powerful tools like Rust Analyzer, rustfmt, Cargo
- Test-driven development and debugging techniques
- String manipulation, error handling, modules
- Leverage AI pair programming with GitHub Copilot
- Rust coding best practices for readability and performance
- Build and document your own libraries using Cargo
- Techniques for writing safe, concurrent, and lock-free code
By the end, you'll master Rust to build reliable, high-performance software - whether starting as a total beginner or experienced programmer.
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Build powerful automation utilities for the terminal with Python and Rust
- Learn to build efficient, reliable command-line utilities
- Gain skills for automating tasks in data/systems engineering
- No prior Python/Rust knowledge required, but programming basics recommended
- Understand best practices for CLI tool development and distribution
This course teaches you how to implement automation and utilities via the command-line interface (CLI) using Python and Rust. Designed for beginners and those with some programming experience.
- Step-by-step tutorials cover core concepts like parsing CLI args, creating subcommands, generating reports, and more
- Write high-performance Rust code for CPU/memory-intensive tasks
- Leverage Python's rich libraries for file I/O, HTTP requests, and data manipulation
- Learn techniques for distributing your CLI tools via PyPI and crates.io
- Best practices for designing intuitive, user-friendly command-line interfaces
By completing this course, you'll gain a solid foundation in Python and Rust to develop sophisticated, powerful command-line tools for automating workflows across various domains.
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Build, Deploy, and Operate Robust Applications
- Apply DevOps workflows using the power and safety of Rust
- Hands-on experience with containerization, observability, CI/CD
- Beginner coding experience required, Linux/Git/Docker basics recommended
- Gain practical skills for software engineering and SRE roles
This intermediate course teaches you to leverage Rust for streamlining full-cycle DevOps processes:
- Build and package applications as Docker containers
- Configure logging and monitoring with ELK, Prometheus for observability
- Automate system tasks - file parsing, cron jobs, script execution
- Set up CI/CD pipelines with GitHub Actions, Jenkins, Makefiles
- Instrument code with error handling, profiling, and benchmarking
- Deploy apps to Kubernetes clusters and serverless environments
- Implement chaos engineering for resilience testing
- Secure systems with Rust's safety guarantees and auditing tools
- Optimize performance with async/await, zero-cost abstractions
Through hands-on projects, you'll gain experience rapidly building, deploying, and operating robust applications using DevOps methodologies powered by Rust.
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This advanced course trains you for the cutting-edge of AI development by combining the power of Rust with Large Language Model Operations
- Learn to build scalable LLM solutions using the performance of Rust
- Master integrating Rust with LLM frameworks like HuggingFace Transformers
- Integrate Rust with LLM frameworks like HuggingFace, Candle, ONNX
Get trained in the latest AI/ML innovations while mastering systems programming with Rust - your pathway to building state-of-the-art LLM applications.
- Optimize LLM training/inference by leveraging Rust's parallelism and GPU acceleration
- Build Rust bindings for seamless integration with HuggingFace Transformers
- Convert and deploy BERT models to Rust apps via ONNX runtime
- Utilize Candle for streamlined ML model building and training in Rust
- Host and scale LLM solutions on AWS cloud infrastructure
- Hands-on labs: Build chatbots, text summarizers, machine translation
- Apply LLMOps DevOps practices - CI/CD, monitoring, security
- Techniques for memory safety, multithreading, lock-free concurrency
- Best practices for LLMOps reliability, scalability, cost optimization
- Real-world projects demonstrating production-ready LLMOps expertise
Taught by
Noah Gift and Alfredo Deza
