Production ML with Hugging Face
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Production ML with Hugging Face
This course is part of Next-Gen AI Development with Hugging Face Specialization
Instructor: Noah Gift
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What you'll learn
Convert and deploy ML models across GGUF, SafeTensors, and APR formats for GPU, CPU, and browser targets
Skills you'll gain
Details to know
February 2026
4 assignments
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There are 4 modules in this course
Learn to deploy ML models to production using the Sovereign Rust Stack—a pure Rust implementation with zero Python runtime dependencies. This hands-on course teaches you to work with three critical model formats (GGUF, SafeTensors, APR), implement MLOps pipelines with CI/CD and observability, and deploy models across GPU, CPU, WebAssembly, and edge targets.
Through real-world projects including a Python-to-Rust transpiler (Depyler), browser-based speech recognition (Whisper.apr), and LLM inference benchmarking (Qwen), you'll master format conversion, cryptographic model signing, and performance optimization. The course culminates in a capstone project deploying Qwen2.5-Coder across all three formats with benchmarking. What makes this course unique: instead of relying on Python frameworks, you'll build with production-grade Rust tooling that compiles to native binaries and WebAssembly. Learn to run sub-millisecond inference in browsers, bundle models into executables, and achieve 2x performance gains over standard tools. Ideal for ML engineers and software developers ready to move beyond notebooks into production deployment.
Understanding ML model formats and the Sovereign AI Stack. Learn GGUF, SafeTensors, and APR formats for different deployment targets.
What's included
6 videos8 readings1 assignment
6 videos•Total 21 minutes
- Course Introduction•3 minutes
- Hugging Face Model Publishing•4 minutes
- Model Types on Hugging Face•3 minutes
- APR Format Deep Dive•4 minutes
- Model Format Comparison•3 minutes
- Why Trace Models •4 minutes
8 readings•Total 8 minutes
- Introduction to Course and Course Resources•1 minute
- Meet your instructors•1 minute
- Key Concepts•1 minute
- Reflection•1 minute
- Key Terms•1 minute
- Reflection•1 minute
- Key Terms•1 minute
- Reflection•1 minute
1 assignment•Total 5 minutes
- Quiz: Model Format•5 minutes
Production infrastructure for ML systems. This module covers the essential MLOps practices needed to deploy and maintain ML models in production environments. Learn how to implement CI/CD pipelines specifically designed for ML workflows, set up comprehensive observability with logs, metrics, and traces, apply cryptographic model signing for supply chain security, and choose optimal deployment patterns based on your infrastructure requirements.
What's included
8 videos6 readings1 assignment
8 videos•Total 24 minutes
- Model Registry Architecture•3 minutes
- CI/CD Pipeline for ML•4 minutes
- Model Observability Stack•3 minutes
- Model Signing & Security•3 minutes
- Binary Deployment Patterns•3 minutes
- Inference Server Architecture•3 minutes
- Corpus Management & DataOps•3 minutes
- Cost-Performance Decision Matrix•3 minutes
6 readings•Total 60 minutes
- Key Concepts•10 minutes
- Reflection•10 minutes
- Key Terms•10 minutes
- Reflection•10 minutes
- Key Terms•10 minutes
- Reflection•10 minutes
1 assignment•Total 5 minutes
- Quiz: MLOps Foundations•5 minutes
Real-world projects built with the Sovereign AI Stack. This module demonstrates practical applications through three production projects: Depyler (a Python-to-Rust transpiler with self-improving ML), Whisper.apr (speech-to-text in browser and CLI), and the APR ecosystem tools. Learn how to build self-improving systems using compiler-in-the-loop training, deploy speech recognition to resource-constrained environments, and leverage the full APR toolchain for model conversion and inference.
What's included
11 videos6 readings1 assignment
11 videos•Total 43 minutes
- Four Projects, One Stack•5 minutes
- Depyler Deep Dive•5 minutes
- Depyler Oracle Training•3 minutes
- Depyler Single-Shot Compile•3 minutes
- Whisper.apr Overview•5 minutes
- Whisper Code Walkthrough•4 minutes
- Whisper Demo•3 minutes
- APR Format Rosetta Stone•3 minutes
- APR Hub & Spoke Architecture•3 minutes
- APR Chat Demo•3 minutes
- Course Conclusion•3 minutes
6 readings•Total 60 minutes
- Key Terms•10 minutes
- Reflection•10 minutes
- Key Concepts•10 minutes
- Reflection•10 minutes
- Key Concepts•10 minutes
- Reflection•10 minutes
1 assignment•Total 5 minutes
- Quiz: Project Showcase•5 minutes
Final project deploying Qwen2.5-Coder-0.5B across all three model formats. Students demonstrate mastery of format conversion, CLI deployment, server deployment, and performance benchmarking.
What's included
3 readings1 assignment
3 readings•Total 21 minutes
- Capstone Project: Multi-Format Deployment•10 minutes
- Before You Go•1 minute
- Next Steps•10 minutes
1 assignment•Total 15 minutes
- Final Graded Quiz•15 minutes
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