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.
Production ML with Hugging Face
您将学到什么
Convert and deploy ML models across GGUF, SafeTensors, and APR formats for GPU, CPU, and browser targets
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最近已更新！
February 2026
作业
4 项作业
授课语言：英语（English）
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本课程是 Next-Gen AI Development with Hugging Face 专项课程 专项课程的一部分
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该课程共有4个模块
Understanding ML model formats and the Sovereign AI Stack. Learn GGUF, SafeTensors, and APR formats for different deployment targets.
涵盖的内容
6个视频8篇阅读材料1个作业
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.
涵盖的内容
8个视频6篇阅读材料1个作业
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.
涵盖的内容
11个视频6篇阅读材料1个作业
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.
涵盖的内容
1篇阅读材料1个作业
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To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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