Next-Gen AI Development with Hugging Face: Coursera Specialization

2026-04-17 · 4min · Pragmatic AI Labs

Table of Contents

Build production AI systems with the Hugging Face ecosystem in pure Rust — from Hub fundamentals through transformer fine-tuning, large language models, and deployed production ML. Five courses covering the full lifecycle of modern open-source AI development.

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Production LLM architecture patterns using Rust, AWS, and Bedrock.

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What You Will Build

Fine-tuned transformers for custom NLP tasks, Rust-based LLM inference pipelines, and production-ready Hugging Face deployments. You will learn the Hub model and dataset ecosystem, transformer internals, and both Python and Rust paths to shipping AI to production.

Courses in This Specialization

Hugging Face Hub and Ecosystem Fundamentals — Models, datasets, spaces, the pipeline API, and how the Hub powers open-source AI.
Fine-Tuning Transformers with Hugging Face — PEFT, LoRA, dataset prep, evaluation, and pushing fine-tuned models back to the Hub.
Large Language Models with Hugging Face — Quantization, batching, prompt engineering, and deploying LLMs via transformers and text-generation-inference.
Advanced Fine-Tuning in Rust — candle, burn, and pure Rust training loops for deterministic, production-grade ML.
Production ML with Hugging Face — Inference endpoints, monitoring, cost control, and scaling the Hub stack.

Who This Is For

ML engineers moving open-source models to production
Python developers who want a Rust path into LLM inference
Platform engineers operating AI services at scale

AI Tooling — 20-course superset covering foundation models, orchestration, and multi-model systems
Large Language Model Operations (LLMOps) — LLMOps focus on Azure, AWS, and open-source stacks
Rust Programming — foundational Rust for the advanced fine-tuning path

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