Course · Intermediate
HuggingFace and Open Models
Closed APIs are convenient but come with per-token costs, data privacy concerns, and limited customization. Open-source models solve these problems but introduce new ones: you're responsible for deployment, fine-tuning, and operations. This course teaches you both sides so you can make the right call for each project, backed by real measurements instead of assumptions.
- Intermediate friendly
- 6 hrs
- 3 lessons
- Premium
Course overview
Learn to find, deploy, and fine-tune open-source models using Hugging Face, then evaluate when to self-host versus use closed APIs by benchmarking cost, quality, and latency.
What's inside
3 lessons
- 01 120 minDeploy Open-Source Models with Hugging Face
Learn to find, test, and deploy an open-source model as a local service so you can move beyond closed APIs when your project requires it.
- 02 120 minFine-Tune and Customize Open-Source Models
Learn to adapt a pre-trained model to your specific task using parameter-efficient fine-tuning with LoRA, and develop a framework for deciding when fine-tuning is worth it versus improving your prompts.
- 03 120 minCompare Model Deployment Strategies
Learn to evaluate open-source and closed-model deployments side by side, measuring cost, quality, and latency, and build a decision framework you can apply to any new AI use case.
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