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- AI
Small Language Model Engineering
1 Course
2 Hours
Skill IQ
Small language models are compact, efficient AI models that can be fine-tuned, compressed, and deployed privately — making them increasingly preferred over large cloud-based models for use cases with strict latency, cost, or data residency requirements.
This path covers how to evaluate and select the right SLM for a production use case, fine-tune and align a model on domain-specific data using techniques such as LoRA, QLoRA, and DPO, and deploy and operate a self-hosted SLM system including agentic workflows and production monitoring.
Content in this path
Small Language Model Engineering
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What You'll Learn
- How to evaluate and select the right small language model (SLM) for a production use case
- How to fine-tune and align a small language model on domain-specific data using parameter-efficient techniques
- How to compress a fine-tuned model using quantization and evaluate quality and efficiency tradeoffs
- How to monitor, version, and operate a production SLM system with observability and security controls
Prerequisites
- Learners should have working knowledge of Python and basic familiarity with machine learning concepts such as model training and evaluation. Prior experience with Hugging Face Transformers or a similar ML framework is helpful but not required.
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