LLMOps: MLOps for Large Language Models

LLMOps focuses on the operational capabilities and infrastructure required to fine-tune existing foundational models and deploying these refined models as part of a product.

Transfer Learning and Large Language Models LLMs (large language models) have grabbed the public attention with ChatGPT and plenty of companies are looking at opportunities to incorporate similar functionality to their products but perhaps with more added domain expertise and focus. This is possible through transfer learning , where you take an existing state-of-the-art model like GPT-3 and refine it into a model for your specific use case by teaching it with domain-specific data. For example, your use case might have a desired output style and format (e.g. medical notes). Through transfer lea

related reading

• Mosaic LLMs: GPT-3 quality formosaicml.com
• How To Scale Your Modeljax-ml.github.io
• Productizing Large Language Modelsblog.replit.com
• What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
• Deep Dive into LLMs like ChatGPTyoutube.com
• Rise of Large Language Model Operations - by Shabaz Patelshabazpatel.substack.com
• Patterns for Building LLM-based Systems & Productseugeneyan.com
• Transformers Inference Optimization Toolset | AstraBlogastralord.github.io
• Scaling: The State of Play in AIoneusefulthing.org
• Overview & Applications of Large Language Models (LLMs)leighmariebraswell.substack.com
• LLM Inference Performance Engineering: Best Practices | Databricks Blogdatabricks.com
• AI Revolution - Transformers and Large Language Models (LLMs)blog.eladgil.com