The Guide to Deploying LLMs for Real-World Deployments
Master LLM implementation and operations with proven strategies from the experts.
Large Language Models (LLMs) like GPT-3 and DALL-E 2 represent a tremendous leap forward in natural language processing.
This guide to deploying LLMs provides a comprehensive playbook for taking your LLMs live based on our team’s real-world experience and best practices.
What's Inside?
Learn practical tips and techniques to:
- Architect LLMs for your specific use case
- Optimize LLM infrastructure for scale and cost-efficiency Implement monitoring, logging, and maintenance workflows
- Employ ethical AI practices for transparency and bias mitigation
- Continuously benchmark, test, and improve LLMs over time
Whether you’re involved in developing, deploying or optimizing large language models, this guide to deploying LLMs equips you with the operational knowledge to successfully run LLMs in production.
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Who you'll hear from
Insights from experts in machine learning and LLMs
Andrew Wilson
Head of Customer Success, Seldon
“LLM’s versatility enables them to be easily adaptable to new use cases beyond their original training objective.”
Sherif Akoush
MLOps Engineer, Seldon
“Careful optimization based on the use case is required to avoid overprovisioning expensive GPU hardware or hurting end users’ latencies.”