Build an effective, performant LLMOps tech stack
This short whitepaper shows you how
Get the definitive guide to the processes and tools for managing LLMs in production
Organizations are rushing to build apps on top of large language models, inspired by use cases in marketing, customer service, question answering, and more. With the proliferation of these apps has come an intense need for ways to productionize, scale, and manage these apps.
But what should this LLMOps tech stack look like? How can you drive the best LLM app performance that you possibly can, both in the lab and in production?
The founders of TruEra, the leading provider of enterprise AI Observability, and the CTO of Enterprise Analytics and AI at Intel Corporation have teamed up to create this whitepaper that will help you to accelerate effective LLM app development while driving high performance.
Get the whitepaper and you will learn:
- The 3 layers of the LLMOps Tech Stack and what’s in each
- The 10-step typical workflow for building, deploying, and managing LLM apps
- What LLM Observability is and why it’s critical to LLM app success
- The road ahead for Gen AI
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Read the whitepaper
TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra’s suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.