In today’s rapidly evolving cognitive era, Generative AI and Large Language Models (LLMs) hold immense promise. However, the journey from initial proof-of-concepts (POCs) to robust, production-grade applications presents substantial challenges. This is where LLMOps comes to the fore. As an emerging field dedicated to managing the end-to-end production life cycle of applications powered by Generative AI and LLMs, LLMOps is a pivotal game-changer.
LLMOps, or Large Language Model Operations, is a hybrid discipline that amalgamates practices from DevOps and MLOps while addressing the specific needs and hurdles of Generative AI and LLMs. It is concentrated on operationalizing these models, encompassing facets such as model versioning, testing, deployment, monitoring, and lifecycle management, all to ensure reliable and efficient functionality in a production setting.
In this talk, you’ll get a comprehensive overview of LLMOps. You’ll be guided through the various stages of transitioning a GenAI-powered application from development to production and maintaining it post-production. Backed by extensive experience with large-scale implementations, the speaker will also provide valuable insights into best practices, essential tools, reliable platforms, and efficient architectural patterns.
Get ready to broaden your horizon in the exciting realm of Generative AI and LLMs and discover how LLMOps can be the key to unlock their full potential at scale.
Key Takeaways:
- Grasp of LLMOps and its role in Generative AI and LLMs.
- Knowledge on operationalizing Generative AI and LLMs.
- Steps to transition GenAI applications from development to production.
- Understanding of post-production maintenance for GenAI applications.
- Exposure to best LLMOps practices, tools, and platforms.
- Ways to leverage LLMOps to scale Generative AI and LLMs.
