Topics In Demand
Notification
New

No notification found.

FMOps: The Generative AI Imperative for Production

October 31, 2023 426 0 AI Machine Learning DevOps

FMOps: The Generative AI Imperative for Production

Generative AI applications and solutions have grown leaps and bounds since the start of 2023. In this context, it is critical to have a streamlined approach to develop, deploy, run, monitor, and manage language model applications. LLMOps is a practice and approach to overseeing the lifecycle of LLMs from training to maintenance using tools and methodologies. By operationalizing technology at scale, LLMOps aims to make the path to adopting Generative AI easier. This study, FMOps: The Generative AI Imperative for Production, intends to usher enterprises into the world of generative AI by offering an industry perspective into how to build successful generative AI solutions.

Key Highlights

LLMOps is a subset of FMOps, foundation model operations, that focuses on the operational capabilities and infrastructure required to fine-tune existing foundational models, capture the prompt engineering, build monitoring pipelines to capture experiments and deploy these refined models in production.

This study addresses the following aspects related to FMOps, and in detail, LLMOps:

  • How is GenerativeAI Different from Traditional AI?
  • What is FMOps in Generative AI?
  • Why FMOps?
  • What Does LLMOps Mean in the Context of FMOps and Generative AI?
  • Differences Between MLOps and LLMOps

FMOps – building operational capabilities for sustainable Generative AI solutions pipeline

The study details:

  • Basics of FMOps and the operational capabilities enabled by FMOps within the framework of an AI system.
  • Benefits derived from FMOps – helps enterprises foster collaboration, reduce conflicts, and hasten release cycles in their LLM pipelines.
  • FMOps helps improve efficiencies and faster model deployment in production, seamless scalability, reduced risk, seamless integration with DataOps practices, smooth data flow from ingestion to model deployment, shorter iteration cycles, data privacy, and optimal resource allocation.

 

MLOps, FMOps, and LLMOps are different

The study offers a perspective on the differences between MLOps, LLMOps, and FMOps. LLMOps, large language model operations,is a subset of FMOps that includes large language and visual models, builds on the principles of MLOps, machine learning operations, and helps enterprises deploy, monitor and retrain their LLMs seamlessly. The study offers a review of the three and the key differences among the three practices associated with AI. LLMops needs:

  • LLMOps is a set of architectural practices and methodologies to work with LLMs
  • Diverse, representative contextual data, in the form of vector databases is the base requirement
  • Broader set of metrics deployed to assess model performance
  • Model training, data, training process, and model versioning is rigorously managed
  • Bias and ethical concerns related controls built into the model, with output tracked for such controls
  • Inferencing and run costs in production are major costs in LLMOps

 

Making of a Winning Gen AI Solution with LLMOps

The study delves in great depth into the following steps associated with practicing LLMOps for desired output:

  • Finding the right model, the right technique, the star team, and the right tech stack
  • Understanding the current LLMOps landscape
  • Building a RACI for LLOps implementation
  • Operationalizing LLMs with LLMOps
  • Finding the right metrics
  • Building the guardrails: policy management

 

For a detailed read, download this free report.

Community by nasscom Insights is focused on building the largest online community catering to the Indian technology sector. The purpose of the community is to bring the latest trends and discussions onto a single platform. Our passion for tech drives the free-flowing exchange of ideas and visions from industry leaders and game-changers across India.



LATEST REPORTS

© Copyright nasscom. All Rights Reserved.