FMOps: The Generative AI Imperative for Production

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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. 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 Findings

FMOps, Foundation Model Operations, is the superset of LLMOps, Large Language Model Ops in Generative AI

FMOps deals with the operational capabilities required for the efficient alignment, deployment, optimization, and monitoring of foundation models within the framework of an AI system.
Operationalizing large language models is unlike traditional AI solutioning.
LLMOps, subset of FMOps, builds on the principles of MLOps and helps enterprises select, operationalize, and manage the right foundation models or LLMs.

FMOps is needed to build operational capabilities for sustainable Generative AI solutions pipeline

FMOps is basic to enterprise-grade operational capabilities within the framework of an AI system, to manage and operate generative AI models.
FMOps helps enterprises foster collaboration, reduce conflicts, and hasten release cycles in their LLM pipelines.
FMOps improves model deployment time, 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, and all are important in an enterprise-grade AI system design

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 are to be rigorously managed.
Bias and ethical concerns related controls built into the model, with output tracked for such controls is critical.
Inferencing and run costs in production are major costs in LLMOps

Making of a Winning Gen AI Solution with LLMOps involves the following steps

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

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