Bridging the gap between AI-hype and real-world deployments

Navigating the future of GenAI and the evolution of MLOps into LLMOps

Welcome to a journey through the transformational landscape of Generative AI (GenAI) and MLOps in data-led environments.

LLMOps brings human supervision in partnership with GenAI… what could go wrong?

We can’t imagine anything more exciting than automation and data science and if you don’t agree, we think that says more about you than us!

In a market where innovation is relentless and the lines between human capabilities and machine intelligence continue to blur, almost all companies are faced with both unprecedented opportunities and complex challenges.

The driving force behind this transformation? Generative AI, often referred to as GenAI, and the strategic orchestration of Machine Learning Operations, or MLOps.

Everyone is still trying to figure out what the killer business application is, or the use cases where GenAI is going to make the biggest difference and add real value. Whilst the holy grail of GenAI is yet to be discovered, it is almost certain that the impact of GenAI on economies, society, and enterprises will be significant. The McKinsey Global Institute estimates GenAI will add between $2.6 and $4.4 trillion in annual value to the global economy. Goldman Sachs estimates that GenAI could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period.

So… it’s important. But we need to navigate the hype and test ideas quickly to see what will actually create value. And that’s why we have created this series. To explore the strategic orchestration of MLOps.

Series Overview

In this series, we embark on a deep dive into the critical intersections of GenAI and MLOps, exploring the intricacies and strategies that are shaping the future of GenAI in companies that value automation and value. Our journey will take us through four, we think essential, domains:

1. Productionisation of GenAI with MLOps Strategies:

• Unique challenges involved in deploying generative models
• Bridging Research and Deployment and how we address the intricacies of GenAI deployment
• The Significance of Data Quality
• 10 MLOps strategies to tackle the challenges of productionising GenAI

2. MLOps for Increased Reliability in Generative AI

• Explore why GenAI, with its stochastic nature has raised concerns about reliability.
• Examine the growing regulatory and compliance issues facing some industries in the age of GenAI.
• Dive into how the implementation of MLOps strategies for LLMs (LLMOps) can measurably enhance the reliability and compliance of GenAI systems.
• Explore how increased reliability of GenAI can open new opportunities for accelerating business applications.

3. MLOps Solutions for GenAI: Introduction to LLMOps

• Explore why GenAI’s stochasticity poses unique challenges, often making it difficult to comprehend once deployed.
• Illustrate these challenges with real-world examples, including insights from tech giants like Google and OpenAI.
• Propose an innovative MLOps-based governance solution for GenAI, allowing progress without sacrificing control and understanding. MLOps solutions are relatively mature and so build upon and adapt what works for this new field.

4. Getting started with Large Language Models: Introducing LLMOps into your business

• Explore the possibilities where LLMs and GenAI can drive innovation and efficiency inside organisations.
• Discover the transformative potential of Large Language Models (LLMs) — what they are, why they matter, and how they work.
• Assess how the integration of LLMs can increase productivity and revenue, complete with estimations.
• Navigate the potential pitfalls, including financial, legal, and safety risks, and learn how LLMOps methodologies can mitigate these challenges.
• Gain an overview of the current state and track record of MLOps in regulated organizations and what we’ve learned during the implementation and maintenance of MLOps pipelines.

We know that there is an exciting parallel field of FMOps (foundational models need some love too) and we’re working on that blog series, but for the vast majority of us and our use cases, LLMs are where we’re working and that forms the basis of this series. Keep an eye out for future posts from Babak Takand and the GlobalLogic team.

If you want to know more about what we’re doing, come and visit us at https://www.globallogic.com/generative-ai