The nitty-gritty of fine-tuning a GenAI model

We’ve all heard about how generative AI is changing almost every aspect of a business. If you crack open the door and peer in on the AI teams. You’ll see them playing with models and, no, we’re not talking about planes and trains. We’re talking about providing the correct inputs necessary to drive desired outputs in an AI model.

On this episode of the Georgian Impact Podcast, we will be discussing the impact of generative AI and fine-tuning data strategy with Rohit Saha, an ML scientist at Georgian’s R&D team. Rohit will explore how large language models (LLMs) and fine-tuning are changing the AI landscape for businesses, the necessary skills for data science teams in the age of generative AI, and the pivotal role of dynamic data strategy in leveraging new technology effectively.

You’ll Hear About:

The role of fine-tuning in tailoring foundational AI models to specific use cases.

How the landscape of ML and AI has evolved with the emergence of LLMs.

Leveraging LLMs to enhance productivity and build enterprise software.

Evolution of skills and talent required in the era of generative AI.

Creating a dynamic data strategy and leveraging open source models for fine-tuning.

Identifying golden use cases and the impact of LLMs on classification tasks.

Who is Rohit Saha?

Rohit Saha is an ML Scientist at Georgian's R&D team. He works with the portfolio companies to accelerate their data science roadmap by assisting them in scoping research problems, writing machine learning or AI code, and putting solutions into production. Rohit has worked across various projects, specializing in computer vision, natural language processing and large language models. His expertise lies in helping companies fine-tune and leverage large language models for enterprise software solutions.