Understanding LLMs and Why Federated Learning Enables AI for Everyone

The data and analytics world is undergoing a seismic shift. We are no longer competing solely on products or services — the ability to efficiently use the power of big data and leverage artificial intelligence (AI) is becoming the new differentiator between success and being left behind, mainly driven by two disruptive technologies: Large Language Models (LLMs) and Federated Learning. The first disrupts how we interact with, understand, and work with data. The second focuses on data access, efficiency, and privacy.

‍

Case Study: Revolutionizing Retail Operations in the EU with LLMs and Federated Learning  

In a recent proof of concept (PoC), Scalytics showcased the seamless integration of Large Language Models (LLMs) and Federated Learning to address real-world retail challenges. This initiative, aligned with the principles of Gaia-X—Europe’s ambitious project to create a secure and federated data infrastructure—demonstrates how Scalytics empowers businesses to leverage advanced AI technologies for a competitive edge, all while maintaining strict compliance with EU data privacy regulations.  

The PoC demonstrated how a  retail client is able streamline inventory management and enhance customer interactions through the strategic use of LLMs combined with Federated Learning. The goal is to analyze and predict consumer behavior with precision allowing store managers to optimize stock levels effectively, minimizing overstocking and shortages.

Simultaneously, Federated Learning ensured that AI models were trained directly at each store location using localized data, eliminating the need for risky data centralization. By using the ideas of Gaia-X principles, we not only ensured compliance with data protection laws but also demonstrated a decentralized data ecosystem that promotes sovereignty and transparency – a core value for European businesses.  

The results of the PoC resulted in significant reductions in data management costs, cloud-related expenses, and inventory costs. Additionally, by providing personalized recommendations based on local data insights, customer engagement skyrocketed.

This case study highlights Scalytics' ability to translate Gaia-X principles into tangible business benefits. We demonstrate how LLMs and Federated Learning can dramatically enhance retail operations, not just theoretically, but in practice. But how can AI be leveraged in daily operations without investing millions into new infrastructures and development talent?

‍

LLMs: Supercharging Business Operations

Imagine a powerful tool capable of generating human-like responses and understanding complex queries, all trained on a vast ocean of text data. That's the essence of LLMs. In the business world, LLMs go beyond mere automation; they become strategic assets. They streamline critical processes like customer service and content creation while simultaneously fueling data analysis for sharper decision-making.

My experience with deploying LLMs across various business functions has revealed their remarkable ability to significantly reduce operational bottlenecks. This translates to increased efficiency and a more satisfied customer base. Imagine a customer service representative equipped with an LLM that can not only answer routine questions but also understand nuanced inquiries and provide tailored solutions. This frees up human agents for more complex tasks and enables a positive customer experience.

‍

Deep Dive into LLM Capabilities: Extracting Insights, Augmenting Expertise

The true power of LLMs lies in their enormous ability to digest and synthesize large textual datasets. The newest model, LLama 3 by Meta (open source) delivers up to 800 tokens per second (tps). LLMs with that speed crunch through information, extracting patterns and uncovering hidden insights that might escape even the most meticulous human analyst. Consider a marketing team tasked with understanding customer sentiment towards a new product launch. Traditionally, this might involve time-consuming surveys and focus groups. Now, LLMs analyze multiple channels and crunch over social media data, online reviews, and customer service interactions, providing a comprehensive and real-time picture of customer sentiment.

LLMs are not simply about replacing human effort — they're about augmenting it. By automating repetitive tasks and providing real-time data-driven insights, LLMs allow human employees to focus on higher-level strategic thinking and creative problem-solving.

‍

Continuous Learning and Improvement: The Dynamic Nature of LLMs

Large language models are not static tools. They continuously learn and evolve as they are exposed to new data and user feedback. This dynamic adaptation is a crucial aspect of their utility in enterprises. For example, if you want to build an LLM-powered search engine for a client's extensive customer service knowledge base, over time, the LLM's understanding of the knowledge base structure and user queries refines significantly. This results in a measurable decrease in average case resolution time by support staff — a clear indicator of the model's ability to improve search capabilities through ongoing learning.

‍

But one crucial part is never mentioned when we read about such incredible success stories: Data. Without access to data sources, like social media feeds, customer interactions, and reviews, a real-world view is not possible. And given the vast amount of data sources, centralizing into data lakes or data warehouses doesn't make economic sense in most cases. The costs and latency of data movement are counterproductive for AI use cases, even for batch-oriented data analytics, like sales forecasts or maintenance reports for large-scale assets, like trucks or energy generators.

‍

Federated Learning: Reshaping Data Privacy and Model Training

While LLMs unlock the power of data, Federated Learning tackles the challenge of data access and privacy — a critical concern in every enterprise that wants to be a frontrunner in AI. Federated Learning is not new; it's been used in research for nearly a decade, and mobile phones have used federated models since the invention of voice recognition and speech assistants like Siri, Amazon Echo or Google Voice. Federation allows companies to train AI models on datasets across various locations, without ever needing to move the data itself from its original store.

Imagine a multinational retail company with offices around the globe. Each location possesses valuable customer data that could be used to train a more robust AI model. But data privacy regulations and security concerns might prevent them from sharing this data centrally, not to mention the different languages.

Federated Learning solves this challenge. By keeping the data on local data environments, mostly local servers or edges, different cloud providers and cloud regions, companies can leverage the collective intelligence of their global network while complying with strict data protection laws. This method proved instrumental in recent multinational projects — developing an AI model to predict customer churn across diverse markets. Federated Learning enabled users to integrate localized insights without implementing expensive ETL and compromising on privacy or security by centralizing data, leading to a comprehensive model and ultimately a significant reduction in customer churn for our client.

‍

Operational Benefits of Federated Learning

The true magic happens when LLMs and Federated Learning join forces. This powerful combination enables everyone to develop highly effective and compliant AI solutions.

Getting back to our retail client, he’s seeking to optimize inventory management across a huge network of stores. By combining LLMs with Federated Learning, the retailer using Scalytics is able to create a demand forecasting model that analyzes real-time sales data from each store location. This model then is used to train LLMs to understand complex patterns, and can predict future demand with remarkable accuracy. Federated Learning ensures that the training process happens without compromising customer privacy, as the data remains securely stored in the individual store’s data processing environment. This win-win scenario exemplifies the potential of LLMs and Federated Learning working together.

‍

Overcoming Barriers to Technology Adoption

Despite the clear advantages, the adoption rates of LLMs and Federated Learning can be hindered by misconceptions regarding their complexity and the cultural shift required for their deployment. Some businesses might be hesitant to use these new technologies due to concerns about integration challenges or a perceived need for a complete overhaul of existing systems.

At Scalytics, we understand these concerns. That's why we prioritize seamless integration and ease of adoption in our solution. We lead workshops for IT leaders and other stakeholders, demonstrating how smoothly Federated Learning can fit into existing systems. By illustrating its adaptability and potential to enhance current processes, these workshops have significantly reduced hesitancy and accelerated adoption rates.

‍