Introduction

Large Language Models (LLMs) have emerged as transformative tools capable of understanding and generating human language with unprecedented accuracy. These models, underpinned by the sophisticated transformer architecture, represent a significant leap forward in natural language processing. This article delves into the technical details that make LLMs so powerful, exploring the foundational components such as self-attention mechanisms, token embeddings, and the interplay of linear and softmax layers.

Introduction

In today’s data-driven world, building efficient machine learning (ML) pipelines is crucial for leveraging data to drive insights and business value. In the realm of machine learning, the creation of robust, scalable, and automated pipelines is not just a technical necessity but a strategic imperative.

Introduction

In the dynamic landscape of financial services Industry, the integration of Generative Artificial Intelligence (Gen AI) has emerged as a game-changer. In this article, we delve into the transformative applications of Gen AI, focusing on the role of Large Language Models (LLMs) such as GPT-3, BERT, and LLaMA, among others.

Background:

Having extracted data from various Data sources and establishing robust data pipelines and creating comprehensive data views in previous stages, which we covered in our previous edition of Finalytica last month. In this article and in continuation of the process, we delve into building reports & dashboards and extracting actionable insights from the accumulated data.

Creating Data Views in various environments for complex BI Dashboard Creation

Introduction:

In various BI implementations, we have addressed the need for efficient data pipelines to build complex BI reports from diverse sources. Here’s how we practically implemented robust data pipelines supporting advanced BI dashboards, utilizing cloud platforms like AWS and GCP, as well as on-premise solutions.