A Large Language Model (LLM) is a notable AI system, powered by neural networks, designed to comprehend and generate human-like text from vast amounts of training data. These models are like language virtuosos, capable of matching context, generating coherent text, and answering questions in a way that seems human. They have gained significant attention due to their potential applications in chatbots, content generation, language translation, and information retrieval. Origin

Once businesses have started collecting and combining all kinds of data, the next elusive step is to extract value from it. Collected data may hold tremendous potential, but no value can be created unless insights are uncovered and translated into actions or business outcomes. Originally published at LinkedIn Pulse on 23 December 2019. Photo credit: www.pexels.com Data storytelling is the process of translating data analyses into layman’s terms to influence business decision

I have seen many articles on types of analysis. Most of these articles discuss four types: descriptive, exploratory, diagnostic (usually either exploratory or diagnostic!), predictive, and prescriptive analysis. But throughout my early career, I’ve personally experienced eight types of analysis. I encourage all aspiring data scientists to be familiarised with all these types of analysis and, if necessary, apply them. Originally published at LinkedIn Pulse on 22 December 2019

Generative AI (GenAI), especially LLMs, has transitioned from a buzzword to an essential business asset. Leading organisations have moved beyond the proof-of-concept (POC) phase and are now actively deploying GenAI solutions. Many non-technology companies have realised that developing proprietary LLMs is unnecessary for solving internal challenges . Instead, they acquire LLMs from vendors or the open-source marketplace. Originally published at LinkedIn Pulse on 23 March 2025

The buzz around AI often overshadows a critical reality: over 85% of employees anticipate that AI will impact their jobs within the next 2-3 years, leading to a mix of excitement and apprehension. There’s a notable disconnect between leadership’s perception of readiness and the reality on the ground, with C-suite leaders more likely to cite a lack of employee readiness as a barrier, even as employees are already adopting generative AI tools at a higher rate than expected. Thi