Over the past two years, I have spearheaded multiple Generative AI (GenAI) initiatives to enhance customer service, streamline internal processes, and gain a competitive edge. These projects have spanned various applications, from deploying customer service chatbots to automating internal workflows. Originally published at LinkedIn Pulse on 24 March 2024. Photo by Tara Winstead @ Pexels.com This journey has provided us with critical insights, revealing both the immense poten

Vibe coding is an emerging paradigm in software development where developers express high-level intent in natural language, and AI assistants generate the underlying code. It flips the developer’s role from coder to orchestrator, with a focus on reviewing, testing, and tightening what the machine produces. In the right context, this is a game-changer. It slashes time spent on boilerplate, accelerates prototyping, and lets non-specialists turn ideas into working models faster

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

In today’s highly competitive environment, the rush to harness the transformative power of Artificial Intelligence (AI) is apparent. However, beneath the surface of many organisations, there is a subtle, often hidden, threat: Shadow AI. This isn’t the stuff of sci-fi thrillers; it involves the widespread use of AI tools and initiatives by individual teams or departments, such as marketing, HR, or customer service, without central oversight, approval, or understanding from IT

In the rapidly evolving landscape of Generative AI (GenAI), the importance of version control for prompts cannot be overstated. Initially simple and rigid, prompts have evolved significantly with advancements in AI technology, particularly with the advent of large language models (LLMs) like GPT-3 and GPT-4, which enable more natural and context-aware interactions. Today, prompts are integral to the performance and reliability of AI systems, necessitating meticulous version c

The article highlights integrating software engineering best practices, like design patterns, into AI and ML workflows. Engineers often focus on achieving high accuracy and performance in model development, sometimes overlooking essential practices that ensure code is modular, maintainable, and scalable. Engineers can create robust, scalable, and maintainable ML systems by incorporating design patterns like pipelines, factories, adapters, decorators, strategies, iterators, fa