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

Generative AI is a branch of artificial intelligence that focuses on creating new content or data from scratch, such as images, text, music, code, diagrams, etc. Generative AI has been making impressive advances in recent years, thanks to the development of deep learning models such as generative adversarial networks (GANs), autoencoders, transformers, and others. Originally published at LinkedIn Pulse on 8 January 2024. Photo by Google DeepMind @ Pexels.com Generative AI ha

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

In the wake of the advent of Large Language Models (LLMs), a multitude of initiatives have been undertaken to enhance user experiences via Natural Language Processing (NLP). A vast array of articles has been penned to encourage organisations to embrace generative artificial intelligence (AI). These pieces, however, have predominantly focused on the impressive ability of LLMs to understand user inquiries and generate responses, and often criticise the fact that they overlook f

Synthetic data has become a buzzworthy topic in recent times, offering a glimmer of hope for addressing the challenge of limited high-quality data for training AI and ML models. The other day, an enthusiastic salesperson came to me with a pitch for a product that claimed to generate synthetic data. Now, don’t get me wrong, AI and ML models are undoubtedly going to shape the future of work. However, I have some reservations about relying solely on synthetic data to build these

The honeymoon phase is over. What comes next will reshape every company on earth. There is a quiet revolution underway inside enterprise technology—and it bears no resemblance to the breathless headlines about artificial general intelligence or trillion-parameter models. It looks like budget reviews. It looks like an infrastructure audit. It looks like boards are demanding to know why AI investments are producing inconsistent returns at unsustainable cost. The era of deployin

To drive value from data, analytics need to be operationalised. Ad-hoc data exploration can be unclear, depends on each individual functional team’s preferences to generate insight, acted on and then forgotten under the pile of documentation. The preference may vary from the way the experiments are conducted, tools being used, and the finding has been disseminated. Every project starts from scratch. And if someone with a vast knowledge leaves the organisation, the prior knowl

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

Multiple recent recruitment surveys revealed that the demand for data engineers has recently exceeded the previous demand for data scientists. The Dice 2020 Tech Job Report said data engineer was the fastest growing job in technology with a 50% year-over-year growth in the number of open positions. Many mentees asked me how do I perceive this shifts in demand, and what area should they pursue. Actually, I see a course correction happening in the industry. Most organisations t

The pervasive notion that sophisticated Artificial Intelligence (AI) systems, particularly predictive models and large language models (LLMs), operate under a regime of continuous, autonomous self-improvement is one of the most persistent and potentially damaging misconceptions in contemporary technology discourse. This belief, often propagated through media narratives and science fiction precedents like Recursive Self-Improvement (RSI), a concept involving a system autonomou

With the advent of the data science practises, especially the likes of Artificial Intelligence (AI) or machine learning and their adaptation in sensitive areas, has drawn massive attention to this technology in the recent years. Whether it is cancer diagnosing system, Amazon’s AI recruiting tool or the US court’s profiling system Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), we can always disagree to the decision made by the AI systems. In fac

Artificial Intelligence (AI), or more precisely Machine Learning (ML), has become an industry trend in the past 10 years. From a buzzword to a new way of automation and decision-making, ML has become mainstream. I have had conversations with multiple organisations keen to use ML, even without a real-world use case. Graduate schools are offering lightweight courses to the masses, consulting companies are providing services on adopting ML, and technology companies are building

Large Language Models (LLMs) represent a transformative technology in the realm of generative artificial intelligence (AI). These machine learning models are designed to understand and generate text that mirrors human language, learning from vast amounts of text through rigorous self- and semi-supervised training. LLMs have a broad range of applications, from generating text and automating workflows to sparking creative ideas and even writing software code. Some of the most p