Generative AI Summit Washington, D.C, 2025

As productivity software evolves, the role of enterprise IT admins has become increasingly challenging.  Not only are they responsible for enabling employees to use these tools effectively, but they are also tasked with justifying costs, ensuring data security, and maintaining operational efficiency.  In my previous role as a Reporting and Analytics Product Manager, I collaborated…

It’s challenging to meet someone who hasn’t heard about GPT and other similar models this year. These Large Language Models (LLMs) signify a groundbreaking shift in the domains of machine learning and artificial intelligence. A field that remained obscure for most of its history is now an integral part of daily life for a vast segment of the global…

The rise of LLMs, and more recently the push to ‘taskify’ these models with agentic application, has ushered in a new era of AI. However, effectively deploying, managing & optimizing these models requires a robust set of tools and practices. Enter one of enterpise’s most vital functions in 2025, LLMOps: a set of methodologies and…

Generative AI is no longer a futuristic concept—it’s a transformative force reshaping today’s most innovative industries. Companies like Klarna and J.P. Morgan are making bold moves by integrating Generative AI into their operations, challenging the status quo and enabling unprecedented efficiency and creativity. This shift isn’t merely an incremental upgrade; it’s a paradigm change that…

As artificial intelligence (AI) continues to permeate various industries, the demand for efficient and powerful AI inference has surged. AI inference, the process of running trained machine learning models to make predictions or decisions, is computationally intensive and often constrained by the performance of the underlying hardware. Enter hardware accelerators[1]—specialized hardware designed to optimize AI…

As artificial intelligence (AI) continues to evolve, its deployment has expanded beyond cloud computing into edge devices, bringing transformative advantages to various industries. AI inference at the edge computing refers to the process of running trained AI models directly on local hardware, such as smartphones, sensors, and IoT devices, rather than relying on remote cloud…