"But the goal of data science is not to execute. Rather, the goal is to learn and develop profound new business capabilities. Algorithmic products and services like recommendations systems, client engagement bandits, style preference classification, size matching, fashion design
Managing and Organising AI Talent
In spite of its potential, executing and achieving success in machine learning and AI projects can prove to be quite challenging. Frequently, even the most diligent efforts can be hindered by uncertainties surrounding a rapidly evolving technical landscape, perplexity about
Cultivating a Mission Directed Approach to AI
AI initiatives in organizations have become increasingly diverse, reflecting the broad nature of the field itself. Many industry professionals have acknowledged that relying solely on machine learning (ML) and statistics is insufficient for solving every data problem within the enterprise.
MLOps and Data Management
Data is tricky to manage once the operations scale. It can grow exponentially, coming in from new sources, and becoming more diverse, which makes it increasingly challenging to process and find insights. Mature organizations like Google, Airbnb and Uber have
Is Machine learning the right tool for your problem?
In recent years, a specific area within the field of artificial intelligence known as machine learning has successfully emulated various aspects of human intellect. Machine learning-based computer programs have demonstrated their prowess by outperforming world champions in games like chess
Tools for building Strategic Resilience
As we enter the Post-COVID world, impending economic downturn takes hold in the business world. Organizations that were performing well well under normal conditions when black swan events like Covid disrupt the normal flow of things. In these scenarios organisations
Data Science Maturity Model: A Primer
In the past decade or so, businesses have realized that they can extract value out of the data they collect (e.g. user data and event data) to make data-informed decisions that replace the old model of deciding best argument
Beyond simulation, applying RL in real world: Challenges and Opportunities
Standard RL offers methods for solving sequential decision making problems (typically formalized by a Markov Decision Process) that involve Maximisation of reward/minimize cost over some time horizon (and where a model of the environment may or may not be