What is a Support Vector Machine?

    Most neophytes, who begin to put their hands to Machine Learning, start with regression and classification algorithms naturally. These algos are uncomplicated and easy to follow. Yet, it is necessary to think one step ahead to clutch the concepts of machine learning better. There are a lot more concepts to learn in machine learning, which may not be as rudimentary as regression or classification techniques, but can help us answer different intricate cases. So, today let us get familiar with one such algorithm, the Support Vector Machine or SVM. What is a Support Vector Machine? Let us walk… Read More


Domino’s Takes Data to the Next Level

Many companies today talk about leveraging data or being data driven, but Domino’s is one company that is truly putting these words into action. The Domino’s data science team is using artificial intelligence (AI)/machine learning (ML) models to completely revolutionize the company at its core. Domino’s data science team provides the company with a modeled view into the future state of its markets, providing actionable guidance on store placement and staffing. The data science team is leveraging AI/ML models to improve in-store operations, discover untapped revenue sources, and most importantly, to continually improve the customer experience. Putting Models to Work… Read More


The Machine Learning Lifecycle and MLOps: Building and Operationalizing ML Models (Part I)

Machine learning was supposed to make things easy by computerizing human cognition. But it made life harder than ever for the data teams tasked with implementing it. A rising number of enterprises implement machine learning (ML) to improve revenue and operations as they digitally transform their businesses. But ML introduces operational complexities and risks that need careful attention. Data teams must holistically manage the ML lifecycle to make their projects efficient and effective. This blog kicks off a series that examines the ML lifecycle, which spans (1) data and feature engineering, (2) model development, and (3) ML operations (MLOps). This… Read More