Find out how data teams can share & discover features, and also create effective ML pipelines.
With the transformational early successes in value creation, AI/ML is set to become ubiquitous. By 2030, AI could potentially contribute up to $15.7tr to the global economy. As more and more organizations are depending on data and Machine Learning (ML) models for their crucial decision-making, the security of data and these ML systems is business […]
Over the past 3 years, we’ve heard a lot about Feature Stores. While they might not sound like much, over time, they’ve become table stakes for enterprises building their offerings on ML. The rapid adoption of feature stores, where they’re starting to become mainstream instead of being a niche restricted to big-tech, can largely be […]
As industries across the globe attempt to adapt to the big data architecture, expensive and ineffective feature engineering practices mean that businesses are very likely to “hit a wall” when it comes to organizing their machine learning operations (MLOps). A lot of time is consumed in data ingestion, and lackluster machine outputs indicate that stakeholders […]
Let’s assume you want to leverage data to improve one of your processes, such as partner benchmarking. Even though it’s one of your top priorities for the year, you have limited resources to spend on partner data collection, segregation, and overall data preparation to do any sort of analysis. And even if you find a […]
