Feature Store

Machine learning models require high-quality, well-organized features for training and inference. Qwak's Feature Store serves as a centralized repository that bridges the gap between raw data and machine learning features. This guide will walk you through the process of leveraging Qwak's Feature Store with Snowflake, a cloud-based data warehousing platform, to manage and serve your machine learning features effectively.

Today's businesses are rushing to implement Artificial Intelligence (AI) and Machine Learning (ML) systems to boost their profitability and participate in what most call the fourth industrial revolution.

Feature stores are rapidly growing popular as organizations look to improve their machine learning productivity and operations (MLOps). With the advancements in MLOps, feature stores are becoming an essential component of the machine learning infrastructure, helping organizations to improve the performance and explainability of their models, and accelerate the productionization of new models.

A machine learning model is only as good as the data that it is trained with. Or to be more precise, it can only be as good as the features that have been built into it. We all know that. 

Building a machine learning model is hard. Deploying it is even harder. Running experiments, writing code for production, integrating with infrastructure, running ongoing tests, and avoiding training-serving skew – none of this is easy.

If you have decided to implement machine learning into your app or product and are exploring your options, you have probably come across terms like ‘online machine learning’ and ‘batch learning’ – but what exactly do these terms mean and why do they matter?