According to 451 Research, 96% of enterprises are actively pursuing a hybrid IT strategy. Modern, real-time businesses require accelerated cycles of innovation that are expensive and difficult to maintain with legacy data platforms. Cloud technologies and respective service providers have evolved solutions to address these challenges.
The Snowflake Data Cloud is a powerful place to work with data because we have made it easy to do difficult things with data, such as breaking down data silos, safely sharing complex data sets, and querying massive amounts of data. As customers move to the Data Cloud, their needs and timelines vary—our goal is to meet every customer where they are on their Data Cloud journey.
As anyone running modern data applications in the cloud knows, costs can mushroom out of control very quickly and easily. Getting these costs under control is really all about not spending more than you have to. Unfortunately, the common approach to managing these expenses—which looks at things only at an aggregated infrastructure level—helps control only about 5% of your cloud spend.
In this series of blog posts, we will showcase an end-to-end hybrid cloud ML workflow using the Iguazio MLOps Platform & Feature Store combined with Azure ML. This blog will be more of an overview of the solution and the types of problems it solves, while the next parts will be a technical deep dive into each step of the process.
