This is the second article in our series on data access best practices. In the first one we looked at the reasons why data access can be challenging for organizations and highlighted some of the key principles that are good to have in mind when setting up this process. In this one we will show you how to protect your sensitive data.
We discuss why data access is a surprisingly difficult problem to solve for large organizations. Neglecting this problem poses underappreciated yet existential risks to a company's ML strategy. We propose a few principles that can help enable data access in a way that both enables innovation and manages risk effectively.
We look at the benefits of MLOps and why you might need it for your company.
We have built Rust bindings to the TileDB tensor storage engine. As good Rustaceans do, we're open-sourcing the bindings and are quite excited about the possibilities that universal tensor storage open up.