Today, I’m excited to share that I’ve joined Continual as Head of Marketing. Continual is radically simplifying the path to operational AI with the first continual AI platform built for the modern data stack. More in a bit on what that means, but the “so what?” is about opening the door for more organizations to embed AI across their business at scale.

Feature stores have arrived in 2021 as an essential piece of technology for operationalizing AI. Despite the enthusiasm for feature stores in high-tech companies, they are still absent from most legacy ML platforms and can be relatively unknown in many enterprise companies. We discussed how feature stores are critical to the data-first approach of next-gen ML platforms in our previous blog, but they are important enough to get their own treatment in a full article.

We are roughly a decade removed from the beginnings of the modern machine learning (ML) platform, inspired largely by the growing ecosystem of open-source Python-based technologies for data scientists. It’s a good time for us to reflect back upon the progress that has been made, highlight the major problems enterprises have with existing ML platforms, and discuss what the next generation of platforms will be like.

The Modern Data Stack is quickly picking up steam in tech circles as the go-to cloud data architecture, and although its popularity has been quickly rising, it can be ambiguously defined at times. In this blog post we’ll discuss what it is, how it came to be, and where we see it going in the future. Regardless of whether you’re new to the modern data stack or have been an early adopter, there should be something of interest for everyone.

I’m extremely excited to introduce Continual. Continual is the easiest way to maintain predictions – from customer churn to inventory forecasts – directly in your cloud data warehouse. It’s built for modern data teams that want to leverage machine learning to drive revenue, streamline operations, and power innovative products and services without complex engineering.