In this article, we’ll take a deep dive into the customer churn/retention use case. This should contain everything needed to get started on the use case, and enterprising readers can also try this out for themselves in a free trial of Continual, following the customer churn example in the linked github repository.

Is your model ready for production? It depends on how it’s measured. And measuring it with the right metric can unlock even better performance. Evaluating model performance is a vital step in building effective machine learning models. As you get started on Continual and start building models, understanding evaluation metrics helps to productionize the best performing model for your use case.

The iPaaS market is clearly growing now at a faster pace than ever anticipated. This is not only due to the pandemic forcing companies to accelerate the digital transformation, but also in general due to the rise of SaaS. The replacement of older, server-bound software solutions with more modular, user-friendly and flexible SaaS solutions means customers need to connect these disparate cloud systems somehow if they want their business to become truly data driven.

The start of a new year is a perfect time to reflect on what was accomplished and look forward, re-evaluate what we can do better. Change, although difficult at first, can also be very rewarding. That’s why I was excited to see similar sentiments shared at Thoughtspot beyond.2021 to move beyond the traditional dashboards of the past.

AI models get smarter, more accurate, and therefore more useful over the course of their training on large datasets that have been painstakingly curated, often over a period of years. But in real-world applications, datasets start small. To design a new drug, for instance, researchers start by testing a compound and need to use the power of AI to predict the best possible permutation.