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This guide will show you how to easily add Continual as the AI layer to your modern data stack with Snowflake at the core. The intention is to provide an introduction to using Continual on Snowflake. After completing this tutorial, users are invited to try more advanced examples. We are going to demonstrate connecting Continual to Snowflake, building feature sets and models from data stored in Snowflake, and analyzing and maintaining the predictive model continuously over time.

Unravel’s AI-enabled full-stack observability for the modern data stack simplifies the way data teams monitor, observe, manage, troubleshoot, and optimize the performance and cost of large-scale data applications. Unravel also accelerates data migration to the cloud by providing a data-driven assessment plan to save time and cost while also enabling you to deploy effortlessly.

Continual is proud to announce that we are now SOC 2 Type 1 certified and compliant and SOC 2 Type 2 in progress. This certification is a publicly visible milestone that demonstrates our core commitment to keeping your data secure. We expect to make additional announcements around our security certification efforts over the coming months. Beyond third party attestations, Continual is built from the ground up with data security and governance in mind.

A Simple Hyperparameter Optimization Guide.

Everyone from managers to C-suite executives wants information from analytics in order to make better decisions. Business analytics gives leaders the tools to transform a wealth of customer, operational, and product data into valuable insights that lead to agile decision-making and financial success. Traditional business intelligence and KPI dashboards have been popular solutions but they have their limitations.

For a quick refresher on what hyperparameter optimization is and what frameworks and strategies are supported by ClearML out of the box, check out our previous blogpost!

By incorporating AI and machine learning into mobile testing tools, teams can become more efficient in test automation. In this article, we'll look at how the adoption of AI and machine learning will improve these tools and what the future of testing might look like.