Following Continual achieving SOC 2 Type 1 compliance in January, we’re proud to announce we are now SOC 2 Type 2 compliant. This milestone demonstrates our ongoing commitment to helping our customers protect their data – and their customer’s data – as they build and grow their operational AI platforms. It’s a hard reality for many software projects that security is added late in their development cycle as their market viability becomes clear.
Fully automatic retraining loop using ClearML Data Right, so you want to create a fully automatic retraining loop, that you can set up once and then pretty much forget about. Where do we even start?!
Machine learning is more accessible than ever, with datasets available online and Jupyter notebooks providing an easy way to explore and train models. In building a model, we often forget that it will be incorporated into an application that will provide value to the user. Therefore, we wanted to demonstrate how we can "use" the models we build in an application.
With the rise of drone usage, it’s easier to take aerial footage than ever before. The resulting data can trigger quick, effective action; removing guesswork and increasing aerial awareness, which can have profound implications on growing profits and trimming expenses. And as drone use rises, so does the usage of AI, to navigate, detect, identify, and track meaningful artifacts and objects.
On the heels of announcing our $14.5M Series A and General Availability, we’re excited to be at the Data + AI Summit to unveil support for Continual on the Databricks Lakehouse. Increasingly, data and ML tool providers are embracing a data-centric approach to the ML workflow. The goal is to focus on what increasing drives ML – the data – compared to infrastructure, algorithms, or pipelines. At Continual we bet on data-centric AI from day one.
The modern data stack continues to attract companies who are looking for a quick onramp into the world of cloud-based analytics and/or actively modernizing their legacy data stacks. We've enumerated the benefits of the modern data stack in previous articles.
In this tutorial, we’re going to build an interactive customer Churn Insights Dashboard using the open-source Python framework, Streamlit, and the Continual predictions generated in Part 1: Snowflake and Continual Quickstart Guide. In Part 1, we connected Continual to Snowflake and used a simple dataset of customer information, activity, and churn status to build and operationalize a machine learning model in Continual to predict the likelihood of a customer churning.
Sometimes the need for processing power you or your team requires is very high one day and very low another. Especially in machine learning environments, this is a common problem. One day a team might be training their models and the need for compute will be sky high, but other days they’ll be doing research and figuring out how to solve a specific problem, with only the need for a web browser and some coffee.
