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AI

Continual Earns SOC 2 Type 2 Compliance

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.

Build an AI App in Under 20 Minutes

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.

[MLOPS] From experiment management to model serving and back. A complete usecase, step-by-step!

The recording of our talk at the MLOps World summit. This talk covers a complete example, starting from experiment management and data versioning, building up into a pipeline and finally deploying using ClearML serving with drift monitoring. We then induce artifical drift to trigger the monitoring alerts and go back down the chain to quickly retrain a model and deploy it using canary deployment.

How SightX Uses ClearML to Build AI Drone Models

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.

[TALK] Model Serving Monitoring and Traceability: The Bigger Picture

The recording of our talk at the AI infrastructure alliance micro summit. This talk covers ClearML serving including monitoring and focuses on the importance of being able to trace the deployed model all the way back to the original experiment, code and data that were used to train it! One of the mayor advantages of a single tool end-to-end MLOps workflow.