We designed our system to fit the needs of data scientists and engineers, no matter which ML tasks they are tackling. Having said that, we’ve seen some use cases come up repeatedly within each industry.
As AI adoption increases within financial institutions, machine learning systems are taking an increasingly large role in their underlying infrastructure. However, in many cases, there is a trade-off between rolling out different versions of these ML systems quickly and maintaining control over them and their behavior. Deepchecks offers a comprehensive solution for continuous validation, enabling the ongoing inspections of the data and the models during a few different phases: pre-launch, production, and re-training. It’s a bit counter-intuitive, but once organizations have these types of guardrails and audit mechanisms in place, they can actually innovate faster!