Mitigating AI risks: Aporia Labs’ promise
With all the greatness that AI promises, it remains vulnerable to several risks, including AI hallucinations, prompt attacks, and data...
We are excited to announce that we have just released a revision of our documentation. It took a quick second but we rebuilt our docs stack from the ground up, using tools that are more flexible, secure, and allow for an extremely fast iteration cycle.
One of our main objectives at Aporia is to help organizations maximize the value of their ML models running in production, no matter how unique their use case may be. We achieve this by empowering data science and ML teams with a highly customizable observability platform to monitor for drift, improve model performance, and ensure their models drive business impact.
While it’s not an easy task to learn a new tool and introduce it into one’s workflow, we’ve invested a lot into building the visibility, monitoring, explainability, and root cause investigation needed to make it easy to get the most out of your production models.
Our new docs provide our users with a wide variety of technical guides on how to achieve effective monitoring and observability for models in production. We offer a technical breakdown of each model type – integration from start to finish, and how you can improve your model with the Aporia platform.
In addition, we are emphasizing our educational content on best practices and core concepts one should address when setting out on this hero’s journey to productionize ML. We carefully designed our new documentation with a focus on enhancing the user experience, resulting in the most seamless navigation yet.
As part of this new era, we are also excited to announce some cool new capabilities that can be achieved only on the Aporia platform:
We want to make sure you’re getting the best and most up-to-date information, always! ????
Easily find it on our main homepage. We’re not changing things up too much.
You made it this far, so check out our new and improved docs now.
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