Building the
future of MLOps

Would you trust someone's decision making if they couldn't explain their reasoning, often made erroneous decisions, and were biased?

ML can sometimes be just that, yet it gets a free pass. It's black box where data goes in and decisions come out with limited transparency into why it did that.

Model Observability, continuous monitoring and explainability, is needed to drive a change and enable teams to be performant and understanding.

ML is no longer an afterthought

While it may be surprising to an outsider, an individual indirectly interacts with hundreds to thousands of models every single day. Even trivial decisions—what you eat, what you see, how you travel—are all powered by AI. We need to equip teams with the right tools to improve AI outcomes.

When things go wrong, lives are impacted

When algorithms decide if you get a loan or diagnose medical conditions, real lives are impacted. Being wrong is no longer an option and we need to do everything we can to continually monitor and improve the systems powering these decisions.

The fear of unknown slows down innovation

When a technology is feared, the default action is opposition—from stakeholders to governments. To enable greater adoption of AI, we need to break down barriers to understanding it and build trust. We can achieve this by bringing model observability to every team in every company.

Ayush Patel

Founder, Censius