The pivot of the modern AI/ML stack
Most of us have witnessed the software evolution from CRUD systems to a reliable ecosystem of SaaS applications. The AI parallel is the evolution from unreliable AI solutions to an ecosystem of trustworthy and responsible AI.
This is possible through the lever of AI Observability which is a holistic approach to drive insights into the model’s behavior, data, and performance across its lifecycle. It is an optimal combination of DataOps and MLOps.
Curious about more? We wrote an AI Observability Wiki
Why it’s the right time to adopt AI Observability
As AI Observability gets adopted industry-wide, there are some short-term and long-term consequences for delaying the adoption of observability into existing and new solutions.
Short Term impact
🏗️ High effort and time spent on maintaining and fixing existing solutions
🔁 More iterations with customers during performance dips and downtime
⏲️ Longer time for root cause analysis and debugging
⚠️ Delayed alerts on violations and potential violations
💤 Workforce unable to focus on new strategies or solutions
Long Term impact
🙅🏾♂️ Loss of customer trust due to recurring issues and unreliable AI
🔻 Loss of competitive edge in terms of quality of AI solutions
With Censius AI Observability platform, streamline and stabilize AI/ML projects with high model transparency and minimal maintenance efforts.