AI Observability

The pivot of the modern AI/ML stack

AI Observability Framework
AI Observability Framework
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

πŸ—οΈ High effort and time spent on maintaining and fixing existing solutions
πŸ” More iterations with customers during performance dips and downtime
⏲️ Longer time for root cause analysis and debugging
⚠️ Delayed alerts on violations and potential violations
πŸ’€ Workforce unable to focus on new strategies or solutions

Long Term impact

πŸ™…πŸΎβ€β™‚οΈ Loss of customer trust due to recurring issues and unreliable AI
πŸ”» 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.