In a recent project and task, I showcase how we use Fiddler AI's key features like AI observability and monitoring, which track hundreds of metrics across traditional machine learning models and large language models to monitor performance, data drift, and accuracy. We use it as security and guardrails that implement real-time safety measures to block harmful outputs and protect against jailbreak and prompt injection attempts. We also utilize it for explainability, helping teams understand why an AI model made a specific prediction or decision, providing deep root cause analysis when problems occur. Lastly, we employ it for governance and compliance, creating centralized audit trails and model cards to help companies meet strict regulatory standards and risk management requirements. Fiddler AI has significantly impacted my organization positively, particularly in security and guardrails. As we use many tools, we previously had humans monitoring all of them. Human errors created loopholes, but now with Fiddler AI, which is trained using our company policies and playbooks, human intervention has become minimal. We have reduced human intervention by 70% and human mistakes by around 90%, which are the points I wanted to mention. Reducing human intervention by 70% directly affects our productivity and day-to-day work. A person who worked with one policy or guardrail for a week now hardly takes one day, saving us six days of effort for each cycle. Moreover, the reduction in human mistakes has significantly improved product quality, with great feedback from vendors and other businesses using the product, increasing confidence levels and scores when using Fiddler AI.
