Let’s meet at the Toronto Machine Learning Summit!
We are looking forward to a great event this week at the Toronto Machine Learning Summit! @Sue Dunnell @Jeremy Jiang @Max Bergstein and Srikar Srinath from the Vianai team will be onsite to join the discussion and explain how we can help data scientists, machine learning engineers, machine learning operations teams and others.
We are ready to help businesses bridge the gaps across the AI lifecycle to monitor models for drift, bias, uncertainty and other risks, monitor models at a very large scale, and dramatically increase model execution speed and throughput.
We’ve worked with companies to accelerate model inference speed more than 100x while reducing the model’s footprint by 300x. We are able to run state-of-the-art AI models on limited compute surfaces without taxing firewalls or routers.
We also know last-mile issues cause delays when figuring out when to retrain and redeploy models. Our next-generation monitoring capabilities help eliminate alert fatigue, and make it easy for ML engineers – or anyone tasked with monitoring models – to understand why models are drifting, and we suggest actions to take to keep models trustworthy while running in production.
We call it our continuous operations process, and it includes wizard-driven policy creation for each model, highly flexible and granular custom thresholds for distance and window-based monitoring, and the ability to view billions of data points in a single window. We provide retraining recommendations and make it easy to use advanced deployment techniques like challenger/champion models. There’s a lot more, and we’d love to tell you all about it.