Congratulations! You’ve researched, developed, and deployed your model. Obviously, the next step is monitoring. Now it’s tempting to focus on the technical and dive into drifts and data anomalies, but there are other critical organizational challenges that can negatively impact your ML operations just as severely. In this talk, we covered both hard organizational challenges like building signal vs noise tolerances and soft organizational challenges like stakeholder identification and aligning expectations. We also shared some best practices on how to lead model observability discovery in your organization and build measurable KPIs for success.
In this episode
Co-founder and CEO, Superwise
Oren is the co-founder and CEO of Superwise, the leading platform for Model Observability. With over 15 years of experience leading the development, deployment, and scaling of ML products, Oren is an expert ML practitioner specializing in MLOps tools and practices. Previously, Oren managed machine learning activities at Intel’s ML center and operated a machine learning boutique consulting agency helping leading tech companies such as Sisense, Gong, AT&T, and others, to build their machine learning-based products and infrastructure.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.