Roaring Elephant

In this episode, Global Black Belt and Technical Architect in Big Data and Advanced Analytics Team at Microsoft, Alex Zeltov, is our guest and he explains the in's and out's of MLOps though various tools like mlflow and kubeflow In this second part, we go into more depth on the practical consequences of implementing MLOps and the various tools that are available. We also go on a bit of a tangent discussing why traditional enterprises are still having a hard time to look at machine learning models as something that requires and benefits from things like model management, version control and periodic updating of models. For more from Alex on MLOps and mlflow, check out his presentation at the Washington DC DataWorks Summit a couple, of weeks ago. The slides are now available on SlideShare and the video is available on YouTube: https://www.youtube.com/watch?v=Ns82mJjJgto MLOps Just like DataOps follows on to DevOps, one may say that MLOps continues after DataOps. While there is a wikipedia page on the su