The Winding Road to Better Machine Learning Infrastructure Through Tensorflow Extended and Kubeflow

As a result, our customers rarely have to build their own Docker containers, once again focusing less on infrastructure and more on their core use case. By having this controlled layer between the user and Kubeflow, we can easily manage upgrades of Kubeflow and TFX. We launched the alpha version of our platform in August and so far we have already seen about 100 users totaling 18,000 runs. Machine learning engineers can now focus on designing and analyzing their ML experiments instead of building and maintaining their own infrastructure, resulting in faster time from prototyping to production. In fact, early analysis indicates some teams are producing 7x more experiments already!