The Kubeflow project is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.
Dan Anghel dives into installing and using Kubeflow Pipelines to create a full machine learning application on Kubernetes so you can become familiar with Google Cloud Platform tools such as Cloud Shell and Kubernetes Engine. You’ll start with an empty environment and create a Kubernetes cluster and install Kubeflow from scratch. You can build and run a full pipeline that does distributed training of a TensorFlow model, then scales and serves the trained model and deploys a web frontend for requesting predictions from the model. Dan teaches you how to use a Jupyter notebook to build and run a pipeline using the Kubeflow Pipelines SDK.
Dan Anghel is a strategic cloud engineer with Google after a more than 10 years’ long adventure in retail. Specialized in machine learning and big data, he’s helping the largest Google customers accelerate their journey into the cloud. Besides AI and machine learning, he’s been passionate about metal music since childhood, so there’s a great chance you will find him at the concert whenever a cool band comes to town.
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