A Day in the Life of a Data Scientist Conquer Machine Learning Lifecycle on KubernetesArgo • Serve trained models for inference with TF Serving • Rapid prototyping with self-service Jupyter notebook from JupyterHub Simplified ML Workflow/Pipeline What is DevOps? • “A cross-disciplinary config • Tensorflow, PyTorch, MXNet, Chainer, and more • JupyterHub to create and manage interactive Jupyter notebooks • Model serving – serve exported models with TF Serving or Seldon • Additional components cost Demo: Create End to End ML Pipelines with Argo Demo: Rapid prototyping with self-service Jupyter notebook from JupyterHub What’s Next? one) Solution is Kubernetes: • Highly Scalable • Easy to0 码力 | 21 页 | 68.69 MB | 1 年前3
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