Hassle free, scalable, machine learning learning with Kubeflow
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Presentation on using Kubeflow from the perspective of Data Scientist given on 14th of March at TensorFlow London Event (bit.ly/2oMsv7F)
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Hassle free, scalable, machine learning learning with Kubeflow
- 1. Hassle free, scalable Machine Learning with Kubeflow Barbara Fusinska @BasiaFusinska
- 2. About me Machine Learning Strategic Cloud Engineer @BasiaFusinska barbarafusinska.com barbara@fusinska.com https://katacoda.com/kubeflow/
- 3. Agenda • What is Machine Learning, Deep Learning and AI? • Introduction to Kubeflow • Data Scientist perspective
- 4. Machine Learning Deep Learning Artificial Intelligence
- 6. Brief History of Neural Network
- 7. What has changed? • Big Data • The Cloud • GPU • Training methods • Tools • Computer Vision, NLP
- 10. Kubernetes • Container orchestration • Automatic deployment • Distributed computing • Open-source
- 11. Kubeflow • Composable, Portable, Scalable ML Stack for Kubernetes • Easy, repeatable deployments • Components: • JupyterHub • TensorFlow Job Operator and Controller (Custom Resources, TFJob) • TensorFlow Model Serving
- 13. Data Scientist perspective Code Algorithm choosing Hyperparameter tuning Adjusting the architecture Building the model Running production code Hardware exploit Model serving Model consumption
- 14. Kubeflow for Machine Learning Code Jupyter Notebooks TFJobs TFJobs Jupyter Notebooks Model Serving