Data science software teams find it tedious to implement ML workflows in a repeatable, maintainable and sustainable manner. Even if such a platform is developed, it has challenges with further inclusion of newer workflows or capabilities, portability across various infrastructure platforms (cloud, on-premise, and hybrid), scalability in terms of compute resources, and managing the number of teams using the platform.

In this talk, participants will learn about the Open Source Machine Learning Platform called Kubeflow. The Kubeflow project is “dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable”. Anywhere you are running Kubernetes, you should be able to run Kubeflow for your ML workloads. Through the live demo, participants will learn to use Kubeflow to create pipelines of different tasks which reflect their day to day ML tasks by using a Jupyter Notebook. The demo example will cover several components of a data-scientist’s day to day tasks including data pre-processing, training a model by first tuning hyperparameters through Katib, evaluating the model against test data and deploying it to serve predictions.

Outline

• Machine Learning is hard, maintaining is tougher (integrating with legacy systems, portability of the platform compared to other vendors)
• Kubernetes provides infrastructure extensibility
• Composability, portability and scalability on top of Kubernetes
• Acquiring Kubernetes skills to develop on may be challenging, hence the open source way!
• Develop, deploy and manage portable distributed ML on Kubernetes
• Features of Kubeflow: right from developing ML pipelines with hyperparameter tuning, training and serving with the help of Jupyter Notebook
• Pipeline example demo about TF MNIST (Jupyter Notebook) with hyperparameter tuning, training and serving
• Benefits: Democratizing Machine Learning - Show real life impact and social cause
• Who’s contributing?
• What’s next in Kubeflow?
• Pitch about being open / open source development
• About Community - Why? What? How? etc
• Contacts for reaching out to contribute or know about Kubeflow

Requirements

None

Speaker bio

Cisco AI,
BTech Computer Science from Visvesvaraya National Institute of Technology, Nagpur

Krishna currently works as an open source developer for Kubeflow, the platform which this presentation is about, under the Cisco AI Cloud CTO Team. Cisco AI, as a group, are ranked third in the number of contributions by lines of code to Kubeflow (http://devstats.kubeflow.org/d/5/companies-summary?orgId=1).
Krishna has an experience of 3 years in designing and engineering AI platforms having previously worked with 3 different start-ups, including SigTuple, an AI based medical analysis platform which developed a platform called ‘Kurma’. Kubeflow solves the same problems which Kurma addresses in a sustainable manner with Kubernetes as its infrastructure layer. This transformation from proprietary software for ML to open source versions of it helps him draw a picture of the paradigm shift which we faced as developers, trying to solve the same problems within the bounds of our firm.

Links

• Author’s LinkedIn: https://www.linkedin.com/in/krishnadurai/Author’s
• About Kubeflow: https://www.kubeflow.org
• Kubeflow GitHub: https://github.com/kubeflow/kubeflow
• Kubeflow blog - A year in perspective: https://medium.com/kubeflow/kubeflow-in-2018-a-year-in-perspective-49c273b490f4
• Kubeflow Demo Material: https://github.com/CiscoAI/KFLab/tree/master/tf-mnist, https://github.com/CiscoAI/KFLab/tree/master/pipelines/tf-mnist

Slides

https://docs.google.com/presentation/d/1jUPHGcHVosVQItS3KUTImDVqzgYkJYuBh6e1vJoSSzE/edit?usp=sharing