Building a MLOps Platform with Kubeflow on GKE | Joinal Ahmed & Nikhil Rana 

About the talk:
Machine Learning (ML) capabilities are increasingly being adopted by enterprises to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization. However, building an ML platform can be a challenging task as every organization and ML project have unique requirements, and there are many options for ML Platforms. In this talk proposal, we will discuss the importance of ML Platforms and the benefits of standardizing the model development and deployment workflow. We will focus on using two popular OSS projects - Kubeflow and Ray - to support the basic ML user journey of notebook prototyping to scaled training to online serving. We will also demonstrate how Kubeflow provides the multi-user environment and interactive notebook management, and Ray orchestrates distributed computing workloads across the entire ML lifecycle, including training and serving. Google Kubernetes Engine (GKE) simplifies deploying OSS ML software in the cloud with autoscaling and auto-provisioning. We will show how platform builders can deploy Kubeflow and Ray to GKE to provide a comprehensive, production-ready ML platform. In summary, this talk proposal will provide attendees with a detailed understanding of the importance of ML Platforms, the benefits of standardizing the model development and deployment workflow, the role of OSS in building an ML platform, and how to deploy Kubeflow and Ray to GKE to create a comprehensive, production-ready ML platform.
About Joinal:
Data Science professional with strong technical understanding. Experienced in managing cross functional teams of data scientists, data engineers, backend developers and SRE delivering end-to-end ML projects, recruiting and mentoring data scientists and ML engineers across levels. Streamlining ML workflows for high performing teams, setting up best practices, and developing highly performant and reliable ml platform supporting an end to end ml projects lifecycle.