Conference:  KubeCon + CloudNativeCon North America 2022

Authors: Saravanan Balasubramanian, Savin Goyal

2022-10-27

The presentation discusses the challenges of introducing machine learning into applications and the need for infrastructure that can provide end-to-end solutions for the entire life cycle of machine learning. It also covers the importance of workflow orchestration and reproducibility in machine learning.

• Infrastructure that can provide end-to-end solutions for the entire life cycle of machine learning is necessary for successful implementation of machine learning into applications
• Workflow orchestration is important for productionizing machine learning workflows
• Reproducibility is important for ensuring trust in machine learning models
• Model deployment can mean many different things depending on the business context

Conference:  Transform X 2021

Authors: Jack Guo, Anitha Vijayakumar, Vishnu Rachakonda, Oleg Avdeëv

2021-10-07

Hosted by MLOps Community. Panelist to be announced soon. Demetrios Brinkmann, founder of MLOPs.community leads a panel managing the increasing compute requirements of AI models, whilst striking the right balance between flexibility for experimentation and stability in production. As enterprises collect more training data, and in many cases label it with Scale AI, they face the challenge of their models growing in both size and compute complexity. Join this session to learn how companies can develop robust and maintainable pipelines to ensure that ML experimentation remains possible, despite increasing model sizes and longer training times. This session will also cover compute for lifecycle phases from experimentation to scaling (with Metaflow, TFX, etc.) pipelines that are ready to deploy to production, including via microservices.