| Key | Value |
|---|---|
| image | https://assets.ubuntu.com/v1/0b41e7c4-AI+ML_AW_white.svg |
| image_width | 350 |
| image_height | 226 |
| meta_image | |
| meta_copydoc | https://docs.google.com/document/d/1f6xhnk2GDv6VnZZyuioWzgWg4Ts6h2U6h2E01qEjnIU/edit# |
| banner_class | grad |
| webinar_code | 463651 |
| primary_cta | Register for the webinar |
| primary_link | #register-section |
Open source technologies, like Kubeflow, are increasingly being used for AI/ML and predictive modeling across industries. However, operationalising the model and building the AI/ML infrastructure requires planning, automation and a lot of DevOps resources.
Our latest webinar provides real world application in finserv AI, with a demo on building and training an AI/ML model on bare metal using MAAS, Kubeflow, Kafka and ElasticSearch to predict whether the S&P 500 will close positive or negative on the current day, drawing on past data. Canonical engineers also provide details on the operations-side of AI/ML, with infrastructure considerations, automation tooling and additional services available.
In this webinar you will learn:
- Details of an ML, production use case for end-to-end wiring using multiple applications
- Demo of building and training an AI/ML model for predicting S&P performance
- Highlight additional solutions and tooling to implement, secure and manage open source technologies in production