Machine learning (ML) models can help organizations gain a competitive advantage by finding patterns in data to help prevent equipment breakdowns, persuade customers to buy more, or capitalize on myriad other business events. Yet most of the expected value is never realized because organizations are unable to align all the data, teams and processes required to get from proof of concept to production.
ML Operations around the ML lifecycle-- that is, preparing data, building a model, and operating it -- can provide you with cohesive orchestration of the many moving parts.
Watch this webinar to learn how addressing MLOps practices can help you:
- Align stages of the ML lifecycle with key stakeholders, including: data scientists, data engineers, ML engineers, DevOps engineers, and governance officers
- Scope the role of a cloud data platform in supporting the MLOps lifecycle and its stakeholders
- Adopt guiding principles to optimize and scale MLOps on the Data Cloud