MLOps Cocktails Done Right How to Mix Data Science, ML Engineering and DevOps
Looking to make it easy for your Data Science, ML Engineering, and DevOps teams to deliver AI/ML solutions? Dive in to learn how you can build more productive AI delivery teams and how to build a scalable and secure ML Infrastructure to make your AI teams even more productive.
During the webinar, you will explore all ingredients that come into an MLOps cocktail, including workflow automation, pipeline orchestration, data quality and data QA, as well as metadata management for various ML assets — with a focus on people and infrastructure. Provectus experts will explain how you can use ML infrastructure to reduce time to market for new ML applications with minimal disturbance in the workflow.
Agenda
Highly productive mix of ML, DS, and DevOps
ML Infrastructure for highly productive AI teams
Workflow automation and Pipeline orchestration
Data quality and Data Quality Assurance
Metadata management for various ML assets
Schedule a 30-minute pre-assessment session for ML Acceleration Program https://provectus.com/ml-infrastructu...
The program is fully funded by Provectus and AWS and available to selected customers upon qualification.
Intended Audience
Data Science Managers, Engineering Managers, ML Engineers, Data Scientists, DevOps & Infrastructure teams, Technology Executives & Decision Makers
Presenters
Stepan Pushkarev, Chief Technology Officer at Provectus
Rinat Gareev, ML Solutions Architect at Provectus
Lenar Gabdrakhmanov, ML Engineer at Provectus