MLOps Cocktails Done Right: How to Mix Data Science, ML Engineering, and DevOps
Learn to build a robust ML Infrastructure, to assure data quality, handle metadata, and ensure MLOps success
On-Demand Webinar
Model training is a small part of a typical ML project. Today, ML work comes with data, model deployment, monitoring, maintenance, and other challenging tasks that prevent organizations from building scalable, extendable, and reusable ML solutions. Request a webinar to learn how to build a robust ML infrastructure, to enable your ML Engineering, Data Science, and DevOps teams to reduce time to market for new ML applications. For implementation options, we will look into Amazon SageMaker and alternative open-source services.
You will learn about:
- Highly productive AI/ML/Data Science teams
- ML Infrastructure for highly productive AI teams
- Workflow automation and Pipeline orchestration
- Data quality and Data Quality Assurance
- Metadata management for various ML assets
Speakers:
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Rinat Gareev, ML Solutions Architect, Provectus
- Lenar Gabdrakhmanov, ML Engineer, Provectus
Who should attend:
- ML Practitioners & ML Engineers
- Data Scientists & Data Engineers
- DevOps & Infrastructure teams
- MLOps professionals
- Architects
- Technology executives
Let’s explore how to build a scalable and secure ML Infrastructure to make your AI teams more productive!
