Companies are struggling to realize returns with their data insights and put AI and ML projects into production. As such companies are looking for people with the practical skills to bring value to their AI projects and operationalize their data insights and ML models while leveraging products that can help accomplish this at scale, at ease and at low cost.
In this session you will learn:
- The key challenges with MLOps
- How to operationalize ML projects
- How to deploy ML models in a repeatable, scalable process
- Monitoring model performance
- Keeping the models up and running and scaling for the future.
Nina Zumel
Nina Zumel has a Ph.D. in Robotics from Carnegie Mellon University and over 20 years of experience practicing and teaching analytics, machine learning and data science. In her roles as VP of Data Science at Wallaroo and Co-Founder & Principal Consultant for Win Vector LLC, she has led or been involved in engagements pertaining to adword revenue attribution, customer transaction models, product recommendation systems, and loan risk modeling.
Dipika Jain
Dipika Jain is the product manager at Wallaroo focussing on Model management & Deployment. Prior to Wallaroo, Dipika led Software Product Development team at Applied machine learning group at Intel focusing on Incubating AI products for optimizing deep learning models and frameworks on Intel hardware
Jul 12, 10:00 AM PDT 
