Success in productionizing ML models is difficult to achieve due to tools, processes and operational procedures. In this session, we demonstrate how data scientists and ML engineers collaborate and efficiently deploy models to production with the Wallaroo platform.
Using a real world scenario we will click down into the ML production journey that Data Scientists and ML engineers go through to take ML models into production. In this session you will learn:
The current pain points and blockers to production
The 2 persona roles in the ML production process. Data Scientist (DS) and ML Engineer
How the ML engineer creates a workspace in Wallaroo, and invites the DS to collaborate
How the DS uploads and deploys models to WL performing simple validation checks on output
How the ML Engineer can check model health (inference speed, etc)
How the DS checks logs, looks for anomalies
How the DS switches model in the pipeline
Nina Zumel
Nina Zumel Nina 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.
Martin BaldMartin Bald is the community lead at Wallaroo.ai. He has over two decades in the tech industry working with Microsoft. He has enjoyed roles in all areas of business from technical pre-sales, partner development, partner marketing, IT Pro evangelism, and developer communities across pro dev to student audiences. In his role with the developer community he has led many programs and projects involving meetups, webinars, workshops, hackathons, customer POCs in the IoT, AI and ML space across a variety of industries
Aug 23, 10:00 AM PDT 
