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Walk through a real world ML production project.

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.

What you’ll learn in this session:

  • 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

Wallaroo levels the machine learning playing field:

Easy, Secure and Scalable Deployment
Launch 3X Faster
12.5X Faster Inferencing
80% Lower Server Costs