Architecting MLOps: The Journey from Identifying ML Use Cases to the ML Platform Architecture

Great engineers often use back-of-the-envelope calculations to estimate resources and costs. This practice is equally beneficial in Machine Learning Engineering, aiding in confirming the feasibility and value of an ML project. In my talk, I’ll introduce a collaborative design toolkit for ML projects. It includes Machine Learning Canvas and MLOps Stack Canvas to identify ML use cases and perform initial prototyping, thus ensuring a business problem can be effectively solved within reasonable cost and resource parameters.

Target Audience: Architects, Developers, Project Leader, Data Scientist Prerequisites: Basic knowledge in machine learning Level: Advanced

17:00 - 18:00
Conference / Event
OOP 2024
Internationales Congress Center München, München