Machine Learning model deployment can be a complicated undertaking, but it doesn’t have to be overly complex. During this 60-minute event, presented by Dr. Andrew Clark and Sid Mangalik, we will cover:
- An introduction to what elements are required for ML decision assurance and reproducibility
- How Monitaur supports best practices while remaining flexible to individual deployment paradigms
- Full-length example of best practices when deploying a Scikit Learn pattern ML model with a REST API within a Docker container
- Examples of ungovernable deployments and what not to do
This presentation is built for data scientists who want to strengthen their foundational understanding of model deployment as well as data science leaders who want to create paradigms for their teams to follow. It's also geared toward anyone passionate about Data Science, Python, and Responsible AI.
Enjoy the replay of this best practices session!