Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Published on
Here I describe the main challenges faced by data scientists involved in deploying machine learning models into real production environments.
I also include references/examples of Python libraries and multi-model systems requiring advanced features such as A/B testing and high scalability/availability.
While discussing the limitations of traditional deployment strategies, I will demonstrate how serverless computing can simplify your deployment workflow.
Login to see the comments