Convenient Containerization with MLFLow Project
Deploying even regular software applications into production is a difficult task to accomplish. It’s even worse when the application is a Machine Learning pipeline. Usually, Machine Learning models are packaged as standalone, executable entities. However, that presents a lot of problems like platform incompatibility, unscalable models, and inconsistent libraries, etc.
These problems can be solved by deploying Machine Learning models using Docker containers. This has several advantages -
This webinar will walk you through the process of containerizing Machine learning models with MLFlow so that they can be ready for deployment in production environments.
Software Consultant at Knoldus Inc.