Knoldus Inc

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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.

Presenters:

Knoldus

Sudeep James
Software Consultant at Knoldus Inc.

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