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 -

  • Your ML applications can run on any platform without any porting or testing required.
  • Once packaged into containers, ML systems can be exposed as microservices. This allows external services to (containers or not) to leverage them at any time without the need to integrate the code inside the application.
  • ML applications that exist within containers can easily be scaled by placing them on cloud-based systems or on container management systems like Kubernetes or Swarm.
  • 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 -


    Sudeep James

    Software Consultant at Knoldus Inc.

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