Knative, serverless without vendor lock-in

 

How serverless works:

Serverless means literally “without server” and it refers to a way to deploy and execute applications where the owner of the application doesn’t have to worry about the server and infrastructure used to serve it. The only responsibility of developers is to write the code needed for the application to work properly and the cloud provider is the one who has to dynamically provision  the server, deploy the application in it, and make it work.

Because the code is totally independent of the platform and server on which it will be deployed, the developer doesn’t need to define endpoints or listeners to communicate with the outside world, instead of this, the cloud provider is the one responsible for providing the means to communicate with other services using events that trigger the execution of the application logic.

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Implementing the statsmodels flavor in MLflow

MLflow, the open-source platform released by Databricks in June 2018, has found a quick and broad acceptance in companies around the world. As stated by Databrick’s co-founder and CTO Matei Zaharia in their presentation post, there were a lot of different tools prior to this, created to track and/or take ML models into production, but they were either proprietary, language-dependent, or able to address only one aspect of the complete Machine Learning lifecycle. MLflow aims to be open in all senses – open source software but also language-agnostic (open interface).

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