Shipping ML to production

How to Containerize Models Trained in Spark: MLLib, ONNX and more

Alternatives to package your models from Spark in a cost-effective manner

If we shall name two trends in modern applications, one should mention containers and machine learning. More and more applications are taking advantage of containerized microservices architectures in order to enable improved elasticity, fault-tolerance, and scalability — whether or on-premise or in the cloud. At the same time…