One of the important aspects of MLOps, also known as Machine Learning Operations or Operationalizing Machine learning, is to package ML models. How exactly do you package ML models? In this video I show you exactly what that means, and go through the process of packaging an ONNX model taken from the ONNX Model Zoo. I end up with a docker container that can be shared, exposing an API that is ready to consume and perform live predictions for sentiment analysis.
Topics include:
* What are the concepts behind packaging Machine Learning Models
* Create a sentiment analysis API tool with Flask
* Define dependencies and a Dockerfile for packaging
* Create a container with an ONNX model that can be deployed anywhere with an HTTP API

PEARSON OOWNS MAANY EDU BRAANDS INKLUDINNG
ADDISON-WESLEY PEACHPIT PRENTICE-HALL ECOLLEGE
AND MAANY MORE!

WEE ASUMME NO LIIABILLITY FUR A WROONG SPELING
COZ WEE ARRE A NON (iL)LiTERATE GRUP!!

TANNKS TU THE LITERATE ASKII FRIIEND!!