Content tagged with "Mlops"

Introduction

Open machine learning research is undergoing something of a reproducibiltiy crisis. In fairness it’s not usually the authors’ fault - or at least not entirely. We’re a fickle industry and the tools and frameworks were ‘in vogue’ and state of the art a couple of years ago are now obsolete. Furthermore, academics and open source contributors are under no obligation to keep their code up to date. It is often left up to the reproducer to figure out how to breathe life back into older work.

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I recently came across an infuriating problem where an MLFlow python model I had trained on one system using Python 3.6 would not load on another system with an identical version of Python.

The exact problem was that when I ran mlflow models serve -m <url/to/model/in/bucket> the service would crash saying that the model could not be unserialized because ValueError: unsupported pickle protocol: 5.

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MLFlow is a powerful open source MLOps platform with built in framework for serving your trained ML models as REST APIs. The REST framework will load data provided in a JSON or CSV format compatible with pandas and pass this directly into your model. This can be handy when your model is expecting a tabular list of numerical and categorical features. However it is less clear how to serve with models and pipelines that are expecting unstructured text data as their primary input. In this post we will explore how to train and then serve an NLP model using MLFlow, scikit-learn and spacy.

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