In my previous blog post „how to manage machine learning models“ I explained the difficulties within the process of developing a good machine learning mod
In the past few moths a slew of Machine Learning management platforms arose. In this article we have a look at ModelDB which supports data scientists by keeping track of models, datasources and parameters. If you use scikit-learn or SparkML it promises easy integration and offers additional visualisation tools.
Developing a good machine learning model is not straight forward, but rather an iterative process which involves many steps. Mostly Data Scientists start by building
Here's what changes when your data science project grows from a proof of concept. How do you deploy your model, how can updates be rolled out, ...?
A common pattern in most data science projects I participated in is that it’s all fun and games until someone wants to put it into production. From that point in time