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Frameworks for Machine Learning Model Management

2021-02-10T09:12:04+00:00

This blog post will compare three different tools developed to support reproducible machine learning model development: MLFlow developed by DataBricks (the company behind Apache Spark), DVC, a software product of the London based startup iterative.ai, and Sacred, an academic project developed by different researchers.

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

Frameworks for Machine Learning Model Management2021-02-10T09:12:04+00:00

How to Manage Machine Learning Models

2021-02-10T09:12:27+00:00

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

How to Manage Machine Learning Models2021-02-10T09:12:27+00:00

Data Science in Production: Packaging, Versioning and Continuous Integration

2021-02-10T09:13:06+00:00

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

Data Science in Production: Packaging, Versioning and Continuous Integration2021-02-10T09:13:06+00:00