Managing Machine Learning Cycles: Five Learnings from comparing Data Science Experimentation/ Collaboration Tools

By Dr.

Michael Eble, Co-Founder and Managing Director with hlthfwd.

Machine learning projects come along with handling different versions of input data, source code, hyperparameters, environment configuration, and alike.

Usually, many model iterations need to be computed before machine learning models can be leveraged in production.

During this experimentation process, its artefacts and their interdependencies create an additional layer of complexity in data science projects.

In order to manage this complexity, we need proper structures and processes as well as adequate software tools that cover the machine learning cycle.

In case you are experiencing similar challenges, the following learnings from comparing such tools might be useful for you.

You can find a link to our raw data at the end of the article.

 For us, the respective tool needs to cover the following use cases alongside a typical machine learning workflow: We determined AI, dotscience, MLFlow, and Polyaxon target larger corporations/ enterprises, while Comet.

ml, Neptune.

ml as well as Weights and Biases address smaller organizations/ start-ups.

   Information about the tools was collected in Q3 and Q4/2019 and updated gradually.

We did not yet fully complete hands-on tests of all tools on our shortlist but reviewed relevant websites, GitHub repos and documentation.

Further investigation is carried out with respect to data visualization, location of servers (in case of PaaS/ SaaS), among others.

You can download the full spreadsheet containing more detailed data about each of the tools from GitHub.

 Bio: Dr.

Michael Eble is co-founder of a digital health start-up in Germany and is responsible for its data-driven business models.

Prior to his current position, Michael was Principal at the management consulting firm mm1 and head of the consultancy’s practice “Data Thinking.

” He led project teams for clients in various industries such as telecommunication and mobility with a focus on new product development.

Michael holds a M.

A.

in economics and management and a M.

A.

in computational linguistics and media studies.

He received his doctoral degree from the University of Bonn while executing data analytics projects at Fraunhofer institute IAIS.

Michael is currently completing his studies in computer science and is writing his master’s thesis about machine learning in the medical field.

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