Recommender-Systems Version Control: TensorFlow releases ‘Machine Learning Metadata (MLMD)’
Version control for recommender systems is a topic that should receive more attention in the community. Given that TensorFlow is often used for implementing recommender systems, this blog post by Ben Mathes and Neoklis Polyzotis is a great step in the right direction:
[MLMD is] a library to track the full lineage of your entire ML workflow. Full lineage is all the steps from data ingestion, data preprocessing, validation, training, evaluation, deployment, and so on. MLMD is a standalone library, and also comes integrated in TensorFlow Extended. There’s also a demo notebook to see how you can integrate MLMD into your ML infrastructure today.
MLMD will help machine learning and recommender systems developers to document the following questions:
* Which dataset was this model trained on?
* What hyperparameters were used?
* Which pipeline was used to create this model?
* Which version of TensorFlow (and other libraries) were used to create this model?
*What caused this model to fail?
* What version of this model was last deployed?
A key question remains, namely whether MLMD will also work with TensorFlow Recommenders?
Read on in the TensorFlow blog https://blog.tensorflow.org/2021/01/ml-metadata-version-control-for-ml.html
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About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).