datascience

10 posts

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Java machine-learning libraries

Introduction In an earlier article I mentioned briefly some possibilities for bringing python-trained machine learning models into production. Specifically, a java environment. In this article I will take a closer look at how this can done and what pitfalls we should avoid. Keras In the words of the Keras website,

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Dask: Out-of-memory machine learning

Introduction Why Dask? For those of us who regularly work with python machine learning libraries, the Pandas DataFrame library is a firm fixture in our toolkit. Pandas DataFrames allow fast and efficient manipulation of data and a host of data wrangling functions. And the processing is fast because pandas does

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Predicting hidden events: inverting the time-to-event paradigm: Part 2

Time-to-event (TTE) use-cases crop up in many places across industries. Some examples would be: the prediction of customer churn (the sales domain), remaining-useful-life or time-to-failure TTF (predictive maintenance), or anomaly detection (machine monitoring). Some events are difficult to predict as they are hidden. We can instead try to look for interim events to improve prediction accuracy.

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Predicting hidden events: inverting the time-to-event paradigm: Part 1

Time-to-event (TTE) use-cases crop up in many places across industries. Some examples would be: the prediction of customer churn (the sales domain), remaining-useful-life or time-to-failure TTF (predictive maintenance), or anomaly detection (machine monitoring).Some events are difficult to predict as they are hidden. We can instead try to look for interim events to improve prediction accuracy.

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