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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Introduction When we work with classification or regression models we are seeking to predict either a discrete value, or a value along a continuum. The accuracy of these models is implicit in the metric we use when training: either in the cost function during the actual training, or something like
Read moreIntroduction 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
Read moreIntroduction This is the next article in my collection of blogs on anomaly detection. This time we will be taking a look at unsupervised learning using the Isolation Forest algorithm for outlier detection. I’ve mentioned this before, but this time we will look at some of the details more closely.
Read moreWe can all think of examples of successful forecasting, but what should we be aware of and what pointers can we adopt going forward?
Read moreThe M-competitions compare and evaluate different approaches to, and implementations of, time-series forecasting. Here is a brief review of the latest one, M5
Read moreIntroduction Unsupervised anomaly detection with unlabeled data – is it possible to detect outliers when all we have is a set of uncommented, context-free signals? The short answer is, yes – this is the essence of how one deals with network intrusion, fraud, and other types of low-instance anomaly. In
Read moreInstead of randomly or exhaustively iterating through combinations of algorithms and parameters, we can use Bayesian Optimization libraries to build up an in-memory approximation to the process we want to fine-tune. We can then make a our selections on prior knowledge.
Read moreTime-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.
Read moreTime-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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