Retrieving relevant content from huge volumes of data remains a challenging task. In this blog post, we'll look into a way to query DAM systems more efficiently with the help of Princeton's WordNet. (...)

Optimization is hard. Often, all that's known about a challenge is the desired outcome, but not the way to get there. In this post, we'll look into solving non-trivial problems automatically with the use of the hyperopt (...)

Controlling the quality of large volumes of training data is a complex and time-consuming task. In this post, we'll look into the problem of using noisy data in machine learning from an unconventional perspective - by ma (...)

Calibration is important, albeit often overlooked, aspect of training machine learning classifiers. It gives insight into model uncertainty, which can be later communicated to end-users or used in further processing of t (...)

The demand for machine learning has been growing constantly over the years. Despite more and more people getting interested in this field, the supply of experts seems to still be lagging behind. In this blogpost, we'll u (...)

No computer system is infallible. When something goes wrong, syslogs are often the first place to look at while diagnosing the issue. With systems growing more and more complex, analyzing millions of log entries has beco (...)

The introduction of AEM as a Cloud Service marks a new era in the world of AEM development. This post provides a tutorial of integrating Cloud Manager, a new CI/CD tool from Adobe, with existing Jenkins-based approaches (...)