Two people will interpret the same data in different ways. It is a norm, rather than exception. Due to the human factor (personal experience, emotions, deficiencies of human brain and tendency to fall for logical fallacies) understanding of the data
Porting PyTorch neural network to Amazon AWS
As part of my Sopra Steria engagement, I have been lately fortunate to spend time in the so-called Aerospace Valley, which is a cluster of aerospace engineering research centers in Toulouse, France. My recent task was to do with cloud
Porting pyTorch cloud detection model to Amazon AWS S3
As part of my Sopra Steria engagement, I have been lately fortunate to spend time in the so-called Aerospace Valley, which is a cluster of aerospace engineering research centers in Toulouse, France. My recent task was to do with cloud
pushing data to AWS. SageMaker sucks. So does Anaconda
I did a lot of tech work on the infrastructure underlying my analytics over the past weeks. I am putting my notes here so they don’t get lost and maybe help someone. Here are three stories, unrelated to each other.
Linear Regression: Killer App with 19-century maths
I often feel the gap between the mainstream Data Science rhetoric and the true business needs is widening. When I hear of Hyperautomation, Edge AI, AutoML, or GANs, I challenge myself to take a leap back, understand our needs better.
Democratization of statistics: Chi2 for non-experts
I am big fan of advanced methods deployed to solve practical problems by ordinary users. Here is our recent achievement. My colleague, an experienced service desk manager, observed that the volume of work in his team has grown. He would
An approach to categorize multi-lingual phrases
I have 130,000 help desk tickets with multi-lingual descriptions. I need to divide this set into categories, such as “password reset”, “license expired”, or “storage failure”. Why? Users could then allocate a category to a new ticket they create. Then
The implications of Scikit-learn bug #21455
As described last week, the Scikit-learn chi-square feature selection is not usable until the bug #21455 is addressed. The problem concerns sklearn.feature_selection.chi2 and the derivative methods, including SelectKBest, if used for categorical features other than binary. The nature of the
Your model may be inaccurate
With Machine Learning in Python, you may do feature selection with SelectKBest. As I just confirmed, this method sometimes returns faulty results. This potentially impacts the accuracy of numerous ML models worldwide. Below the details and the way out. The
Answering Why (with Chi-Square)
Analysts don’t like the “why” questions. They are tough to answer. For instance, in a help desk analysis, it is easy to show which tickets are resolved faster. But it is difficult to say why. In my practice in Sopra