How to use additional information from labels to train a plausibility mode Working with predictive deep learning models in practice often comes with the question how far we can trust the predictions of that model. While there are several frameworks…
data science
Data leakage in practice is a widely underestimated effect in machine learning, which happens especially where a lot of feature engineering is involved. Data leakage happened even in Kaggle competitions, where winners exploited these systematic flaws in the data. This…
This is a list of resources for ml/ai engineers and data scientists
Since Data Science was proposed as the sexiest job of the 21th century in 2012, a lot of people from all kind if different fields started to move to data science or related machine learning roles. Solving complex problems with…
Preprocessing and data transformation are the most important parts of all machine learning pipelines. No matter what type of model you use, if the preprocessing pipeline is buggy, your model will deliver wrong predictions . This remains also true, if…
In this How-To series, I want to share my experience with machine learning models in productions environments. This starts with the general differences to typical software projects and how to acquire and deal with data sets in such projects, goes…
In the past years a new job role has become very popular (at least I noticed it in job postings and media). The machine learning engineer. But what is the difference between this new job role and data engineering or…
I almost completed Nanodegree “Machine Learning Engineer” from Udacity and now its time for a first review. There are some positive and negative things to say, i don´t want to sugarcoat. I liked the projects and the reviewers and also some…