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
After weeks of training and optimizing a neural net at some point it might be ready for production. Most deep learning projects never reach this point and for the rest it’s time to think about frameworks and technology stack. In…
This is the last post in the series about machine learning in practice. This time the post will be about productionizing machine learning models. I want to share my experience from several production machine learning systems and show how it…
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…