Angielski

Machine Learning has become a fundamental part of many companies’ data science efforts. But as models have gotten more complex, it can be difficult to know what is causing them to make certain predictions. That is why we observe fast increase of interpretability tools such…

Deploying machine learning models in a production environment is a significant challenge, often requiring as much effort as training the model itself. In this post, I will discuss one of several key aspects that need to be taken care of, which is logging all necessary…

Introduction Today, we will focus on the topic of data collection. This is an important aspect of the work of a data analyst or machine learning engineer. The models we can train and, most importantly, the correctness and usefulness of the entire solution depend on…

Today, I would like to introduce you to the process of planning Machine Learning projects, as practiced in my company, COGITA. If you are involved in ML projects—whether as an analyst or a Data Scientist—you are likely either participating in this process or closely utilizing…