Machine Learning Project Steps

Sticky Post

This is our first post about Advanced Analytics. Here, you will find an Introduction about Machine Learning and all the steps you must implement to perform successful and sustainable projects. During the next pills, you will find our experience through… Continue Reading →

Apps in Containers

Background Due to the progress in fields like data storage, networking or computational capability, computing has experimented a huge advancement in the last decades. As Moore said 50 years ago, the number of transistors in a microprocessor will double every… Continue Reading →

Data Significance

Feature Engineering The definition of Feature Engineering is applying domain knowledge of the data to create new features that allow Machine Learning algorithms to work better, or to work at all. The action of creating new features is an iterative… Continue Reading →

Dealing with data

Working deeply on achieving the features that best describe your problem always beats model tuning. In this pill, we will explain the different phases you may face when dealing with data and some recommendations. Generally speaking, it is best to… Continue Reading →

Profiles in ML projects

The success in a machine learning project heavily depends on the team involved. Through this second pill, we would like to illustrate, from our experience, the best combination of profiles to drive success. Data Engineer This profile is generally in… Continue Reading →

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