Data Augmentation – Cherry-picking data in a useful and positive way
Data Pre-Processing techniques exist to ensure data quality and they can be grouped in different ways. There is no consensus on the best way to create these groups, although examples of such groups usually include data cleaning, feature transformation, feature...
Feature Learning – Helping Machine Learning algorithms derive value from data
Data Pre-Processing techniques exist to ensure data quality and they can be grouped in different ways. There is no consensus on the best way to create these groups, although examples of such groups usually include data cleaning, feature transformation, feature...
Unsupervised Learning – Wandering through the swamp of unlabelled data
A task-oriented approach to Machine Learning allows us to divide it into three categories: supervised learning (data are labelled according to some criteria), unsupervised learning (no labels exist for the data), and reinforcement learning (no data is directly available, only...
Feature Transformation – Making data features Machine Learning friendly
Data Pre-Processing techniques exist to ensure data quality and they can be grouped in different ways. There is no consensus on the best way to create these groups, although examples of such groups usually include data cleaning, feature transformation, feature...
Supervised Learning – Teaching the machine how to do tricks
A task-oriented approach to Machine Learning allows us to divide it into three categories: supervised learning (data are labelled according to some criteria), unsupervised learning (no labels exist for the data), and reinforcement learning (no data is directly available, only...
Transitioning from raw to high quality data by means of Data Pre-Processing
Some of the most important pillars in Data Science and Machine Learning supporting the ability to extract meaningful insights and knowledge from the data are: 1) how representative are those data of the phenomenon being measured and 2) the quality...
Data Mesh Services – Part I
Data Architecture is hard. First, second and third generation Data Architectures had proven how difficult is to unpack the underlying limitations of each generation. The Data Architecture Generations Generation #1 – Proprietary Datawarehouses and BI systems with high budget spending…
MLOps – Measure your maturity
MLOps is not a thing from the future. Trust us. Just like DevOps, MLOps it’s not only about technology, it’s about processes, their operationalization and essentially the people involved. In this blog post, we will be sharing a maturity model…
