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Integrated Deployment: Continuous Deployment

Click to learn more about author Paolo Tamagnini. In this second article of our integrated deployment blog series – where we focus on solving the challenges around productionizing Data Science – we look at the model part of the process. In the previous article we covered a simple integrated deployment use case. We first looked at an existing […]

Ensemble Models: Bagging and Boosting

Click to learn more about author Rosaria Silipo. Ensemble models combine multiple learning algorithms to improve the predictive performance of each algorithm alone. There are two main strategies to ensemble models — bagging and boosting — and many examples of predefined ensemble algorithms. Bootstrap aggregation, or bagging, is an ensemble meta-learning technique that trains many […]

From a Single Decision Tree to a Random Forest

Click to learn more about author Rosaria Silipo. The co-author of this column was Kathrin Melcher. Decision trees are a set of very popular supervised classification algorithms. They are very popular for a few reasons: They perform quite well on classification problems, the decisional path is relatively easy to interpret, and the algorithm to build […]

Data Scientists and Machine Learning Algorithms for the Data-Driven World

Artificial Intelligence (AI) and Machine Learning are projected to become mainstream technologies in the coming years, and are clearly already having a significant impact across many industries. How exactly is this happening? How are Data Scientists using their skills to develop better Machine Learning algorithms? Where are these innovative technologies going in the future? With […]