How to Practice Data-Centric AI and Have AI Improve its Own Dataset
Editor’s note: Jonas Mueller is a speaker for ODSC West this October 30th to November 2nd. Be sure to check out his talk, “How to Practice Data-Centric AI and Have AI Improve its Own Dataset,” there! Machine learning models are only as good as the data... Read more
How Machine Learning Can Be Used to Cut Energy Bills
Utility companies are turning to machine learning to lower customers’ energy bills — and their own. They can offer better prices for consumers when overhead and operational costs are lower, creating a win-win situation for everyone involved. Here’s how machine learning and AI are making power... Read more
Machine Learning Engineering in the Real World
The majority of us who work in machine learning, analytics, and related disciplines do so for organizations with a variety of different structures and motives. These could be for for-profit corporations, not-for-profits, charities, or public sector organizations like the Government or Universities. In pretty much all... Read more
MLOps: Monitoring and Managing Drift
Editor’s note: Oliver Zeigermann is a speaker for ODSC West 2023 this Fall. Be sure to check out his talk, “MLOps: Monitoring and Managing Drift,” there! The trouble with machine learning starts after you put your model into production.  Typically, you want to bring something into... Read more
A Primer to Scaling Pandas
Editor’s note: Doris Lee is a speaker for ODSC West this Oct 30 to Nov 2. Be sure to check out her talk, “Scaling your Data Science Workflows by Changing a Single Line of Code,” there! pandas is one of the most popular data science libraries... Read more
Harnessing Machine Learning on Big Data with PySpark on AWS
Editor’s note: Suman Debnath is a speaker for ODSC APAC this August 22-23. Be sure to check out his talk, “Build Classification and Regression Models with Spark on AWS,” there! In the unceasingly dynamic arena of data science, discerning and applying the right instruments can significantly... Read more
Demystifying Machine Learning: Popular ML Libraries and Tools
As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). It’s a fascinating field that can seem daunting at first, but I assure you, with the right mindset and resources, anyone can master it. In this comprehensive guide,... Read more
Decision Trees From Scratch With Python
We already know a single decision tree can work surprisingly well. The idea of constructing a forest from individual trees seems like the natural next step. Today you’ll learn how the Random Forest classifier works and implement it from scratch in Python. This is the sixth of many... Read more
Area Under the Curve and Beyond with Integrated Discrimination Improvement and Net Reclassification
TLDR AUC is a good starting metric when comparing the performance of two models but it does not always tell the whole story NRI looks at the new models ability to correctly reclassify cancers and benigns and should be used alongside AUC IDI quantifies improvement of the slopes of... Read more
7 Pitfalls to Avoid While Using Model-Agnostic Interpretation Techniques
Interpretable machine learning techniques are becoming more popular among the data science community as more and more complex machine learning algorithms are adopted which are not easily interpretable. Model-Agnostic Interpretation techniques do not care about the underlying models, but they have the capability to interpret the... Read more