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4 Reasons Why Declarative ML Makes Sense for Engineers
Machine learning is starting to go mainstream, graduating out of the research lab and making its way into products. In fact, every engineering team we’ve worked on has had an item on their roadmap that went something like “Improve with machine learning”. But “doing... Read more
Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI
Editor’s note: Jonas Mueller is a speaker for ODSC East this May 9th-11th. Be sure to check out his session, “Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI,” there! Anybody who has worked on a real-world ML project knows how messy data can... Read more
Leveraging Time-Series Segmentation and Machine Learning for Better Forecasting Accuracy
Several papers discussed the importance of segmenting time series into groups and modeling each group separately to enhance forecasting accuracy overall. But what does this look like in practice? At the end of the day, why not use an AutoML package (Automated Machine Learning) or an... Read more
Create Audience Segments Using K-Means Clustering in Python
Editor’s note: Ali Rossi is a speaker for ODSC East 2023 this May 9th-11th. Be sure to check out her talk, “Uncovering Behavioral Segments by Applying Unsupervised Learning to Location Data,” there! Segmentation is a crucial aspect of modern marketing, allowing companies to divide their audience... Read more
State of Machine Learning Survey Results Part Two
Last week, we posted the first article recapping our recent machine learning survey. There, we talked about some of the results, such as what programming languages machine learning practitioners use, what frameworks they use, and what areas of the field they’re interested in. In the second... Read more
State of Machine Learning Survey Results Part One
In an effort to learn more about our community, we recently shared a survey about machine learning topics, including what platforms you’re using, in what industries, and what problems you’re facing. In a series of articles, we’d like to share the results so you too can... Read more
Solve Your MLOps Problems with an Open Source Data Science Stack
Editor’s note: Dean Pleban is a speaker for ODSC East 2023. Be sure to check out his talk, “Solving MLOps from First Principles,” there! Data scientists have challenges and need tools to overcome them. It’s best to use open-source, best-of-breed, modular solutions. It’s also a good... Read more
Churn Prevention with Reinforcement Learning
Creating a churn propensity model is now pretty standard for data scientists. Today, churn is the most common data science problem in the world, because every company wants recurring revenue. But how do you go from a churn model to churn prevention? It is much harder... Read more
Distributed training with PyTorch and Azure ML
By Beatriz Stollnitz, Principal Cloud Advocate at Microsoft Suppose you have a very large PyTorch model, and you’ve already tried many common tricks to speed up training: you optimized your code, you moved training to the cloud and selected a fast GPU VM, you installed software packages that... Read more
Faster Training and Inference Using the Azure Container for PyTorch in Azure ML
By Beatriz Stollnitz, Principal Cloud Advocate at Microsoft If you’ve ever wished that you could speed up the training of a large PyTorch model, then this post is for you! The Azure ML team has recently released the public preview of a new curated environment that... Read more