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Financial Market Challenges and ML-Supported Asset Allocation
Editor’s note: Peter Schwendner, PhD is a speaker for ODSC Europe this June. Be sure to check out his talk, “ML Applications in Asset Allocation and Portfolio Management,” there! The year 2022 presented two significant turnarounds for tech: the first one is the immediate public visibility... Read more
Production Machine Learning for Mission-Critical Applications
Editor’s note: Robert Crowe is a speaker for ODSC Europe this June. Be sure to check out his talk, “Production ML for Mission-Critical Applications,” there! Deploying advanced machine learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems. This is especially... Read more
sktime – Python Toolbox for Machine Learning with Time Series
Editor’s note: Franz Kiraly is a speaker for ODSC Europe this June. Be sure to check out his talk, “sktime – Python Toolbox for Machine Learning with Time Series,” there! Welcome to sktime, the open community and Python framework for all things time series. Here’s what... Read more
Architecting the Edge for AI and ML
Believe it or not, the Raspberry Pi came out 11 years ago. In that time, single board computers (SBCs) have gotten unbelievably powerful. During this same decade every major telecom provider started rolling out 5G services. Oh, and by the way, AlexNet, the neural network that completely... Read more
Is Machine Learning Necessary to Solve Problems in Biology?
Editor’s note: Joshy George is a speaker for ODSC East this May 9th-11th. Be sure to check out his talk, “Is Machine Learning Necessary to Solve Problems in Biology,” there! The French mathematician Pierre-Simon Laplace suggested that we can accurately predict the universe’s future if we... Read more
Powering Millions of Real-Time Decisions with LyftLearn Serving
Editor’s note: Hakan Baba and Mihir Mathur are speakers for ODSC East 2023 this May. Be sure to check out their talk, “Powering Millions of Real-time Decisions with Distributed Model Serving,” there! Hundreds of millions of real-time decisions are made each day at Lyft by online... Read more
Using Azure ML to Train a Serengeti Data Model for Animal Identification
Article on Azure ML by Bethany Jepchumba and Josh Ndemenge of Microsoft In this article, I will cover how you can train a model using Notebooks in Azure Machine Learning Studio. To get the data, you will need to follow the instructions in the article: Create... Read more
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