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Go to the profile of Alex Fleischer
Alex Fleischer in IBM Data Science in Practice

4 figures, 3 numbers, and some thoughts about IBM CPLEX and IBM Research Ponder This

IBM Ponder This Challenge is a monthly puzzle IBM Research started in May 1998. Puzzles are not only games but are also very useful for getting better at modeling business problems…

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Go to the profile of Abonia Sojasingarayar
Abonia Sojasingarayar in IBM Data Science in Practice

Faster R-CNN vs YOLO vs SSD — Object Detection Algorithms

Overview and comparative study of object…

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Go to the profile of Gene Azad
Gene Azad in IBM Data Science in Practice

Feature Platforms — A New Paradigm in Machine Learning Operations (MLOps)

Features are food for AI. If…

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Go to the profile of Fred Reiss
Fred Reiss in IBM Data Science in Practice

Easier Model Serving with zerocopy

Serving deep learning models is hard, but it doesn’t have to be that way.

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Go to the profile of Jatindra Suthar
Jatindra Suthar in IBM Data Science in Practice

Embedding watsonx Assistant in Flutter web app

Background

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Go to the profile of Alex Fleischer
Alex Fleischer in IBM Data Science in Practice

Optimization : simply do more with less, zoo, buses and kids

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Go to the profile of Doug Stauber
Doug Stauber in IBM Data Science in Practice

watsonx.governance now works with AI Anywhere

Last last year, we launched watsonx.governance on IBM Cloud with our…

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Go to the profile of Bhargav Mankad
Bhargav Mankad in IBM Data Science in Practice

Data Resilience Unleashed: A Comprehensive Guide to Multi-Site Active-Active Bucket Replication with MinIO —…

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Go to the profile of Yongli An
Yongli An in IBM Data Science in Practice

Using IBM Turbonomic for Monitoring Cloud Pak for Data — Part 2

Advanced report-based monitoring

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Go to the profile of Annalise Sumpon
Annalise Sumpon in IBM Data Science in Practice

How Maximo Visual Inspection empowers Image Segmentation

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About
IBM Data Science in Practice
IBM Data Science in Practice
IBM Data Science in Practice is written by data scientists for data scientists to gain hands-on and in-depth learning and to read about inspirational applications and conceptual understanding for challenging topics in the field. Discuss and network: community.ibm.com/datascience
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