-
Fraud Detection, Imbalanced Classification, and Managing Your Machine Learning Experiments Using Comet
An end-to-end guide for building and managing an ML-powered fraud detection system A brief history of fraud The earliest recorded…
-
Fixing Object Detection Models with Better Data
Introduction Object detection tasks can be particularly tedious to debug. If you’ve worked with large object detection datasets in the…
-
Comet + Metaflow: The MLOps tech stack that truly scales with your engineering team
Machine learning is experimental in nature. It’s more like research in a lab than it is like building traditional software.…
-
Powering Anomaly Detection for Industry 4.0
What is Industry 4.0? You’ve probably heard the buzz: Industry 4.0 is revolutionizing the way companies manufacture, develop and distribute their…
-
Introducing Comet’s New Image Panel
The Need for Visualization For computer vision tasks such as segmentation, reconstruction, or object detection, it is imperative to visualize…
-
Build Production Ready Computer Vision Models with Comet and YOLOv5
To jump directly into resources about how to use Comet and Ultralytics YOLOv5, check out: Start training and logging Ultralytics…
-
RecList: The better way to evaluate recommender systems
How the team behind RecList is moving ML forward When it comes to evaluating ML models, there’s debate about which…
-
Introducing Comet Artifacts Lineage
Our latest product update eases tracking, reproducibility, and collaboration in ML experiment management When you are building and training ML…
-
3 Tips for Evaluating ML Platforms and Tools
Artificial intelligence (AI) is encountering yet another hurdle to delivering value, in the form of friction among and between teams.…
-
How to Compare Two or More Experiments in Comet
This article, written by Angelica Lo Duca, first appeared on Heartbeat. For two months now I have been studying Comet, a platform for…