Introducing Comet MPM: Model Production Monitoring
For many of us, it’s already a struggle to take a seemingly successful ML model live, but deployment is only…
Introducing Comet Interactive Reports and ML Templates
Workflows and processes are a critical need for every machine learning and AI project. When done well, they can enable…
Introducing CodeCarbon, an Open Source Tool to Track the CO2 Emissions of Research
Deep Learning’s Emissions Problem In the summer of 2019, a group of researchers led by Emma Strubell at the University…
Using Custom Visualizations to Debug Object Detection Models
Check out Comet's Custom Panel to debug Object Detection Models
Comet ❤️ Hugging Face
Get started with auto-logging model metrics and parameters to Comet from the Hugging Face transformers library.
Industry Q&A: Tracking Metrics for In-production ML Models
Comet recently hosted the online panel, “How do top AI researchers from Google, Stanford and Hugging Face approach new ML problems?” This…
Industry Q&A: Where Most ML Projects Fail
Although every machine learning project is different, there are common pitfalls and challenges that machine learning teams face when building and training models, and then taking them into production. Many of these challenges can be addressed when taken into consideration upfront, such as understanding the end goal, as well as the limitations that will be faced in your production environment.
