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How to 10x Throughput When Serving Hugging Face Models Without a GPU
By optimising how a model is served, we serve over 100 predictions per second with a simply Python API using…
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Stakion Joins Comet
Over the past 2 years, there has been a steady increase in investment towards Machine Learning initiatives. When we started…
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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…
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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…
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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…
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Using Custom Visualizations to Debug Object Detection Models
Check out Comet’s Custom Panel to debug Object Detection Models
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Comet ❤️ Hugging Face
Get started with auto-logging model metrics and parameters to Comet from the Hugging Face transformers library.
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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…
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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…
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Industry Q&A: Starting the ML Process
One of the hardest parts of machine learning is simply getting started. See how top AI researchers are address this…
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Comet Blog
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From Observability to Optimization: Announcing the Opik Agent Optimizer Public Beta
At Comet, we’re driven by a commitment to advance innovation in AI, particularly in the realm of LLM observability. Our…
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