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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…
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Introducing Comet Artifacts Lineage
Our latest product update eases tracking, reproducibility, and collaboration in ML experiment management When you are building and training ML…
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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.…
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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…
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Make Tracking Your Machine Learning Experiments Easy
This article, written by Kurtis Pykes, first appeared on Heartbeat. A large portion of your time as a machine learning practitioner will…
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7 Simple Steps to Standardizing the ML Experiment (Feb. 23)
Notes from the eight session of a brand new Office Hours series: Seven Simple Steps to Standardizing the Experiment with…
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7 Simple Steps to Standardizing the ML Experiment (Feb. 16)
Notes from the seventh session of a brand new Office Hours series: Seven Simple Steps to Standardizing the Experiment with…
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Putting Machine Learning Models Successfully into Production
Sharing insights from Convergence speakers in advance of the free-to-attend, one-day virtual machine learning conference
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7 Simple Steps to Standardizing the ML Experiment (Feb. 9)
Notes from the sixth session of a brand new Office Hours series: Seven Simple Steps to Standardizing the Experiment discussing…
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7 Simple Steps to Standardizing the ML Experiment (Feb. 2)
Notes from the fifth session of a brand new Office Hours series: Seven Simple Steps to Standardizing the Experiment discussing…
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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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