
Luis Ceze
Articles
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Sep 25, 2024 |
cacm.acm.org | Luis Ceze |Ted Selker |WALID SABA |Alex Williams
There is no denying that deep learning, especially with generative models AI, has deeply transformed how we use computers. It has also quickly become where a very large fraction of the world’s computing resources is now focused. Most deep-learning systems implementations involve expressing the machine learning (ML) model in some higher-level framework (for example, Caffe in the early days, then TensorFlow, and now PyTorch).
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Aug 13, 2024 |
dzone.com | Luis Ceze
The mantra in the world of generative AI models today is "the latest is the greatest," but that’s far from the case. We are lured (and spoiled) by choice with new models popping up left and right. Good problem to have? Maybe, but it comes with a big opportunity: model fatigue. There’s an issue that has the potential to wreak havoc on your ML initiatives: prompt lock-in.
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Jul 21, 2024 |
datanami.com | Alex Woodie |Luis Ceze
Doesn’t it seem like there’s a new machine learning model introduced every week? That’s probably because there is. From Sora to LLaMA-3 and Claude 2, models today come in all shapes and sizes—open source, off the shelf—with varying performance rates, cost implications, and rate limits. Each provider makes big promises to revolutionize the industry, and your business in particular. But the reality is that model fatigue is setting in.
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May 2, 2024 |
octo.ai | Luis Ceze |Jason Knight |Tianqi Chen
GenAI apps are changing the world. We’ve seen through our customers how new apps are simplifying how users get new information, interact with services, and unlock creative potential. Even as organizations explore the newest models and latest capabilities, they are acutely aware of the resource impact of the success of these applications. Builders want efficiency, customizability and reliability, as they build for this growing demand.
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Mar 26, 2024 |
thenewstack.io | Eric Futoran |Charles Xie |Luis Ceze |Eric co-founded Scopely
When we think of “observability,” most of us define it as “metrics, logs and traces.” It’s not. What we really mean is to enrich and observe those pre-defined sets of data and then layer analysis on top of them to better measure and understand business Key Performance Indicators (KPIs) — proactively. In other words, observability isn’t just about collecting and sorting data sets. It’s not just about alerts, correlations and uptime.
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