The Evaluator
Your go-to blog for insights on AI observability and evaluation.
Showing 341–350 of 458 posts (page 35 of 46)
Five Unexpected Ways To Use ML Observability
This blog is written in partnership with Dan Schonfeld, Data Science Manager at eBay Taking a machine learning (ML) model from research to production and maintaining it once live is a difficult endeavor. Done well, it takes a village of people and purpose-built tools. ML observability is emerging as an indispensable part of that MLOps…
Eight Takeaways From Our Event With Women of AI
Recently, Arize AI hosted an event with the Canada chapter of Women of AI, a global nonprofit dedicated to increasing female representation and participation in the world of AI and machine learning (ML). Moderated by Arize’s Amber Roberts, speakers included Arize Founding Engineers Tsion Behailu and Manisha Sharma, Clearsum’s Lead AI Scientist Elhman Kheradmand, and…
What Is PR AUC?
In the latest edition of “The Slice,” a blog series from Arize that explains the essence of ML concepts in a digestible question-and-answer format, we dive into how to calculate PR AUC – and where it’s most useful. Want to see AUC in the Arize platform? Sign up for a free account. AUC, short for…
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Arize AI + OpenAI
This blog was written in collaboration with Amit Goren, Group Product Marketing Manager at Arize Using Arize and OpenAI together can help organizations better build unstructured models and monitor and troubleshoot those models in production, lowering costs and maximizing performance. NLP Models Are Tough To Get Out Into the Real World and Monitor Once Deployed…
The Death of Central ML Is Greatly Exaggerated
Don’t throw in the towel on centralized machine learning (ML) teams yet; here’s how to get it right Over the last decade, the industry has gone from celebrating the rise of the “central ML team” to questioning whether it should exist. I can’t help but feel like I’m watching Rome burn. It doesn’t have to…
Six Takeaways From Our Event On the Evolution of the Data Stack
Recently, Arize AI co-founder and Chief Product Officer Aparna Dhinakaran sat down with Lior Gavish, co-founder and CTO at data observability platform Monte Carlo, to discuss the evolving and intertwined worlds of data and machine learning (ML) infrastructure. Miss the event? Here are six top takeaways from the discussion. There Are Important Differences Between Data…
Shipping NLP Sentiment Classification Models With Confidence
This code-along walkthrough covers how to ingest embedding data and look at embedding drift. You can follow along the Colab version of this blog here. This blog was written in partnership with Nate Mar, Founding Engineer at Arize Increasingly, companies are turning to natural language processing (NLP) sentiment classification to better understand and improve customer…
Arize AI’s Next Era of Growth
Today, we announced a $38 million Series B led by TCV, with participation from existing investors Battery Ventures, Foundation Capital, and Swift Ventures. The investment is the industry’s largest-ever in a machine learning observability platform. Our Role In a Changing World When Aparna and I started Arize, we were on a mission to make AI…
Arize Receives Certifications Validating Health Information Security for HIPAA Compliance
Artificial intelligence is transforming modern healthcare. AI-focused healthcare startups raised over $12 billion last year, delivering everything from life-saving interventions in cancer care to reductions in claims fraud. Large insurers, providers, and pharmaceutical companies are also investing in machine learning to improve health outcomes and the overall patient experience. Altogether, Accenture predicts that deployed AI…
Gaining Insights from Private Data Using Federated Learning
Sid Roy is Manager of Machine Learning Engineering at Devron, a federated machine learning platform dedicated to unlocking the innovation and insight of data while preserving privacy. Before joining Devron, Sid was an AI Fellow at Insight Data Science and received his PhD in computational science with a focus on machine learning from the University…
