The Evaluator
Your go-to blog for insights on AI observability and evaluation.
Showing 331–340 of 458 posts (page 34 of 46)
Hugging Face + Arize: Partnership and Code Example
This article was written in collaboration with Amit Goren, Senior Product Marketing Manager at Arize We’re excited to share that Arize AI and Hugging Face are partnering to help organizations train unstructured models and monitor and troubleshoot those models in production, lowering costs and maximizing performance. Want to dive right in? Sign up for your…
Generative AI Is Working Its Way Into Your Business – Are You Ready?
Generative AI is capturing the public imagination in a way few technical breakthroughs have since Thomas Edison delighted audiences with the earliest motion pictures over a century ago. Armed with little more than a keyboard, today anyone can conjure up a dazzling array of media – from artistic imagery to code to nuanced articles on…
Calculate Real-Time AI ROI With Custom Metrics
We are excited to announce support for custom metrics across the Arize platform. This new feature enables you to tailor any metric to your ML monitoring needs. Learn how to use custom metrics to automate AI ROI calculations, map impact across all model inference data, and reduce overall costs. There is a wide breadth of…
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Supercharge Production ML With BentoML and Arize AI
BentoML and Arize AI have partnered to streamline the MLOps toolchain and help teams build, ship, and maintain business-critical models. Leverage Bento’s ML service platform to easily turn ML models into production-worthy prediction services. Once your model is in production, use Arize’s ML observability platform to attain the necessary visibility to keep your model in…
Why You Need To Monitor Recommender Systems
An overview of recommendation systems, including how teams should monitor and troubleshoot models in production Learn more about how Arize helps clients observe recommendation systems, sign up for a free account, dive into an interactive demo or request a trial of Arize. Millions of machine learning algorithms over the last several years have been funded,…
Sparking ML-Powered Innovation In the Telecommunications Industry
Habib Baluwala is Domain Chapter Lead for Commercial Data at Spark New Zealand, the country’s largest telecommunications and digital services company. There, he works with Chapter Lead for AI and fellow data scientist Aadil Dowlut. In this wide-ranging interview, the two colleagues talk about Spark New Zealand’s machine learning use cases, how they monitor and…
Introducing Xander Song, Arize’s New Developer Advocate
Xander Song is Arize AI’s new Developer Advocate. Before joining Arize, Song worked as a machine learning engineer at Test.ai. He is based in Oakland, California. Can you introduce yourself and share your career background? I have an interdisciplinary background and took a winding path to my current role at Arize. As a philosophy and…
Shipping Your Image Classification Model With Confidence
This blog was written in partnership with Gurmehar Kaur Somal, Application Engineer at Arize AI This code-along blog walks through developing an image classification model, preparing and ingesting embedding data, and analyzing embedding drift You can follow along the Colab version of this blog here. Computer vision has entered the mainstream. From cancer detection to…
The Importance of Real-Time Data Pipelines: An Interview with mParticle’s Shafiq Shivji
Shafiq Shivji is Group Product Marketing Manager at mParticle, where he leads developer experience and data integrity domains. He brings over a decade of experience in product and sales engineer roles across various industries including cybersecurity, education technology, healthcare and telecommunications. Tell us about your career journey. How did you get into your current position…
How to Monitor Ranking Models
In a world filled with infinite options and finite time and resources, how do you ensure you’re providing your customers and users with the most relevant information at all times? Many companies deploy ranking models to solve this problem, aiding business-critical results across industries. Ranking models are highly visible and provide a mutually beneficial outcome…
