人工智能超级流:高效的机器学习

指导老师:Shingai Manjengwa，…
2022年2月|时长:3小时12分钟
视频:MP4 1920×1080 48 KHz |语言：英语+中英文字幕（机译）
大小解压后:2.77 GB

由英特尔赞助

机器学习已经显著增长，随之而来的是ML模型的足迹——这使得培训、部署和监控变得困难和昂贵。如果您能够让您的ML模型和系统更加高效，无论是在成本、计算、存储、延迟还是碳足迹方面，会怎么样？加入我们的超级讨论，专家们将深入研究使用更少资源和提供更高质量的技术。AI Superstream: Efficient Machine Learning


关于人工智能超级流系列:这是一个为期半天的在线活动系列，由三部分组成，汇集了一些人工智能领域最聪明的头脑的见解。您将更深入地了解可以帮助您的组织保持竞争力的最新工具和技术，并学习利用人工智能来推动真正的业务成果。

你将学到什么以及如何应用它
了解深度学习所需的硬件和软件资源
了解如何优化ML模型和工作负载
了解如何构建健壮且可扩展的机器学习系统
探索应对气候变化的人工智能效率
这个现场活动的录音是给你的，因为…
您是一名ML工程师或数据从业者，想要使用更高效的算法并提高ML模型的效率。
你是一名数据团队领导或CDO，想要主动降低ML系统和管道的成本和资源使用。
你是一名产品利益相关者，想要了解更多关于ML效率如何与业务目标相一致的信息。



Instructors: Shingai Manjengwa, …
February 2022 | Duration: 3h 12m
Video: MP4 1920×1080 48 KHz | English
Size: 2.6 GB

Sponsored by Intel

Machine learning has grown significantly, and with it the footprint of ML models—which can make training, deploying, and monitoring difficult and expensive. What if you could make your ML models and systems more efficient, whether in the form of cost, compute, storage, latency, or carbon footprint? Join us for this Superstream where experts dive into techniques for using fewer resources and delivering better quality.

About the AI Superstream Series: This three-part series of half-day online events is packed with insights from some of the brightest minds in AI. You’ll get a deeper understanding of the latest tools and technologies that can help keep your organization competitive and learn to leverage AI to drive real business results.

What you’ll learn and how you can apply it
Understand hardware and software resources required for deep learning
Learn how to optimize ML models and workloads
Discover how to build robust and scalable machine learning systems
Explore AI efficiencies that combat climate change
This recording of a live event is for you because…
You’re an ML engineer or data practitioner who wants to use more-efficient algorithms and improve ML model efficiency.
You’re a data team leader or CDO who wants to proactively reduce the cost and resource use of ML systems and pipelines.
You’re a product stakeholder who wants to learn more about how ML efficiencies align with business goals.

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