Metaflow

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工具介绍:
用于现实生活中的机器学习、人工智能和数据科学的框架
收录时间:
2024年4月5日
月流量:
27.6K
社交媒体&邮箱:
Website
AI开发工具
Metaflow 工具信息

什么是Metaflow?

使用Metaflow构建和管理现实生活中的机器学习、人工智能和数据科学项目。

如何使用 Metaflow?

使用笔记本进行探索,使用Metaflow进行开发,本地进行测试和调试。将规模扩展到云端。通过单击一次将实验部署到生产环境,而不需要更改任何代码。

Metaflow 的核心功能

建模

部署

版本控制

编排

计算

数据

Metaflow 的使用案例

#1

开发安全可靠的机器学习产品

#2

通过MLOps加速实验

#3

提高数据科学流程以加快创新速度

来自 Metaflow 的常见问题

什么是Metaflow?

Metaflow适用于谁?

如何部署Metaflow?

Metaflow 评价 (0)

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Metaflow数据分析

Metaflow 网站流量分析

最新流量情况

月访问量
27.6K
平均访问时长
00:00:33
每次访问页数
2.26
跳出率
45.24%
Dec 2023 - Jan 2025 所有流量

地理位置

Top 5 国家/地区

United States
33.03%
Colombia
12.71%
Canada
6.95%
Russia
6.80%
India
6.26%
Dec 2023 - Jan 2025 仅桌面设备

流量来源

自然搜索
46.57%
直接访问
40.86%
外链引荐
7.68%
社交媒体
4.18%
展示广告
0.61%
邮件
0.09%
Dec 2023 - Jan 2025 仅限全球桌面设备

热门关键词

关键词
交通
每次点击费用
metaflow
32.1K
$ 0.69
netflix metaflow
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metaflow step name resume
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metaflow python
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metaflow netflix
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社交媒体聆听

All
YouTube
Tiktok
54:06

#225 The Full Stack Data Scientist | Savin Goyal, Co-Founder & CTO at Outerbounds

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with this role often including software engineering tasks. In particular, one of the key functions of a full stack data scientist is to take machine learning models and get them into production inside software. So, what separates projects from production? Savin Goyal is the Co-Founder & CTO at Outerbounds. In addition to his work at Outerbounds, Savin is the creator of the open source machine learning management platform Metaflow. Previously Savin has worked as a Software Engineer at Netflix and LinkedIn. In the episode, Richie and Savin explore the definition of production in data science, steps to move from internal projects to production, the lifecycle of a machine learning project, success stories in data science, challenges in quality control, Metaflow, scalability and robustness in production, AI and MLOps, advice for organizations and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: https://open.spotify.com/show/02yJXEJAJiQ0Vm2AO9Xj6X?si=d08431f59edc4ccd Links Mentioned in the Show: Outerbounds - https://outerbounds.com/ Metaflow - https://metaflow.org/ Connect with Savin on Linkedin - https://www.linkedin.com/in/savingoyal/ [Course] Developing Machine Learning Models for Production - https://www.datacamp.com/courses/developing-machine-learning-models-for-production Related Episode: Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author - https://www.datacamp.com/podcast/why-ml-projects-fail-and-how-to-ensure-success-with-eric-siegel-founder-of-machine-learning-week Rewatch sessions from RADAR: AI Edition - https://www.datacamp.com/radar-ai-2024 New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

DataCamp
2024年7月15日
542
0
57:06

Generative AI in Production: Best Practices and Lessons Learned

Meryem Arik: Meryem is a recovering physicist, co-founder, and CEO of TitanML.TitanML is an NLP development platform that focuses on the deployability of LLMs - allowing businesses to build smaller and cheaper language model deployments easily. - Greg Loughnane is a Generative AI and LLM educator, community builder, CEO, and Founder of AI Makerspace, where he’s building useful onramps for people looking to join the unautomatable workforce of the future. - Alessya Visnjic is the CEO of WhyLabs, the market leader in AI observability. AI teams at Square and Glassdoor use the WhyLabs platform to ensure AI models, from classification to LLMs, generate accurate, fair, and safe customer experiences. - Chris Alexiuk, aka The LLM Wizard and educator extraordinaire. Chris was previously an Instructor and curriculum developer at FourthBrain and is currently a founding ML Engineer at Ox and CTO at AI Makerspace. - Hannes Hapke is a principal machine learning engineer at Digits . He implemented deep learning systems from inception to production and has authored two books: building ml pipelines and NLP in action. - Ville Tuulos is the co-founder and CEO of Outerbounds, a human-centric platform for data, ML, and AI projects based on metaflow.org, an open-source project he started while working on ML Infra and Architecture at Netflix. Questions What advice would you give someone looking to build a new generative AI-powered product or feature? What steps would you advice someone to take to understand its feasibility for real-world applications? (Panelists to ask: Ville, Hannes, Chris, Greg) What are the most common challenges when translating a generative AI product/feature prototype into a production-ready product? (Panelists to ask: Ville, Chris, Greg, Alessya) Can you share a war story where translating a generative AI-powered prototype to production posed unexpected challenges and how you overcame them? (Panelists to ask: Hannes) Are specific tools, platforms, or infrastructure indispensable when moving from prototype to production, specifically for generative AI-powered products or features? (Panelists to ask: Ville, Chris, Alessya) How do you address scalability when implementing generative AI models in production, especially when the original research might have been conducted on smaller datasets? (Panelists to ask: Hannes) What measures do you take to ensure that a generative AI model, which works well in a controlled research environment, is robust and reliable when deployed in real-world scenarios? (Panelists to ask: Hannes, Alessya) With the rapid advancements in generative AI research, where do you see the future of its implementation in production heading? (Panelists to ask: Chris, Greg) What advice would you give aspiring practitioners looking to bridge the skill gap between building generative AI toy examples in a notebook to production implementation? (Panelists to ask: Ville, Greg)

Harpreet Sahota
2023年10月13日
162
0

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Metaflow: 用于现实生活中的机器学习、人工智能和数据科学的框架
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