keystone-ml.org valuation and analysis
Robots.txt Information
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| GoogleBot | / | ✔ |
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| Title | KeystoneML |
| Description | News Fork me on GitHub Toggle navigation KeystoneML Quick Start Programming Guide Running on EC2 Example Pipelines Benchmarks API Docs KeystoneML is a software |
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Server Information
| WebSite | |
| Host IP | 192.30.252.154 |
| Location | United States |
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keystone-ml.org Valuation
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US$494,961
Last updated: 2023-05-13 19:45:09
keystone-ml.org has Semrush global rank of 21,384,147. keystone-ml.org has an estimated worth of US$ 494,961, based on its estimated Ads revenue. keystone-ml.org receives approximately 57,111 unique visitors each day. Its web server is located in United States, with IP address 192.30.252.154. According to SiteAdvisor, keystone-ml.org is safe to visit. |
Traffic & Worth Estimates
| Purchase/Sale Value | US$494,961 |
| Daily Ads Revenue | US$457 |
| Monthly Ads Revenue | US$13,707 |
| Yearly Ads Revenue | US$164,480 |
| Daily Unique Visitors | 3,808 |
| Note: All traffic and earnings values are estimates. | |
DNS Records
| Host | Type | TTL | Data |
| keystone-ml.org. | A | 3599 | IP: 192.30.252.154 |
| keystone-ml.org. | A | 3599 | IP: 192.30.252.153 |
| keystone-ml.org. | NS | 3600 | NS Record: ns19.domaincontrol.com. |
| keystone-ml.org. | NS | 3600 | NS Record: ns20.domaincontrol.com. |
| keystone-ml.org. | MX | 3600 | MX Record: 0 smtp.secureserver.net. |
| keystone-ml.org. | MX | 3600 | MX Record: 10 mailstore1.secureserver.net. |
HtmlToTextCheckTime:2023-05-13 19:45:09
| Fork me on GitHub Toggle navigation KeystoneML Quick Start Programming Guide Running on EC2 Example Pipelines Benchmarks API Docs KeystoneML is a software framework, written in Scala , from the UC Berkeley AMPLab designed to simplify the construction of large scale , end-to-end , machine learning pipelines with Apache Spark . We contributed to the design of spark.ml during the development of KeystoneML, so if you’re familiar with spark.ml then you’ll recognize some shared concepts, but there are a few important differences, particularly around type safety and chaining, which lead to pipelines that are easier to construct and more robust. KeystoneML also presents a richer set of operators than those present in spark.ml including featurizers for images, text, and speech, and provides several example pipelines that reproduce state-of-the-art academic results on public data sets. News 2017-04-18 The KeystoneML paper will be presented at ICDE 2017. See you in San Diego! 2017-03-02 |
HTTP Headers
HTTP/1.1 200 OK Server: GitHub.com Date: Wed, 22 Dec 2021 00:37:50 GMT Content-Type: text/html; charset=utf-8 Content-Length: 16167 Vary: Accept-Encoding Last-Modified: Tue, 18 Apr 2017 16:17:06 GMT Vary: Accept-Encoding Access-Control-Allow-Origin: * ETag: "58f63c02-3f27" expires: Wed, 22 Dec 2021 00:47:50 GMT Cache-Control: max-age=600 Accept-Ranges: bytes x-proxy-cache: MISS X-GitHub-Request-Id: 8E84:0BA1:3F8A3A:5A2FA3:61C2735E |
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