MACHINE LEARNING SYSTEM INTERFACE
Some embodiments include an experiment management interface for a machine learning system. The experiment management interface can manage one or more workflow runs related to building or testing machine learning models. The experiment management interface can receive an experiment initialization com...
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| creator | Mehanna Hussein Mohamed Hassan Azzolini Alisson Gusatti Dunn Jeffrey Scott Vagata Pamela Shen Xie Xiaowen Farnham Rodrigo Bouchardet Sidorov Aleksandr Paton James Robert Bowers Stuart Michael |
| description | Some embodiments include an experiment management interface for a machine learning system. The experiment management interface can manage one or more workflow runs related to building or testing machine learning models. The experiment management interface can receive an experiment initialization command to create a new experiment associated with a new workflow. A workflow can be represented by an interdependency graph of one or more data processing operators. The experiment management interface enables definition of the new workflow from scratch or by cloning and modifying an existing workflow. The workflow can define a summary format for its inputs and outputs. In some embodiments, the experiment management interface can automatically generate a comparative visualization at the conclusion of running the new workflow based on an input schema or an output schema of the new workflow. |
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| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2016358101A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2016358101A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2016358101A13</originalsourceid><addsrcrecordid>eNrjZFD0dXT28PRzVfBxdQzy8_RzVwiODA5x9VXw9AtxDXJzdHblYWBNS8wpTuWF0twMym6uIc4euqkF-fGpxQWJyal5qSXxocFGBoZmxqYWhgaGjobGxKkCAJwqIw0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>MACHINE LEARNING SYSTEM INTERFACE</title><source>esp@cenet</source><creator>Mehanna Hussein Mohamed Hassan ; Azzolini Alisson Gusatti ; Dunn Jeffrey Scott ; Vagata Pamela Shen ; Xie Xiaowen ; Farnham Rodrigo Bouchardet ; Sidorov Aleksandr ; Paton James Robert ; Bowers Stuart Michael</creator><creatorcontrib>Mehanna Hussein Mohamed Hassan ; Azzolini Alisson Gusatti ; Dunn Jeffrey Scott ; Vagata Pamela Shen ; Xie Xiaowen ; Farnham Rodrigo Bouchardet ; Sidorov Aleksandr ; Paton James Robert ; Bowers Stuart Michael</creatorcontrib><description>Some embodiments include an experiment management interface for a machine learning system. The experiment management interface can manage one or more workflow runs related to building or testing machine learning models. The experiment management interface can receive an experiment initialization command to create a new experiment associated with a new workflow. A workflow can be represented by an interdependency graph of one or more data processing operators. The experiment management interface enables definition of the new workflow from scratch or by cloning and modifying an existing workflow. The workflow can define a summary format for its inputs and outputs. In some embodiments, the experiment management interface can automatically generate a comparative visualization at the conclusion of running the new workflow based on an input schema or an output schema of the new workflow.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2016</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20161208&DB=EPODOC&CC=US&NR=2016358101A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20161208&DB=EPODOC&CC=US&NR=2016358101A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Mehanna Hussein Mohamed Hassan</creatorcontrib><creatorcontrib>Azzolini Alisson Gusatti</creatorcontrib><creatorcontrib>Dunn Jeffrey Scott</creatorcontrib><creatorcontrib>Vagata Pamela Shen</creatorcontrib><creatorcontrib>Xie Xiaowen</creatorcontrib><creatorcontrib>Farnham Rodrigo Bouchardet</creatorcontrib><creatorcontrib>Sidorov Aleksandr</creatorcontrib><creatorcontrib>Paton James Robert</creatorcontrib><creatorcontrib>Bowers Stuart Michael</creatorcontrib><title>MACHINE LEARNING SYSTEM INTERFACE</title><description>Some embodiments include an experiment management interface for a machine learning system. The experiment management interface can manage one or more workflow runs related to building or testing machine learning models. The experiment management interface can receive an experiment initialization command to create a new experiment associated with a new workflow. A workflow can be represented by an interdependency graph of one or more data processing operators. The experiment management interface enables definition of the new workflow from scratch or by cloning and modifying an existing workflow. The workflow can define a summary format for its inputs and outputs. In some embodiments, the experiment management interface can automatically generate a comparative visualization at the conclusion of running the new workflow based on an input schema or an output schema of the new workflow.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2016</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZFD0dXT28PRzVfBxdQzy8_RzVwiODA5x9VXw9AtxDXJzdHblYWBNS8wpTuWF0twMym6uIc4euqkF-fGpxQWJyal5qSXxocFGBoZmxqYWhgaGjobGxKkCAJwqIw0</recordid><startdate>20161208</startdate><enddate>20161208</enddate><creator>Mehanna Hussein Mohamed Hassan</creator><creator>Azzolini Alisson Gusatti</creator><creator>Dunn Jeffrey Scott</creator><creator>Vagata Pamela Shen</creator><creator>Xie Xiaowen</creator><creator>Farnham Rodrigo Bouchardet</creator><creator>Sidorov Aleksandr</creator><creator>Paton James Robert</creator><creator>Bowers Stuart Michael</creator><scope>EVB</scope></search><sort><creationdate>20161208</creationdate><title>MACHINE LEARNING SYSTEM INTERFACE</title><author>Mehanna Hussein Mohamed Hassan ; Azzolini Alisson Gusatti ; Dunn Jeffrey Scott ; Vagata Pamela Shen ; Xie Xiaowen ; Farnham Rodrigo Bouchardet ; Sidorov Aleksandr ; Paton James Robert ; Bowers Stuart Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2016358101A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2016</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Mehanna Hussein Mohamed Hassan</creatorcontrib><creatorcontrib>Azzolini Alisson Gusatti</creatorcontrib><creatorcontrib>Dunn Jeffrey Scott</creatorcontrib><creatorcontrib>Vagata Pamela Shen</creatorcontrib><creatorcontrib>Xie Xiaowen</creatorcontrib><creatorcontrib>Farnham Rodrigo Bouchardet</creatorcontrib><creatorcontrib>Sidorov Aleksandr</creatorcontrib><creatorcontrib>Paton James Robert</creatorcontrib><creatorcontrib>Bowers Stuart Michael</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mehanna Hussein Mohamed Hassan</au><au>Azzolini Alisson Gusatti</au><au>Dunn Jeffrey Scott</au><au>Vagata Pamela Shen</au><au>Xie Xiaowen</au><au>Farnham Rodrigo Bouchardet</au><au>Sidorov Aleksandr</au><au>Paton James Robert</au><au>Bowers Stuart Michael</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>MACHINE LEARNING SYSTEM INTERFACE</title><date>2016-12-08</date><risdate>2016</risdate><abstract>Some embodiments include an experiment management interface for a machine learning system. The experiment management interface can manage one or more workflow runs related to building or testing machine learning models. The experiment management interface can receive an experiment initialization command to create a new experiment associated with a new workflow. A workflow can be represented by an interdependency graph of one or more data processing operators. The experiment management interface enables definition of the new workflow from scratch or by cloning and modifying an existing workflow. The workflow can define a summary format for its inputs and outputs. In some embodiments, the experiment management interface can automatically generate a comparative visualization at the conclusion of running the new workflow based on an input schema or an output schema of the new workflow.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | MACHINE LEARNING SYSTEM INTERFACE |
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