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DTSTART:20240310T030000
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DTSTAMP:20240621T214607Z
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DTSTART;TZID=America/Los_Angeles:20240611T190000
DTEND;TZID=America/Los_Angeles:20240611T210000
DESCRIPTION:This event will be available live at SEMI\, as well as over Zoo
 m. Get to SEMI by 6:30pm to network – and enjoy great pizza and refreshm
 ents! All attendees MUST register using the Eventbrite form (see link).\n\
 nNetflix’s journey in enhancing its machine learning capabilities led to
  the creation of Axion\, a revolutionary fact store that is designed to op
 timize the quality and accessibility of data for machine learning algorith
 ms. This talk will delve into the evolution of Axion\, highlighting its cr
 itical role in advancing Netflix’s recommendation systems. It will also 
 explore the challenges faced in managing and utilizing large-scale data\, 
 and how Axion’s innovative architecture addresses these challenges.\n\nY
 ou will gain insights into the technical intricacies of Axion\, including 
 its four main components: (1) the fact-logging client\, (2) ETL (extract\,
  transform\, load)\, (3) the query client\, and (4) the data quality infra
 structure. You will learn about the unique approaches employed by Axion fo
 r data logging\, transformation\, and querying that ensure efficient and r
 eliable data access for its sophisticated machine learning architecture.\n
 \nSpeaker(s): Tejas Chopra\, \n\n567 Yosemite Dr\, Milpitas\, California\,
  United States\, 95035\, Virtual: https://events.vtools.ieee.org/m/414925
LOCATION:567 Yosemite Dr\, Milpitas\, California\, United States\, 95035\, 
 Virtual: https://events.vtools.ieee.org/m/414925
ORGANIZER:
SEQUENCE:15
SUMMARY:Axion: Machine Learning Storage at Netflix
URL;VALUE=URI:https://events.vtools.ieee.org/m/414925
X-ALT-DESC:Description: <br /><p><strong>This event will be available live 
 at SEMI\, as well as over Zoom. Get to SEMI by 6:30pm to network &ndash\; 
 and enjoy great pizza and refreshments! All attendees <u>MUST</u> register
  using the Eventbrite form (see link).</strong></p>\n<p>Netflix&rsquo\;s j
 ourney in enhancing its machine learning capabilities led to the creation 
 of Axion\, a revolutionary fact store that is designed to optimize the qua
 lity and accessibility of data for machine learning algorithms. This talk 
 will delve into the evolution of Axion\, highlighting its critical role in
  advancing Netflix&rsquo\;s recommendation systems. It will also explore t
 he challenges faced in managing and utilizing large-scale data\, and how A
 xion&rsquo\;s innovative architecture addresses these challenges.</p>\n<p>
 You will gain insights into the technical intricacies of Axion\, including
  its four main components: (1) the fact-logging client\, (2) ETL (extract\
 , transform\, load)\, (3) the query client\, and (4) the data quality infr
 astructure. You will learn about the unique approaches employed by Axion f
 or data logging\, transformation\, and querying that ensure efficient and 
 reliable data access for its sophisticated machine learning architecture.<
 /p>
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