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  <titleInfo>
    <title>Machine learning system design interview</title>
    <subTitle>an insider's guide</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Aminian, Ali</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
    <role>
      <roleTerm type="text">author.</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Xu, Alex</namePart>
    <role>
      <roleTerm type="text">author.</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">ii</placeTerm>
    </place>
    <place>
      <placeTerm type="text">India</placeTerm>
    </place>
    <publisher>Shroff publishers</publisher>
    <dateIssued>c2023 [third Indian reprint 2025]</dateIssued>
    <dateIssued encoding="marc">2025</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>v, 284 pages : illustrations, charts ; 26 cm</extent>
  </physicalDescription>
  <abstract>"Machine learning system design interviews are the most difficult to tackle of all technical interview questions. This book provides a reliable strategy and knowledge base for approaching a broad range of ML system design questions. It provides a step-by-step framework for tackling an ML system design question. It includes many real-world examples to illustrate the systematic approach, with detailed steps you can follow" --Amazon</abstract>
  <note type="statement of responsibility">by Ali Aminian, Alex Xu.</note>
  <note>Includes index. </note>
  <note>CSE</note>
  <subject>
    <topic>Machine learning</topic>
  </subject>
  <subject>
    <topic>Computer programming</topic>
  </subject>
  <subject>
    <topic>Employment interviewing</topic>
    <topic>Computer programming</topic>
  </subject>
  <subject>
    <topic>Computer programming</topic>
    <topic>Exercise</topic>
  </subject>
  <subject>
    <topic>Machine learning</topic>
    <topic>Exercise</topic>
  </subject>
  <classification authority="ddc">006.31</classification>
  <identifier type="isbn">9789355429469</identifier>
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    <recordCreationDate encoding="marc">251116</recordCreationDate>
    <recordChangeDate encoding="iso8601">20251116122926.0</recordChangeDate>
    <recordIdentifier source="BD-DhAAL">45029</recordIdentifier>
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