CINXE.COM

<!DOCTYPE html> <html class='v2' dir='ltr' lang='en' xmlns='http://www.w3.org/1999/xhtml' xmlns:b='http://www.google.com/2005/gml/b' xmlns:data='http://www.google.com/2005/gml/data' xmlns:expr='http://www.google.com/2005/gml/expr'> <head> <link href='https://www.blogger.com/static/v1/widgets/3566091532-css_bundle_v2.css' rel='stylesheet' type='text/css'/> <meta content='text/html; charset=UTF-8' http-equiv='Content-Type'/> <meta content='blogger' name='generator'/> <link href='https://blog.tensorflow.org/favicon.ico' rel='icon' type='image/x-icon'/> <link href='https://blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html' rel='canonical'/> <link rel="alternate" type="application/atom+xml" title="The TensorFlow Blog - Atom" href="https://blog.tensorflow.org/feeds/posts/default" /> <link rel="alternate" type="application/rss+xml" title="The TensorFlow Blog - RSS" href="https://blog.tensorflow.org/feeds/posts/default?alt=rss" /> <link rel="service.post" type="application/atom+xml" title="The TensorFlow Blog - Atom" href="https://www.blogger.com/feeds/7864883956188652345/posts/default" /> <link rel="alternate" type="application/atom+xml" title="The TensorFlow Blog - Atom" href="https://blog.tensorflow.org/feeds/9124147844941336220/comments/default" />  <link href='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/s1600/social-Practices-of-ML-systems-engineering.png' rel='image_src'/> <meta content='MLSysBook.ai explores key ML systems engineering concepts and how TensorFlow tools support each stage of the machine learning life cycle.' name='description'/> <meta content='https://blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html' property='og:url'/> <meta content='MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering' property='og:title'/> <meta content='MLSysBook.ai explores key ML systems engineering concepts and how TensorFlow tools support each stage of the machine learning life cycle.' property='og:description'/> <meta content='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/w1200-h630-p-k-no-nu/social-Practices-of-ML-systems-engineering.png' property='og:image'/> <meta charset='UTF-8'/> <meta content='IE=edge' http-equiv='X-UA-Compatible'/> <meta content='width=device-width, initial-scale=1' name='viewport'/> <meta content='https://www.gstatic.com/tf_blog/images/image_blank.png' property='og:image'/> <meta content='https://www.gstatic.com/tf_blog/images/image_blank.png' property='twitter:image'/> <meta content='summary_large_image' name='twitter:card'/> <meta content='MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering' property='twitter:title'/> <title>MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering — The TensorFlow Blog</title> <style id='page-skin-1' type='text/css'></style>  <link href='https://fonts.googleapis.com/css?family=Google+Sans:400,500,700|Roboto:400,400italic,500,500italic,700,700italic|Roboto+Mono:400,500,700|Material+Icons' rel='stylesheet'/>   <link href="//prismjs.com/themes/prism.css" rel="stylesheet"> <script src="//prismjs.com/prism.js" type="text/javascript"></script> <script src='https://cdnjs.cloudflare.com/ajax/libs/prism/1.17.1/plugins/autoloader/prism-autoloader.min.js'></script>   <link href='https://cdn.jsdelivr.net/npm/zoom-vanilla.js/dist/zoom.css' rel='stylesheet'/> <script defer='defer' src='https://cdn.jsdelivr.net/npm/zoom-vanilla.js/dist/zoom-vanilla.min.js' type='text/javascript'></script>  <link href='https://www.gstatic.com/tf_blog/images/favicon.png' rel='shortcut icon' type='image/png'/> <script type='text/javascript'> //<![CDATA[ const qs = (string, el = document) => el.querySelector(string); const qsa = (string, el = document) => el.querySelectorAll(string); class App { constructor() { this.body = qs('body'); this.detailBody = qs('.tensorsite-detail__body'); this.overlay = qs('.header__overlay'); this.hamburger = qs('.header__hamburger'); this.sideMenu = qs('.header__side-menu'); this.detailBodies = qsa('.tensorsite-detail__body'); this.searchForms = qsa('.searchbox'); this.searchInputs = qsa('.search-input'); this.homeHref = qs('#home-href'); this.featuredCard = qs('.tensorsite-card.featured'); this.featuredPostHref = this.featuredCard && this.featuredCard .querySelector('.tensorsite-card__href') .getAttribute('href'); this.cards = qsa('.tensorsite-card'); this.images = qsa('img[border]'); this.cardDescriptions = qsa('.tensorsite-content__description'); this.hiddenDescription = qsa('.tensorsite-detail__description'); this.iconLinks = qs('.social-icons__links').children this.iconTooltips = qsa('[class^="icon-tooltip"]') this._toggleMobileMenu = this._toggleMobileMenu.bind(this); this._closeMenu = this._closeMenu.bind(this); this._onResize = this._onResize.bind(this); this._getScreen = this._getScreen.bind(this); this._searchGoogle = this._searchGoogle.bind(this); this._handleSearchKeypress = this._handleSearchKeypress.bind(this); this._removeDividerAboveImage(); this._setAllTagActive(); this._showFeaturedPost(); this._redirectWithMaxResults(); this._makeImagesZoomable(); this._removeCardLineBreaks(); this._getNextPost().then(()=>{ this._removeCardLineBreaks(); }) this.addEventListeners(); } addEventListeners() { window.addEventListener('resize', this._onResize); this.hamburger.addEventListener('click', this._toggleMobileMenu); this.searchForms.forEach(el => el.addEventListener('submit', this._searchGoogle)); this.searchInputs.forEach(el => el.addEventListener('keypress', this._handleSearchKeypress)); Array.from(this.iconLinks).forEach((icon, i) => { icon.addEventListener("mouseover", () => icon.querySelectorAll('[class^="icon-tooltip"]')[0].style.display = 'block'); icon.addEventListener("mouseout", () => icon.querySelectorAll('[class^="icon-tooltip"]')[0].style.display = 'none'); }) } _getNextPost() { return new Promise((resolve) => { const nextHref = qs('.tensorsite-detail__next-url'); if (this.detailBody && nextHref) { let request = new XMLHttpRequest(); request.open('GET', nextHref.getAttribute('href'), true); request.onload = function() { if (this.status >= 200 && this.status < 400) { // Success! Should be an HTML response // Save html in variable so you're able to query select const parser = new DOMParser(); const html = parser.parseFromString(this.response, "text/html"); const nextTitle = html.querySelector('.tensorsite-detail__title'); const nextDesc = html.querySelector('.tensorsite-detail__description'); const nextTags = html.querySelector('.tensorsite-detail__tags'); const nextHref = qs('.tensorsite-detail__next-url').getAttribute('href'); const nextImgUrl = html.querySelector('.tensorsite-detail__main-image'); const nextTitleEl = document.querySelector('.tensorsite-content__title.next'); const nextDescEl = document.querySelector('.tensorsite-content__description.next'); let nextTagsEl = document.querySelector('.tensorsite-content__subtitle.next'); const nextHrefEl = document.querySelector('.tensorsite-card__href.next'); const nextImgEl = document.querySelector('.tensorsite-content__image-wrapper'); const nextContainer = qs('.tensorsite-next'); const footer = qs('.tensorsite-footer'); if (nextTitleEl && nextTitle) { nextTitleEl.innerHTML = nextTitle.innerHTML; } if (nextDescEl && nextDesc) { nextDescEl.innerHTML = nextDesc.innerHTML; } if (nextTagsEl && nextTags) { nextTagsEl.innerHTML = nextTags.innerHTML; } if (nextHref && nextHrefEl) { nextHrefEl.setAttribute('href', nextHref); } if (nextImgEl && nextImgUrl) { // If Blogger can't find a firstImageUrl, it returns a // message informing us of that, so this checks // if the string is a URL if(!/http/.test(nextImgUrl.innerHTML)){ nextImgEl.classList.add('hidden'); } else { nextImgEl.querySelector('img').src = nextImgUrl.innerHTML; } } if (nextHref) { nextContainer.classList.add('active'); footer.classList.add('grey'); } resolve(); } else { // We reached our target server, but it returned an error console.error('Error: Could not get the next title'); } }; request.send(); } }) } get isMenuOpen() { return this.sideMenu.classList.contains('is-open'); } _handleSearchKeypress(e) { if (e.which == 13) { this._searchGoogle(); } } _searchGoogle(e) { e.preventDefault(); const {value} = e.target.querySelector('.search-input'); window.location.href = 'https://www.google.com/search?q=site%3A' + window.location.hostname + '%20' + value; } _toggleMobileMenu() { this.body.classList.toggle('no-scroll'); this.overlay.classList.toggle('show'); this.sideMenu.classList.toggle('is-open'); if (this.isMenuOpen) { this.overlay.addEventListener('click', this._closeMenu); } else { this.overlay.removeEventListener('click', this._closeMenu); } } _closeMenu(e) { if (this.isMenuOpen) { this._toggleMobileMenu(); } } _onResize() { if (this._getScreen().width > 839 && this.isMenuOpen) { this._closeMenu(); } } _getScreen() { return { scrollY: window.scrollY, width: window.innerWidth, height: window.innerHeight, } }; _removeDividerAboveImage() { if (this.detailBody && this.detailBody.firstElementChild && this.detailBody.firstElementChild.querySelector('img')) { const firstDivider = qs('.divider'); firstDivider.style.display = 'none'; } } _isCurrentPathAllPosts(){ const {pathname, search} = window.location; return pathname === '/' || (pathname === '/search' && !/label/.test(search)) } _setAllTagActive() { if(this._isCurrentPathAllPosts()){ const allTag = qs('.header__nav-item.all'); allTag.parentElement.classList.add('active'); } } // Shows featured post only if not on the home page _showFeaturedPost() { if (window.location.pathname === '/' && this.featuredCard) { // If any posts in the list have the same href, hide them. // Using a boolean in order to skip the first match // since that is the actual featured card. let skippedFirst = false; this.cards.forEach(card => { const link = card .querySelector('.tensorsite-card__href') .getAttribute('href'); if (link === this.featuredPostHref) { if (skippedFirst) { card.classList.add('hidden'); } skippedFirst = true; } }) } } _makeImagesZoomable(){ this.images.forEach(image => { image.setAttribute('data-action', 'zoom'); if(/a/i.test(image.parentNode.tagName)){ image.parentNode.replaceWith(image) } }) } // Adds max-results query param if URL contains a label filter but // doesn't contain max-results _redirectWithMaxResults(){ const {search} = window.location; const isLabelMatch = /(tensorflow|tfx|community)/gi.test(search) if(isLabelMatch && !/max-results/.test(search)){ window.location.href = window.location.href + '&max-results=20' } } _removeCardLineBreaks(){ const descriptions = this.cardDescriptions || [this.hiddenDescription]; if(descriptions){ descriptions.forEach(node=> { let stringArray = node.innerText.split(/(\r\n|\n|\r)/g); for(let i = stringArray.length; i > 0; i--){ if(/(\r\n|\n|\r)/.test(stringArray[i]) || stringArray[i] === ''){ let j = i - 1; while(j > 0 && (/(\r\n|\n|\r)/.test(stringArray[j]) || stringArray[j] === '')){ stringArray.splice(j, 1); j-- } } } return node.innerText = stringArray.join('') }) } } } window.addEventListener('DOMContentLoaded', (event) => { new App(); }); //]]> </script> <script type='text/javascript'> (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o), m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) })(window,document,'script','https://www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-142343919-1', 'auto', 'blogger'); ga('blogger.send', 'pageview'); </script> <link href='https://www.blogger.com/dyn-css/authorization.css?targetBlogID=7864883956188652345&zx=fec37b9b-5beb-4355-a145-f93f4e783e5d' media='none' onload='if(media!='all')media='all'' rel='stylesheet'/><noscript><link href='https://www.blogger.com/dyn-css/authorization.css?targetBlogID=7864883956188652345&zx=fec37b9b-5beb-4355-a145-f93f4e783e5d' rel='stylesheet'/></noscript> <meta name='google-adsense-platform-account' content='ca-host-pub-1556223355139109'/> <meta name='google-adsense-platform-domain' content='blogspot.com'/> </head> <body> <div class='header__overlay'></div> <div class='section' id='nav'><div class='widget HTML' data-version='1' id='HTML1'> <header class='header'> <div aria-hidden='true' data-href='https://blog.tensorflow.org/' hidden='true' id='home-href'></div> <div class='top-row'> <div class='top-row__left'> <button aria-label='Toggle menu' class='header__hamburger' type='button'> <i class='material-icons'>menu</i> </button> <a class='tensorsite-blog-logo' href='https://blog.tensorflow.org/'> <img alt='TensorFlow Blog Logo' class='tensorsite-blog-logo__image' src='https://www.gstatic.com/tf_blog/images/tfblog_logo.svg'/> </a> </div> <div class='top-row__right'> <div class='header__search-container'> <form action='' class='searchbox'> <input aria-label='Search box' class='search-input' name='q' onblur='if (this.value=="") {this.value="Search the Blog";}' onfocus='if (this.value=="Search the Blog") {this.value=""}' placeholder='Search the Blog' type='text' value=''/> <i class='material-icons'>search</i> <input style='visibility:hidden;position:absolute' type='submit'/> </form> </div> <a class='header__cta' href='https://www.tensorflow.org/'> <svg class='cta-icon' height='12' viewBox='0 0 18 18' width='12' xmlns='http://www.w3.org/2000/svg'> <g fill='none' fill-rule='evenodd' transform='translate(-3 -3)'> <rect height='24' width='24'></rect> <path d='M20.55,10.95 L13.05,3.45 C12.45,2.85 11.55,2.85 10.95,3.45 C10.35,4.05 10.35,4.95 10.95,5.55 L15.9,10.5 L4.5,10.5 C3.6,10.5 3,11.1 3,12 C3,12.9 3.6,13.5 4.5,13.5 L15.9,13.5 L10.95,18.45 C10.35,19.05 10.35,19.95 10.95,20.55 C10.95,20.55 10.95,20.55 10.95,20.55 C11.55,21.15 12.45,21.15 13.05,20.55 C13.05,20.55 13.05,20.55 13.05,20.55 L20.55,13.05 C21.15,12.45 21.15,11.55 20.55,10.95 C20.55,10.95 20.55,10.95 20.55,10.95 Z' fill='#000'></path> </g> </svg> Return to TensorFlow Home </a> </div> </div> <div class='nav-row'> <div class='nav-items'> <tab> <a class='header__nav-item all' dir='ltr' href='https://blog.tensorflow.org/'> All </a> </tab> <tab> <a class='header__nav-item' dir='ltr' href='https://blog.tensorflow.org/search?label=TensorFlow+Core&max-results=20'> TensorFlow Core </a> </tab> <tab> <a class='header__nav-item' dir='ltr' href='https://blog.tensorflow.org/search?label=TensorFlow.js&max-results=20'> TensorFlow.js </a> </tab> <tab> <a class='header__nav-item' dir='ltr' href='https://blog.tensorflow.org/search?label=TensorFlow+Lite&max-results=20'> TensorFlow Lite </a> </tab> <tab> <a class='header__nav-item' dir='ltr' href='https://blog.tensorflow.org/search?label=TFX&max-results=20'> TFX </a> </tab> <tab> <a class='header__nav-item' dir='ltr' href='https://blog.tensorflow.org/search?label=Community&max-results=20'> Community </a> </tab> </div> <section class='social-icons'> <div class='social-icons__container-header'> <div class='social-icons__links'> <a class='icon-link' href='https://discuss.tensorflow.org' rel='noopener noreferrer' target='_blank'> <img alt='TensorFlow Forum' src='https://www.gstatic.com/tf_blog/images/ic_forum_2.svg'/> <div class='icon-tooltip'>TensorFlow Forum</div> </a> <a class='icon-link' href='https://www.youtube.com/tensorflow' rel='noopener noreferrer' target='_blank'> <img alt='TensorFlow YouTube' src='https://www.gstatic.com/tf_blog/images/ic_youtube.svg'/> <div class='icon-tooltip'>TensorFlow YouTube</div> </a> <a class='icon-link' href='https://twitter.com/TensorFlow' rel='noopener noreferrer' target='_blank'> <img alt='TensorFlow Twitter' src='https://www.gstatic.com/tf_blog/images/ic_twitter.svg'/> <div class='icon-tooltip'>TensorFlow Twitter</div> </a> <a class='icon-link' href='https://github.com/tensorflow' rel='noopener noreferrer' target='_blank'> <img alt='TensorFlow GitHub' src='https://www.gstatic.com/tf_blog/images/ic_github.svg'/> <div class='icon-tooltip-github'>TensorFlow GitHub</div> </a> </div> </div> </section> </div> </header> <div class='header__side-menu'> <div class='header__side-menu__logo-container'> <a class='tensorsite-blog-logo' href='https://blog.tensorflow.org/'> <img alt='TensorFlow Blog Logo' class='tensorsite-blog-logo__image' src='https://www.gstatic.com/tf_blog/images/tfblog_logo.svg'/> </a> </div> <div class='header__side-menu__content'> <div class='header__side-menu__items'> <div class='header__search-container mobile'> <form action='' class='searchbox'> <input aria-label='Search box' class='search-input' name='q' onblur='if (this.value=="") {this.value="Search the Blog";}' onfocus='if (this.value=="Search the Blog") {this.value=""}' placeholder='Search the Blog' type='text' value=''/> <i class='material-icons'>search</i> <input style='visibility:hidden;position:absolute' type='submit'/> </form> </div> <div class='header__side-menu__title'>Tags</div> <tab> <li> <a class='header__nav-item mobile' dir='ltr' href='https://blog.tensorflow.org/'> All </a> </li> </tab> <tab> <li> <a class='header__nav-item mobile' dir='ltr' href='https://blog.tensorflow.org/search?label=TensorFlow+Core&max-results=20'> TensorFlow Core </a> </li> </tab> <tab> <li> <a class='header__nav-item mobile' dir='ltr' href='https://blog.tensorflow.org/search?label=TensorFlow.js&max-results=20'> TensorFlow.js </a> </li> </tab> <tab> <li> <a class='header__nav-item mobile' dir='ltr' href='https://blog.tensorflow.org/search?label=TensorFlow+Lite&max-results=20'> TensorFlow Lite </a> </li> </tab> <tab> <li> <a class='header__nav-item mobile' dir='ltr' href='https://blog.tensorflow.org/search?label=TFX&max-results=20'> TFX </a> </li> </tab> <tab> <li> <a class='header__nav-item mobile' dir='ltr' href='https://blog.tensorflow.org/search?label=Community&max-results=20'> Community </a> </li> </tab> </div> <div class='spacer'></div> <div class='header__side-menu__bottom'> <a class='header__cta mobile' href='https://www.tensorflow.org/'> Return to TensorFlow Home </a> </div> </div> </div> </div></div> <div class='content-wrap'> <div class='section' id='blog'><div class='widget FeaturedPost' data-version='1' id='FeaturedPost1'> </div><div class='widget Blog' data-version='1' id='Blog1'> <div class='tensorsite-container--narrow'> <div class='tensorsite-detail'> <a aria-hidden='true' class='tensorsite-detail__next-url' hidden='true' href='https://blog.tensorflow.org/2024/10/whats-new-in-tensorflow-218.html'></a> <div aria-hidden='true' class='tensorsite-detail__current-url' hidden='true'>https://blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html</div> <div aria-hidden='true' class='tensorsite-detail__tags' hidden='true'> <span>AI</span> <b class='label-divider-dot'>·</b> <span>Community</span> </div> <div aria-hidden='true' class='tensorsite-detail__main-image' hidden='true'> https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/s1600/social-Practices-of-ML-systems-engineering.png </div> <p aria-hidden='true' class='tensorsite-detail__description' hidden='true'> <span class='tensorsite-content__info'> November 19, 2024 — </span> <em>Posted by Jason Jabbour, Kai Kleinbard and <a href="http://scholar.harvard.edu/vijay-janapa-reddi/" target="_blank">Vijay Janapa Reddi</a> (Harvard University)</em>Everyone wants to do the modeling work, but no one wants to do the engineering.<i>If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing and building the engines that take them there.</i>Introduction"Everyone wants to do modeling, but no one wants to do t… </p> <div class='tensorsite-content__subtitle'> <a href='https://blog.tensorflow.org/search?label=AI&max-results=20'> <span>AI</span> </a> <b class='label-divider-dot'>·</b> <a href='https://blog.tensorflow.org/search?label=Community&max-results=20'> <span>Community</span> </a> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-detail__title'> MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering </div> <div class='tensorsite-detail__contact'> <div class='tensorsite-detail__info'> <span class='tensorsite-detail__timestamp'>November 19, 2024</span> </div> <a class='icon-link' href='https://twitter.com/intent/tweet?text=%22MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering%22 from the TensorFlow Blog%0A%0Ahttps://blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html' rel='noopener noreferrer' target='_blank' title='Share this post on Twitter'> <svg alt='Twitter Social Icon' class='twitter-icon social-icon' height='19' viewBox='0 0 23 19' width='23' xmlns='http://www.w3.org/2000/svg'> <g fill='none' fill-rule='evenodd' transform='translate(-7 -9)'> <rect height='36' width='36'></rect> <path d='M14.076,27.2827953 C22.566,27.2827953 27.21,20.2477953 27.21,14.1477953 C27.21,13.9477953 27.21,13.7487953 27.197,13.5507953 C28.1,12.8977953 28.88,12.0887953 29.5,11.1617953 C28.657,11.5347953 27.764,11.7797953 26.848,11.8877953 C27.812,11.3107953 28.533,10.4037953 28.878,9.33479527 C27.972,9.87179527 26.98,10.2507953 25.947,10.4547953 C24.198,8.59579527 21.274,8.50679527 19.415,10.2547953 C18.217,11.3817953 17.708,13.0617953 18.08,14.6647953 C14.368,14.4787953 10.91,12.7257953 8.566,9.84279527 C7.341,11.9507953 7.967,14.6497953 9.995,16.0047953 C9.261,15.9827953 8.542,15.7837953 7.9,15.4267953 L7.9,15.4847953 C7.9,17.6827953 9.449,19.5747953 11.603,20.0107953 C10.924,20.1957953 10.211,20.2227953 9.519,20.0897953 C10.124,21.9707953 11.856,23.2587953 13.832,23.2957953 C12.197,24.5797953 10.178,25.2777953 8.098,25.2747953 C7.731,25.2747953 7.364,25.2527953 7,25.2087953 C9.111,26.5627953 11.567,27.2817953 14.076,27.2787953' fill='#545454'></path> </g> </svg> </a> </div> <div class='divider divider--article-top'></div> <div class='tensorsite-detail__body'> <meta content="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/s1600/social-Practices-of-ML-systems-engineering.png" name="twitter:image"></meta> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/s1600/social-Practices-of-ML-systems-engineering.png" style="display: none;" /> <em>Posted by Jason Jabbour, Kai Kleinbard and <a href="http://scholar.harvard.edu/vijay-janapa-reddi/" target="_blank">Vijay Janapa Reddi</a> (Harvard University)</em> <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjWzldLNhl1RyM2-V8XdUtlf4P5__oJdAaN9GxyGvic4K8RCtOH8KpixLS-oQF5ZB5zn89d3QX-lX_6rn2eqeCJ-evLJKfo7unJRL8sGFodqswVywDYmc9sRTkf-Bo3ToOgECA7nElSXroZBsNFh_o2chlCuipwlWAwyJ4gLvxiosMQcU-8iRpf5jaUuxk/s1600/header-Practices-of-ML-systems-engineering.png"><img border="0" data-original-height="800" data-original-width="100%" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjWzldLNhl1RyM2-V8XdUtlf4P5__oJdAaN9GxyGvic4K8RCtOH8KpixLS-oQF5ZB5zn89d3QX-lX_6rn2eqeCJ-evLJKfo7unJRL8sGFodqswVywDYmc9sRTkf-Bo3ToOgECA7nElSXroZBsNFh_o2chlCuipwlWAwyJ4gLvxiosMQcU-8iRpf5jaUuxk/s1600/header-Practices-of-ML-systems-engineering.png" /></a> <a name='more'></a><p></p> <h2>Everyone wants to do the modeling work, but no one wants to do the engineering.</h2> <p><i>If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing and building the engines that take them there.</i></p> <h3>Introduction</h3> <p>"Everyone wants to do modeling, but no one wants to do the engineering," highlights a stark reality in the machine learning (ML) world: the allure of building sophisticated models often overshadows the critical task of engineering them into robust, scalable, and efficient systems.</p> <p>The reality is that ML and systems are inextricably linked. Models, no matter how innovative, are computationally demanding and require substantial resources—with the rise of generative AI and increasingly complex models, understanding how ML infrastructure scales becomes even more critical. Ignoring the system's limitations during model development is a recipe for disaster.</p> <p>Unfortunately, educational resources on the systems side of machine learning are lacking. There are plenty of textbooks and materials on <a href="https://www.tensorflow.org/resources/learn-ml#books" target="_blank">deep learning theory and concepts</a>. However, we truly need more resources on the infrastructure and systems side of machine learning. Critical questions—such as how to optimize models for specific hardware, deploy them at scale, and ensure system efficiency and reliability—are still not adequately understood by ML practitioners. This lack of understanding is not due to disinterest but rather a gap in available knowledge.</p> <p>One significant resource addressing this gap is <a href="http://MLSysBook.ai" target="_blank">MLSysBook.ai</a>. This blog post explores key ML systems engineering concepts from MLSysBook.ai and maps them to the TensorFlow ecosystem to provide practical insights for building efficient ML systems.</p> <h3>The Connection Between Machine Learning and Systems</h3> <p>Many think machine learning is solely about extracting patterns and insights from data. While this is fundamental, it’s only part of the story. Training and deploying these "deep" neural network models often necessitates vast computational resources, from powerful GPUs and TPUs to massive datasets and distributed computing clusters.</p> <p>Consider the recent wave of large language models (LLMs) that have pushed the boundaries of natural language processing. These models highlight the immense computational challenges in training and deploying large-scale machine learning models. Without carefully considering the underlying system, training times can stretch from days to weeks, inference can become sluggish, and deployment costs can skyrocket.</p> <p>Building a successful machine-learning solution involves the entire system, not just the model. This is where ML systems engineering takes the reins, allowing you to optimize model architecture, hardware selection, and deployment strategies, ensuring that your models are not only powerful in theory but also efficient and scalable.</p> <p>To draw an analogy, if developing algorithms is like being an astronaut exploring the vast unknown of space, then ML systems engineering is similar to the work of rocket scientists building the engines that make those journeys possible. Without the precise engineering of rocket scientists, even the most adventurous astronauts would remain earthbound.</p> <div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjsH_zM77JldWBIbYLEZWoocfBzqhBA0iul9s9ZUHsAYmSYoUIEN6aWoUFe3woWvHLgFRNsUiUkKGWcYS4nDjBqiMI3o-DMgIxmLIH-wURGkvfwox5NF5oDtczINYqcisbOUJhUDGuw2KtZAly-wiMS7_nHLy2FkJCtTXyT3hcmPExZnxk1Hgz6vwwvaYs/s1600/MLSysbook-AI-cover-image.png" style="display: block; padding: 1em 0px; text-align: center;"><img alt="An abstract circular design resembling a network or neural pathways consisting of interconnected nodes and lines in shades of blue, pink, and gray, against a white background" border="0" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjsH_zM77JldWBIbYLEZWoocfBzqhBA0iul9s9ZUHsAYmSYoUIEN6aWoUFe3woWvHLgFRNsUiUkKGWcYS4nDjBqiMI3o-DMgIxmLIH-wURGkvfwox5NF5oDtczINYqcisbOUJhUDGuw2KtZAly-wiMS7_nHLy2FkJCtTXyT3hcmPExZnxk1Hgz6vwwvaYs/s1600/MLSysbook-AI-cover-image.png" width="100%" /></a></div> <h3>Bridging the Gap: MLSysBook.ai and System-Level Thinking</h3> <p>One important new resource this blog post offers for insights into ML systems engineering is an open-source "textbook" — <a href="http://MLSysBook.ai" target="_blank">MLSysBook.ai</a> —developed initially as part of Harvard University's <a href="https://sites.google.com/g.harvard.edu/cs249-tinyml-2023" target="_blank">CS249r Tiny Machine Learning</a> course and <a href="https://www.edx.org/certificates/professional-certificate/harvardx-tiny-machine-learning" target="_blank">HarvardX's TinyML</a> online series. This project, which has expanded into an open, collaborative initiative, dives deep into the end-to-end ML lifecycle.</p> <p>It highlights that the principles governing ML systems, whether designed for tiny embedded devices or large data centers, are fundamentally similar. For instance, while tiny machines might employ INT8 for numeric operations to save resources, larger systems often utilize FP16 for higher precision—the fundamental concepts, such as quantization, span across both scenarios.</p> <p>Key concepts covered in this resource include:</p> <ul><ol> <li><b>Data Engineering:</b> Setting the foundation by efficiently collecting, preprocessing, and managing data to prepare it for the machine learning pipeline.</li> <li><b>Model Development:</b> Crafting and refining machine learning models to meet specific tasks and performance goals.</li> <li><b>Optimization:</b> Fine-tuning model performance and efficiency, ensuring effective use of hardware and resources within the system.</li> <li><b>Deployment:</b> Transitioning models from development to real-world production environments while scaling and adapting them to existing infrastructure.</li> <li><b>Monitoring and Maintenance:</b> Continuously tracking system health and performance to maintain reliability, address issues, and adapt to evolving data and requirements.</li> </ol></ul> <p>In an efficient ML system, data engineering lays the groundwork by preparing and organizing raw data, which is essential for any machine learning process. This ensures data can be transformed into actionable insights during model development, where machine learning models are created and refined for specific tasks. Following development, optimization becomes critical for enhancing model performance and efficiency, ensuring that models are tuned to run effectively on the designated hardware and within the system's constraints.</p> <p>The seamless integration of these steps then extends into the deployment phase, where models are brought into real-world production environments. Here, they must be scaled and adapted to function effectively within existing infrastructure, highlighting the importance of robust ML systems engineering. However, the lifecycle of an ML system continues after deployment; continuous monitoring and maintenance are vital. This ongoing process ensures that ML systems remain healthy, reliable and perform optimally over time, adapting to new data and requirements as they arise.</p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikJkY7JpFbxDQ_j0CRSad_aP9CJHu9a9NxEq6eVK50_tcdJLum-gBeWKd1bUTVf8e9yk6zsIgleXvX8j2nQBcY7WVrWe7cutfuiRHAh3fODXrv-j4ye-FnGkGs5SQqZyojTbhQtNqkyKHwkksLVtgh-Mspfzj1PSqN71ULNhdiQ_qXj_F4rpVzHvqtFQA/s1600/image1.png" style="display: block; margin-left: auto; margin-right: auto; padding: 1em 0px; text-align: center;"><img alt="A flowchart diagrams the dependencies between different machine learning concepts, tools, and systems. Beige boxes represent concepts like 'Data Engineering' and tools like 'TensorFlow Data', while blue boxes indicate higher-level systems like 'ML Systems Engineering Principles' and 'Efficient ML Systems'. Arrows and dotted lines illustrate the relationships and workflow between these elements." border="0" data-original-height="1999" data-original-width="1388" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikJkY7JpFbxDQ_j0CRSad_aP9CJHu9a9NxEq6eVK50_tcdJLum-gBeWKd1bUTVf8e9yk6zsIgleXvX8j2nQBcY7WVrWe7cutfuiRHAh3fODXrv-j4ye-FnGkGs5SQqZyojTbhQtNqkyKHwkksLVtgh-Mspfzj1PSqN71ULNhdiQ_qXj_F4rpVzHvqtFQA/s1600/image1.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="text-align: start;"><i>A mapping of MLSysBook.AI's core ML systems engineering concepts to the TensorFlow ecosystem, illustrating how specific TensorFlow tools support each stage of the machine learning lifecycle, ultimately contributing to the creation of efficient ML systems.</i></span></td></tr></tbody></table> <h3>SocratiQ: An Interactive AI-Powered Generative Learning Assistant</h3> <p>One of the exciting innovations we’ve integrated into MLSysBook.ai is SocratiQ—an AI-powered learning assistant designed to foster a deeper and more engaging connection with content focused on machine learning systems. By leveraging a Large Language Model (LLM), SocratiQ turns learning into a dynamic, interactive experience that allows students and practitioners to engage with and co-create their educational journey actively.</p> <p>With SocratiQ, readers transition from passive content consumption to an active, personalized learning experience. Here’s how SocratiQ makes this possible:</p> <ul> <li><b>Interactive Quizzes:</b> SocratiQ enhances the learning process by automatically generating quizzes based on the reading content. This feature encourages active reflection and reinforces understanding without disrupting the learning flow. Learners can test their comprehension of complex ML systems concepts.</li> <div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh2kTEI9cc1EC8cT3TZOQOlOjid4Stn6HUw6u_lMJhzlyFjN9uUhEiAF7KgdLt4Kkx-WsrD6h2qD2SXYixq3mhNBHcofRNjirj5YBGkIakL88shHyLDPWF42XXFU6S3pZrY8jSaoA617qSo96w68yc1mfCTdxxpGMX2dmMuR2Aq6TEAIsMtAjVGMlQzzwA/s1600/image1.gif" style="display: block; padding: 1em 0; text-align: center; "><img alt="moving image of an interactive quiz in SocratiQ" border="0" data-original-height="375" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh2kTEI9cc1EC8cT3TZOQOlOjid4Stn6HUw6u_lMJhzlyFjN9uUhEiAF7KgdLt4Kkx-WsrD6h2qD2SXYixq3mhNBHcofRNjirj5YBGkIakL88shHyLDPWF42XXFU6S3pZrY8jSaoA617qSo96w68yc1mfCTdxxpGMX2dmMuR2Aq6TEAIsMtAjVGMlQzzwA/s1600/image1.gif"/></a></div> <li><b>Adaptive, In-Content Learning:</b> SocratiQ offers real-time conversations with the LLM without pulling learners away from the content they're engaging with. Acting as a personalized Teaching Assistant (TA), it provides tailored explanations.</li> <div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjUay_zDBbYIRqjdZURADOmGhOddBRBVCFpavhrLd58U_DPVO6svaHDWdZt1v79OBztkIGX8YX5HUsIwsTZH7GdW41UvB2tBN9pFh_F7ndV3GRypiXSFdur-Urzbk7ADgGnMVwpAyGDiChNwX3tHQSlbXxz2QMT1qlt1JFeNdm95_FOHrjo0uRPIK9iD84/s1600/image2.gif" style="display: block; padding: 1em 0; text-align: center; "><img alt="moving image of an real-time conversation with the LLM in SocratiQ" border="0" data-original-height="368" data-original-width="599" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjUay_zDBbYIRqjdZURADOmGhOddBRBVCFpavhrLd58U_DPVO6svaHDWdZt1v79OBztkIGX8YX5HUsIwsTZH7GdW41UvB2tBN9pFh_F7ndV3GRypiXSFdur-Urzbk7ADgGnMVwpAyGDiChNwX3tHQSlbXxz2QMT1qlt1JFeNdm95_FOHrjo0uRPIK9iD84/s1600/image2.gif"/></a></div> <li><b>Progress Assessment and Gamification:</b> Learners’ progress is tracked and stored locally in their browser, providing a personalized path to developing skills without privacy concerns. This allows for evolving engagement as the learner progresses through the material.</li> <div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhn8OGbgDk-LadioL-JftNCi3Dzc8g0t46yHHiXEOwK07fynDdsdGTbBVfA8DuHtd72ygnpuWw3dk6hQmAbaIFT590IHtTyCZdFzRoNtriU07Ww4p4rV3sFaHcM6bvO2VLphGyDhZ0wLX3Po4VGJ69aXehtbaJWArUxDIqkrQGXftB3CYR2RBzV_lLdJs/s1600/image5.png" style="display: block; padding: 1em 0; text-align: center; "><img alt="A Quiz Performance Dashboard in SocratiQ" border="0" data-original-height="796" data-original-width="1406" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhn8OGbgDk-LadioL-JftNCi3Dzc8g0t46yHHiXEOwK07fynDdsdGTbBVfA8DuHtd72ygnpuWw3dk6hQmAbaIFT590IHtTyCZdFzRoNtriU07Ww4p4rV3sFaHcM6bvO2VLphGyDhZ0wLX3Po4VGJ69aXehtbaJWArUxDIqkrQGXftB3CYR2RBzV_lLdJs/s1600/image5.png"/></a></div> </ul> <p>SocratiQ strives to be a supportive guide that respects the primacy of the content itself. It subtly integrates into the learning flow, stepping in when needed to provide guidance, quizzes, or explanations—then stepping back to let the reader continue undistracted. This design ensures that SocratiQ works harmoniously within the natural reading experience, offering support and personalization while keeping the learner immersed in the content.</p> <p>We plan to integrate capabilities such as research lookups and case studies. The aim is to create a unique learning environment where readers can study and actively engage with the material. This blend of content and AI-driven assistance transforms MLSysBook.ai into a living educational resource that grows alongside the learner's understanding.</p> <h3>Mapping MLSysBook.ai's Concepts to the TensorFlow Ecosystem</h3> <p>MLSysBook.AI focuses on the core concepts in ML system engineering while providing strategic tie-ins to the TensorFlow ecosystem. The TensorFlow ecosystem offers a rich environment for realizing many of the principles discussed in MLSysBook.AI. This makes the TensorFlow ecosystem a perfect match for the key ML systems concepts covered in MLSysBook.AI, with each tool supporting a specific stage of the machine learning process:</p> <ul> <li><b><a href="https://www.tensorflow.org/datasets" target="_blank">TensorFlow Data</a> (Data Engineering):</b> Supports efficient data preprocessing and input pipelines.</li> <li><b><a href="https://www.tensorflow.org/tutorials" target="_blank">TensorFlow Core</a> (Model Development):</b> Central to model creation and training.</li> <li><b><a href="https://www.tensorflow.org/lite" target="_blank">TensorFlow Lite</a> (Optimization):</b> Enables model optimization for various deployment scenarios, especially critical for edge devices.</li> <li><b><a href="https://www.tensorflow.org/tfx/guide/serving" target="_blank">TensorFlow Serving</a> (Deployment):</b> Facilitates smooth model deployment in production environments. </li> <li><b><a href="https://www.tensorflow.org/tfx" target="_blank">TensorFlow Extended</a> (Monitoring and maintenance):</b> Offers comprehensive tools for ongoing system health and performance.</li> </ul> <p>Note that MLSysBook.AI does not explicitly teach or focus on TensorFlow-specific concepts or implementations. The book's primary goal is to explore fundamental ML system engineering principles. The connections drawn in this blog post to the TensorFlow ecosystem are simply intended to illustrate how these core concepts align with tools and practices used by industry practitioners, providing a bridge between theoretical understanding and real-world application.</p> <h3>Support ML Systems Education: Every Star Counts 馃専</h3> <p>If you find this blog post valuable and want to improve ML systems engineering education, please consider giving the MLSysBook.ai <a href="https://github.com/harvard-edge/cs249r_book" target="_blank">GitHub repository</a> a star ⭐.</p> <p>Thanks to our sponsors, each ⭐ added to the MLSysBook.ai GitHub repository translates to donations supporting students and minorities globally by funding their research scholarships, empowering them to drive innovation in machine learning systems research worldwide.</p> <p>Every star counts—help us reach the generous funding cap!</p> <h3>Conclusion</h3> <p>The gap between ML modeling and system engineering is closing, and understanding both aspects is important for creating impactful AI solutions. By embracing ML system engineering principles and leveraging powerful tools like those in the TensorFlow ecosystem, we can go beyond building models to creating complete, optimized, and scalable ML systems.</p> <p>As AI continues to evolve, the demand for professionals who can bridge the gap between ML algorithms and systems implementation will only grow. Whether you're a seasoned practitioner or just starting your ML journey, investing time in understanding ML systems engineering will undoubtedly pay dividends in your career and the impact of your work. If you’d like to learn more, listen to our MLSysBook.AI <a href="https://notebooklm.google.com/notebook/bae2e128-7926-4ba9-8503-2b54ff3237f9/audio" target="_blank">podcast</a>, generated by Google’s NotebookLM.</p> <p>Remember, even the most brilliant astronauts need skilled engineers to build their rockets!</p> <h4>Acknowledgments</h4> <p><i>We thank Josh Gordon for his suggestion to write this blog post and for encouraging and sharing ideas on how the book could be a useful resource for the TensorFlow community.</i></p> </div> </div> <div class='tensorsite-detail-footer'> <div class='article-divider'> <img alt='Diamond Article Divider' src='https://www.gstatic.com/tf_blog/images/ic_article_end.svg'/> </div> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=AI&max-results=20'> AI </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Community&max-results=20'> Community </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Data+Engineering&max-results=20'> Data Engineering </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Deployment&max-results=20'> Deployment </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Machine+Learning&max-results=20'> Machine Learning </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Model+Development&max-results=20'> Model Development </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Monitoring+%26amp;+Maintenance&max-results=20'> Monitoring & Maintenance </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Optimization&max-results=20'> Optimization </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=SocratiQ&max-results=20'> SocratiQ </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Systems+Engineering&max-results=20'> Systems Engineering </a> </div> </div> <div class='tensorsite-next'> <div class='tensorsite-container--large'> <div class='tensorsite-next__title'>Next post</div> <div class='tensorsite-card'> <a aria-label='Next Card' class='tensorsite-card__href next' href='https://blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html'></a> <div class='tensorsite-content__image-wrapper'> <img alt='MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering' class='tensorsite-content__image' src='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/s1600/social-Practices-of-ML-systems-engineering.png'/> </div> <div class='tensorsite-content'> <div class='tensorsite-content__subtitle next'> <span>AI</span> <b class='label-divider-dot'>·</b> <span>Community</span> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-content__title next'> MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering </div> <p class='tensorsite-content__description next'> <span class='tensorsite-content__info'> November 19, 2024 </span> — <span> <em>Posted by Jason Jabbour, Kai Kleinbard and <a href="http://scholar.harvard.edu/vijay-janapa-reddi/" target="_blank">Vijay Janapa Reddi</a> (Harvard University)</em>Everyone wants to do the modeling work, but no one wants to do the engineering.<i>If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing and building the engines that take them there.</i>Introduction"Everyone wants to do modeling, but no one wants to do t… </span> </p> </div> </div> </div> </div>  </div></div> </div>  <div class='tensorsite-full-footer'> <div class='section' id='footer'><div class='widget HTML' data-version='1' id='HTML2'> <section class='tensorsite-footer'> <div class='tensorsite-container tensorsite-footer__container'> <div class='tensorsite-footer__side tensorsite-footer__side--left'></div> <div class='tensorsite-footer__side tensorsite-footer__side--right'></div> <div class='tensorsite-footer__content'> <div class='tensorsite-content'> <div class='tensorsite-content__title tensorsite-content__title--grow'> Build, deploy, and experiment easily with TensorFlow </div> <div class='tensorsite-content__cta-wrapper'> <a class='tensorsite-content__button tensorsite-button tensorsite-button--white' href='https://www.tensorflow.org/'> Get started </a> </div> </div> </div> </div> <div class='tensorsite-footer__lines'></div> </section> <section class='social-icons'> <div class='social-icons__container-footer'> <a alt='TensorFlow Home' class='tensorsite-logo' href='https://www.tensorflow.org/'> <img alt='TensorFlow Logo' class='tensorsite-logo__image' src='https://www.gstatic.com/tf_blog/images/tf_lockup.svg'/> </a> <div class='footer__side--right'> <div class='footer__links'> <a alt='Youtube Social Link' class='footer-link' href='https://www.google.com/' rel='noopener noreferrer' target='_blank'> Google </a> <a alt='Twitter Social Link' class='footer-link' href='https://policies.google.com/privacy' rel='noopener noreferrer' target='_blank'> Privacy </a> <a alt='Github Link' class='footer-link' href='https://policies.google.com/terms' rel='noopener noreferrer' target='_blank'> Terms </a> <a class='footer-link' href='https://blog.tensorflow.org/p/tensorflow-blog-contribution-notice.html'> Contributions notice </a> </div> <div class='social-icons__links'> <a alt='Youtube Social Link' class='icon-link' href='https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ' rel='noopener noreferrer' target='_blank'> <img src='https://www.gstatic.com/tf_blog/images/ic_youtube.svg'/></a> <a alt='Twitter Social Link' class='icon-link' href='https://twitter.com/TensorFlow' rel='noopener noreferrer' target='_blank'> <img src='https://www.gstatic.com/tf_blog/images/ic_twitter.svg'/></a> <a alt='Github Link' class='icon-link' href='https://github.com/tensorflow' rel='noopener noreferrer' target='_blank'> <img src='https://www.gstatic.com/tf_blog/images/ic_github.svg'/></a> </div> </div> </div> </section> </div></div> </div> <script type="text/javascript" src="https://www.blogger.com/static/v1/widgets/984859869-widgets.js"></script> <script type='text/javascript'> window['__wavt'] = 'AOuZoY7wsiVs4lhu_2BJvQeixllom6d27g:1732348674711';_WidgetManager._Init('//www.blogger.com/rearrange?blogID\x3d7864883956188652345','//blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html','7864883956188652345'); _WidgetManager._SetDataContext([{'name': 'blog', 'data': {'blogId': '7864883956188652345', 'title': 'The TensorFlow Blog', 'url': 'https://blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html', 'canonicalUrl': 'https://blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html', 'homepageUrl': 'https://blog.tensorflow.org/', 'searchUrl': 'https://blog.tensorflow.org/search', 'canonicalHomepageUrl': 'https://blog.tensorflow.org/', 'blogspotFaviconUrl': 'https://blog.tensorflow.org/favicon.ico', 'bloggerUrl': 'https://www.blogger.com', 'hasCustomDomain': true, 'httpsEnabled': true, 'enabledCommentProfileImages': true, 'gPlusViewType': 'FILTERED_POSTMOD', 'adultContent': false, 'analyticsAccountNumber': 'UA-142343919-1', 'encoding': 'UTF-8', 'locale': 'en', 'localeUnderscoreDelimited': 'en', 'languageDirection': 'ltr', 'isPrivate': false, 'isMobile': false, 'isMobileRequest': false, 'mobileClass': '', 'isPrivateBlog': false, 'isDynamicViewsAvailable': true, 'feedLinks': '\x3clink rel\x3d\x22alternate\x22 type\x3d\x22application/atom+xml\x22 title\x3d\x22The TensorFlow Blog - Atom\x22 href\x3d\x22https://blog.tensorflow.org/feeds/posts/default\x22 /\x3e\n\x3clink rel\x3d\x22alternate\x22 type\x3d\x22application/rss+xml\x22 title\x3d\x22The TensorFlow Blog - RSS\x22 href\x3d\x22https://blog.tensorflow.org/feeds/posts/default?alt\x3drss\x22 /\x3e\n\x3clink rel\x3d\x22service.post\x22 type\x3d\x22application/atom+xml\x22 title\x3d\x22The TensorFlow Blog - Atom\x22 href\x3d\x22https://www.blogger.com/feeds/7864883956188652345/posts/default\x22 /\x3e\n\n\x3clink rel\x3d\x22alternate\x22 type\x3d\x22application/atom+xml\x22 title\x3d\x22The TensorFlow Blog - Atom\x22 href\x3d\x22https://blog.tensorflow.org/feeds/9124147844941336220/comments/default\x22 /\x3e\n', 'meTag': '', 'adsenseHostId': 'ca-host-pub-1556223355139109', 'adsenseHasAds': true, 'adsenseAutoAds': false, 'boqCommentIframeForm': true, 'loginRedirectParam': '', 'view': '', 'dynamicViewsCommentsSrc': '//www.blogblog.com/dynamicviews/4224c15c4e7c9321/js/comments.js', 'dynamicViewsScriptSrc': '//www.blogblog.com/dynamicviews/d78375fb222d99b3', 'plusOneApiSrc': 'https://apis.google.com/js/platform.js', 'disableGComments': true, 'interstitialAccepted': false, 'sharing': {'platforms': [{'name': 'Get link', 'key': 'link', 'shareMessage': 'Get link', 'target': ''}, {'name': 'Facebook', 'key': 'facebook', 'shareMessage': 'Share to Facebook', 'target': 'facebook'}, {'name': 'BlogThis!', 'key': 'blogThis', 'shareMessage': 'BlogThis!', 'target': 'blog'}, {'name': 'X', 'key': 'twitter', 'shareMessage': 'Share to X', 'target': 'twitter'}, {'name': 'Pinterest', 'key': 'pinterest', 'shareMessage': 'Share to Pinterest', 'target': 'pinterest'}, {'name': 'Email', 'key': 'email', 'shareMessage': 'Email', 'target': 'email'}], 'disableGooglePlus': true, 'googlePlusShareButtonWidth': 0, 'googlePlusBootstrap': '\x3cscript type\x3d\x22text/javascript\x22\x3ewindow.___gcfg \x3d {\x27lang\x27: \x27en\x27};\x3c/script\x3e'}, 'hasCustomJumpLinkMessage': false, 'jumpLinkMessage': 'Read more', 'pageType': 'item', 'postId': '9124147844941336220', 'postImageThumbnailUrl': 'https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/s72-c/social-Practices-of-ML-systems-engineering.png', 'postImageUrl': 'https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/s1600/social-Practices-of-ML-systems-engineering.png', 'pageName': 'MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering', 'pageTitle': 'The TensorFlow Blog: MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering', 'metaDescription': 'MLSysBook.ai explores key ML systems engineering concepts and how TensorFlow tools support each stage of the machine learning life cycle.'}}, {'name': 'features', 'data': {}}, {'name': 'messages', 'data': {'edit': 'Edit', 'linkCopiedToClipboard': 'Link copied to clipboard!', 'ok': 'Ok', 'postLink': 'Post Link'}}, {'name': 'template', 'data': {'name': 'custom', 'localizedName': 'Custom', 'isResponsive': false, 'isAlternateRendering': false, 'isCustom': true}}, {'name': 'view', 'data': {'classic': {'name': 'classic', 'url': '?view\x3dclassic'}, 'flipcard': {'name': 'flipcard', 'url': '?view\x3dflipcard'}, 'magazine': {'name': 'magazine', 'url': '?view\x3dmagazine'}, 'mosaic': {'name': 'mosaic', 'url': '?view\x3dmosaic'}, 'sidebar': {'name': 'sidebar', 'url': '?view\x3dsidebar'}, 'snapshot': {'name': 'snapshot', 'url': '?view\x3dsnapshot'}, 'timeslide': {'name': 'timeslide', 'url': '?view\x3dtimeslide'}, 'isMobile': false, 'title': 'MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering', 'description': 'MLSysBook.ai explores key ML systems engineering concepts and how TensorFlow tools support each stage of the machine learning life cycle.', 'featuredImage': 'https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCrY0ybsygB35ZxLoYErMW4s26SSiF_pHCKXTSO2poVyVL2-zlIr0WC6yugLgtnQzi7m_OlgnzUKEMG50g6tDH_CrClBqDK_Py8BXjHcZxWFprthF6uRcOzI1EaXSRhYl-xpUyTycUA6vXmjWPNWsYjOvCcBxsjbZ3TcsN7kNUW8dfVrEJuGY_gYjqi1Y/s1600/social-Practices-of-ML-systems-engineering.png', 'url': 'https://blog.tensorflow.org/2024/11/mlsysbookai-principles-and-practices-of-machine-learning-systems-engineering.html', 'type': 'item', 'isSingleItem': true, 'isMultipleItems': false, 'isError': false, 'isPage': false, 'isPost': true, 'isHomepage': false, 'isArchive': false, 'isLabelSearch': false, 'postId': 9124147844941336220}}]); _WidgetManager._RegisterWidget('_HTMLView', new _WidgetInfo('HTML1', 'nav', document.getElementById('HTML1'), {}, 'displayModeFull')); _WidgetManager._RegisterWidget('_FeaturedPostView', new _WidgetInfo('FeaturedPost1', 'blog', document.getElementById('FeaturedPost1'), {}, 'displayModeFull')); _WidgetManager._RegisterWidget('_BlogView', new _WidgetInfo('Blog1', 'blog', document.getElementById('Blog1'), {'cmtInteractionsEnabled': false, 'lightboxEnabled': true, 'lightboxModuleUrl': 'https://www.blogger.com/static/v1/jsbin/2646514562-lbx.js', 'lightboxCssUrl': 'https://www.blogger.com/static/v1/v-css/1964470060-lightbox_bundle.css'}, 'displayModeFull')); _WidgetManager._RegisterWidget('_HTMLView', new _WidgetInfo('HTML2', 'footer', document.getElementById('HTML2'), {}, 'displayModeFull')); </script> </body> </html>