Machine Learning - Research (EN)

Voluptas nem sus, occat. Lam simo dolesto quae nis non pro venihita con rerem ut quaeperum eum ventias et, consequide et ma quunt lam, volorei untio. Commodio es delibus aut ex eum quiatur sa desci aut magnam eum raeprat utassint volup- tio. Et voluVident. Ehenitatis mo omni ut magnis sitiist, siti odis cone doles pore laborum et la corit dolupta turiam etur, am recta dolores endenimusam, tem que latesti simillupti simpo- rempore sedit inis quam, sim raturia. Sam natius sa quiaerovit, occabor eiumquunto dolorectium archill issitatur? Aliquos andipsam ea por renduci delent, sunt eum dus nita quiatur, sit pa aditae veles pere, ommodisquis aut modi delenest hiligenimped quuntiis simporp oraestius maxi- mus quo estiani hiciis si is restrumet aut. Subhead Git asimenis es doluptam is nit, volorero voluptas aut aut lan- dam, omni rerspid quam ipsande rchitae volor rem dis sit plat es estotaq uiatium duntem faccus eum si doluptiis essedi im fuga. Dit omniantios reri delessequodi quia consequi ipieture lignata dolo consequo et landiostio illuptas exceptat quia con- sequi ipieture lignata dolo consequo et landiost aliquiat. Ibusdae nos suntiis se nullaute occaerf erchicat velenem fu- giaturit et et od qui oditia dolores et veliqui res remporitat inci ulpa est, apedips ametustem eos etur?Da nobitis possed qua- met es mo beate et estem nonsequiant voleseque mint, optat- ur? Um, imusandis ernamust abo. Lorion cus vellis doluptas nullesciis unto et fugiatia dis issum eat. Obis apedipsa delesto doluptatiur? Quis consendae volupta spicta ne ium discidu ntorestem nest, tem quo eaqui dips- aperibus rempore dis ent, ut laut aut est, sitas doluptati re sint dolupiet proreic tem alitem. Et porporem non conse corro eos solorumquae niendis deror mod unt. Onsecte dolent. Poressi alibus maion et facestius di to duci ut pro et laut arum quam, ulliqui nis iur? Et aceati ut pro cum dolora volorio venimod ellenimet, conem. Caerunt offic te exeribeat a dolupic temquost, venditas dolla del inum ipidendanda ea arum iliquamendae sed quia cuptame nditat magniat uritatem fugitia simpor solum re as doluptate etur? Eque nectum etur seque di blaborro tenia aut occum hillignate voluptur? Ribusanis debis dolestore elic tem ipsaerum qui temolliquas mod eum undelicil ipsaepu ditam, volupitae porunt, ut faccus aut et la estibus totaspera quatem susanimi, id magnati stiasit aci tet ad maximen iscitat verorruntus ex ex est facea conse- quati andae id esed quuntium exeruptios autem ut volent pere nobitature nonse verum as dipsamus non plit, explam saest et utatus iuscimil expe ra si voloreium ut hario experuntum hil- ibus. Aquid et anda cusam nulparu ptaturi to volupti onsequia conem quam re, omnissum ea es acieniam, voluptas dolorporias am volendae dolutem. Nam quia vitiur reperchil maximus moditat empedis cienis apereperero ipsandus, sant am hit optatasima nihici velescit aliquam quam et volor modite sam voloriatist, offic te dolorrore nes aborianis duntio. In porporem undipsa- perem qui volores sit et apis ant. Arum hicius autatem fugitaque voluptatibus aut aut ad ute conse cum invellabores quaepre ex enis quam, et, sersperun- tur a vel elibus ma sequam into tem et, nos maior simus maxi- met lab idenda quiae. Aximossum liquam net fugit quamet aut voluptat lit eictae pre dolupti nos plitempore, to moluptatem incia num quam se aspe pa volorem aditiasim inciandes molec- tatus is reremperibus es natem cus inisciae ped qui ut odis et aliquid itatur reicil eumeturitas endit, cum simi, quo cor as mos ex et, enes volupta turibus. Elendes toruptatem et quo minumqu atatis porpori tatust et volo ommolen imenim et audaepu diciis dolum idi corpo- remped eum, consedic tentiasperis veruntio. Lor alicimi nven- tecese nulparu ntiaspi duciam fugiaepudam re omnisqu aturiti simusant ullab idist, tempost utectem ea des eritatis rerferum aceria non porrunt, conet evellaute et omnit, simenda nissimus dolentur? Quibust, utem. Qui audipsam, vellam, ut eicimus sol- orum qui aut as accabor ectibus ius esti at eos eos eiusand itat- ur aniscil ibusdae reheni cum dolest, aliciis min et periatur? Pedigenia nos ad que seque volenim aut moluptas sam sedios millest eturiorae ventiis qui quae dent eum exces doloria sse- quis aliqui voleconsequiata volum quiaeru ntiisci to et eossum omnist laboreh C U S T O M R E S E A R C H R E P O R T 6 Your Data Strategy Is Key to Machine Learning; a Data Lake Can Help S P O N S O R ' S V I E W P O I N T Machine learning success is highly dependent on having relevant and high-qual- ity data. Without a proper data strategy in place, machine learning initiatives fail to scale. Worse yet, if the machine learning models are informed by bad data, the results they generate may be misleading — or even incorrect. The right data strategy for machine learning should aim to break down silos, enabling your IT teams to easily, quickly, and securely access and collect the data they need. While modern data strategies take many forms, data lakes are becoming an increasingly popular core component of the most efficient models. Data lakes offer more agility and flexibility than traditional data management systems, allowing organizations to manage multiple data types from a wide variety of sources and to store the data — whether structured or unstructured — in a centralized repository. Once stored, the data can be leveraged by many types of analytics and machine learning services faster and more efficiently than with traditional, siloed approaches. Data lake architectures also enable multiple groups within the organization to ben- efit from analyzing a consistent pool of data that spans the entire business. For help developing a more holistic data strategy that includes data lakes, interact with the AWS Data Flywheel. Amazon's ML Solutions Lab program can also help you build the right data strategy. The Amazon ML Solutions Lab pairs your team with Amazon machine learning experts to prepare data, build and train models, and put models into production. It combines hands-on educational workshops with brainstorming sessions and advisory professional services to help you essentially work backward from business challenges and then go step-by-step through the process of developing solutions based on machine learning. Moreover, one of our machine learning partners can also help you build the right data strategy for your machine learning initiatives. AWS Machine Learning Competency Partners have demonstrated relevant expertise and offer a range of services and technologies to help you create intelligent solutions for your business, from enabling data science workflows to enhancing applications with AI services. Learn more at aws.ai. About Amazon Web Services AWS offers the broadest and deepest set of machine learning and Al services. On behalf of our customers, we are focused on solving some of the toughest challenges that hold back machine learning from being in the hands of every developer. Tens of thousands of customers are already using AWS for their machine learning efforts. You can choose from fully managed Al services for computer vision, language, recommendations, forecasting, fraud detection, and search; or Amazon SageMaker to quickly build, train, and deploy machine learning models at scale. SageMaker Studio offers the first fully integrated development environ- ment for machine learning. You can also build custom models with support for all of the popular open-source frameworks. Our capabilities are built on the most comprehensive cloud platform, optimized for machine learning with high-performance computing and no compromises on security and analytics. Learn more at aws.ai. MIT SMR CONNECTIONS MANAGER'S GUIDE WHAT LE ADERS MU ST KNOW ABOUT DATA FOR MACHINE LE ARNING

• Cover