Zonke iinkqubo zibonisa xa kufinyelela kwinqanaba elifanelekileyo. I-traffic ifumaneka ngempumelelo, iingxaki zikhuselwa, kwaye izixazululo ze-design ezininzi ziye ziye ziye ziye ziye ziye zibonise yonke into elandelayo. Inqaku le nqaku lithembisa i-engineering ye-Sai Sreenivas Kodur, ukusuka ukwakha iinkqubo ze-recommendation e-e-commerce ukuya kwiiplatforms ze-AI ze-enterprise kunye neemveliso ze-domain. Kwixesha elandelayo, ibonisa indlela yokusebenza kwi-scale ukutshintshwa kweengcali ukusuka kwi-components eyodwa ukuya kwi-platform foundations, iinkqubo zokusebenza zebhanki kunye ne-team structures, ikakhulukazi njengoko i-AI ibonisa indlela yokwakha i-software. Izifundo ezidlulileyo kwi-systems kunye ne-machine learning U-Sai Sreenivas Kodur wabelane kunye ne-bachelor kunye ne-master kwi-Computer Science and Engineering kwi-Indian Institute of Technology, i-Madras. Kwimfundo yobugcisa kunye nemfundo yobugcisa, uye wabelane kwi-compilers kunye ne-machine learning. I-research yobugcisa njani iimfuno ze-machine learning ingasetyenziselwa ukuphucula ukusebenza kwe-software kwiinkalo ze-hardware ezininzi. Ukusetyenziswa kwiziphumo ze-system-level eyenziwe ngama-algorithms, iimodeli zokusebenza, kunye neengxaki ze-hardware ezisebenzayo kunye. Iziphumo ezincinane ze-implementation ziyafumaneka iziphumo ezininzi ezantsi. Ukususela ekugqibeleni kwimfundo, imiphumo enzima kakhulu le nqakraza ayikho ukufumana izixhobo ezithile, kodwa amandla ukufundisa iinkqubo ezintsha ngokukhawuleza kunye nokuguqulwa kwimeko ezintsha zobuchwepheshe. Iinkqubo ze-search kunye ne-recommendation kwi-scale Iimpawu zokuqala ze-industry ze-Sai zihlanganisa ukwakha kunye nokufunda iinkqubo ze-search kunye ne-recommendation kwiiplatforms ezinkulu ze-e-commerce e-India, kuquka i-Myntra kunye ne-Zomato. Zonke iinkqubo ziquka i-indexing, ukufumana, kunye nokufaka kwi-catalogue yeeminyaka engaphezu kwama-1 million ezintsha ngokushesha. Zifumaneka i-300,000 iimfuno ngenyanga ngamnye. Kule ububanzi, ubungakanani lwekhompyutha yibandakanya izinzuzo ezininzi ezihambelana. Ukukhutshwa kwe-indice kufuneka yibhanjiswe kwiimfuneko ye-latency. Umgangatho we-ranking kuxhomekeke kwi-data pipelines, i-infrastructure reliability, kunye ne-model behavior ezihambelana. Iingxaki ezininzi zikafumaneka kuphela emva kokusetyenziswa. Iingxaki ze-design ezinokuthi zibonakalisa ngokufanelekileyo ngexesha lokusetyenziswa kwezimpendulo ze-traffic, iingxaki ze-delay, kunye nokuhlanganiswa kwe-load. Ukuphuculwa kwe-relevance okanye ukusebenza kubandakanya ukuxhaswa kwinkqubo ezidlulileyo, ukusetyenziswa kwedatha, kunye nenkqubo yokusetyenziswa, ngaphandle kokuguqulwa kwezinye iingxaki. I-Startup Environments kunye ne-Engineering Exposure Kwixesha lokuqala yayo yobugcisa, i-Sai wahlala ukuba isebenze ngokufanelekileyo kwi-startup environments. Zifundo ziye zithunyelwe kwiintlobo ezininzi zeengcali, kubandakanya i-system design, i-production operations, kunye nokuxhomekeke kwimeko yeemveliso kunye neemveliso. Iziqinisekiso ze-tech zihlanganisa ngokukhawuleza iimfuno zeengcali kunye neengxaki zokusebenza. Kwiimeko ezininzi, imiphumo yeemibuzo yokuzonwabisa ngokushesha. Iinkqubo ezinzima ezincinane ziquka iinkqubo ezininzi njengoko ukusetyenziswa kwandisa. Iinkqubo ezisekelwe nge-abstractions ezifanelekileyo kunye ne-pipelines ezininzi ezininzi zithunyelwe ngexesha elide. Ukusetyenziswa kwimeko, iinkqubo ziye zihlanganiswa kuphela yi-code kunye ne-infrastructure, kodwa nangokuthi iinkqubo zokusebenza, indlela izixazululo ziye zithunyelwe, kunye neendlela ezihlangene kwi-platforms xa ziye zithunyelwa. Ukwakha i-Food Intelligence Systems kwi-Spoonshot Sai emva kokufunda i-Spoonshot yaye iye i-Chief Technology Officer yayo. I-Foodbrain yinkqubo yokuqala, i-Foodbrain, ifumanise i-100 terabytes yeendaba ezahlukileyo ezisuka kwi-30,000 iilwimi kunye neemodeli ze-AI kunye nezifundo ze-food ezinxulumene ne-domain. Le ngqungquthela lithunyelwe i-Genesis, iimveliso esetyenziselwa iimveliso ze-food ehlabathini ezifana ne-PepsiCo, i-Coca-Cola, kunye ne-Heinz ekusetyenziseni izixazululo ze-innovation kunye nokuthuthukiswa kwimveliso. Ukwakhiwa kwe-Foodbrain kubandakanya ukusebenza kunye neengxaki ze-data, iimfuno ze-domain ezintsha, kunye neengxaki ze-enterprise ye-reliability. I-system kufuneka ifumaneke kwi-inputs ezintsha ngaphandle kokuguqulwa kwe-architectural ezininzi. Ngexesha lokuzikhethela kwezobugcisa ze-Sai, i-Spoonshot ibonise i-$4 million ye- venture funding kwaye ifumaneka kwi-team ye-50 e-USA kunye ne-India. Kwixesha leyo, wabelane iinkqubo ze-AI ezikhuselekileyo zeendatha ngokwenza umsebenzi wokwenza iinkqubo ze-data kunye neqela le-Data Science. Oku kunceda ixesha lokugqibela lokuphuhlisa iimodeli ezintsha ngama-60%, kwaye ukugcina i-accuracy engaphezulu kwe-90%. I-Enterprise AI Platforms kunye ne-Reliability U-Sai uye usebenza njenge-Director of Engineering kwi-ObserveAI, apho uqhuba i-platform engineering, i-analytics, kunye ne-enterprise product teams. The platform supported enterprise customers such as DoorDash, Uber, Swiggy, and Asurion. These customers had strict expectations around reliability, performance, and operational visibility. Ukuphakamisa i-platform ukunceda ukwandisa ukusetyenziswa kwizigidi ezisetyenziswa kwimveliso, i-data ingestion pipelines, kunye ne-observability practices. Lezi zophando zithunyelwe kwi-$15 million yemali eyongezelelweyo yonyaka. Ukongezelela kwe-technical scaling, i-Sai ilungele ukwakha umthamo yokuzonwabisa kwezobugcisa. I-Sai yandisa ukucacisa iinkqubo zokusebenza, i-interviews ezininzi ze-130, kunye neengcali zeengcali ezininzi zokusetyenziswa ukunceda ukuvelisa i-platform kwi-long term. Ukubonisa njani isakhiwo se-organizational ikhawuleza imiphumo ye-system. Njengoko i-platform iye yandisa, iinkqubo ze-coordination, i-ownership, kunye ne-decision-making ziye kubonakala kwinkqubo ye-technical. Ukusuka kwi-System Engineering ukuya kwi-AI-Native Teams Across roles, Sai maintained hands-on involvement while gradually expanding into broader technical leadership responsibilities. Ukucaciswa kwayo kwakhona kubandakanyeka kwi-platform foundation kunye ne-workflows eyenza amaqela zokusebenza ngempumelelo kunye ne-data kunye ne-AI systems. I-Mentorship yeengcali eziphambili kunye ne-investment kwi-abstractions ezifanelekileyo ziye ziye ziye ziye zihlanganisa kwinkqubo ezininzi. Iimpawu zophando zayo zibonisa lo mfuneko esebenzayo. Iimpawu ezifana " yaye » Zifumaneka njani i-AI inokufumana iimveliso kunye ne-engineering workflows. Genesis: Intelligence Innovation Ukutya" I-Debugmate: i-AI agent ye-on-call debugging ephumelelayo kwi-complex production systems" I-Debugmate ibonise ukunciphisa i-77% ye-on-call load ngokunceda abathengi nge-incident trial usebenzisa idatha ye-observability kunye ne-system context. Izifundo ze-engineering ye-long-term Ukubuyekeza kwimeko yeSai Sreenivas Kodur, i-theme epheleleyo i-empawu ze-building ezininzi ezininzi ezininzi ezininzi ezininzi. Njengoko i-AI isebenze ukuthuthukiswa kwe-software, le nqakraza kubaluleke kakhulu, ikakhulukazi xa amaqela ziqala ukwakha ngokwenene. Ngaphandle kokufaka i-AI kwi-architectures ezidlulileyo. I-AI agents zithintela iintlobo ezintsha zokusebenza kunye neempawu ezahlukeneyo zokusetyenziswa kwinkqubo. I-Data kunye ne-Infrastructure platform eyenziwe ngexesha lokuqala kubasebenzisi abadumile kufuneka zihlanganise ukuxhaswa kwezi zixazululo. AI-native software teams I-AI-native software iinkonzo Ngaphandle kokuphumelela kwimveliso ye-productivity ye-individual, le nkqubo yenzelwe kwi-platform foundations, i-data workflows, kunye ne-team structures, ezikhuphumelela ngexesha elide. I-Career ibonisa indlela yobuchwepheshe eyenziwe ngqongqo, ukuzinza kunye ne-impact ye-long-term. Ukucaciswa kwe-Imunify-Imunify ye-Linkedin Ukucaciswa kwe-Imunity - I-Imagine Ukucinga Ukucinga Ukucinga

This writer has a vested interest be it monetary, business, or otherwise, with 1 or more of the products or companies mentioned within.

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Ubuchwepheshe kwi-Scale: ukusuka kwi-Search Systems ukuya kwi-AI-Native Platforms kunye ne-Data Products

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