Kuyini i-AI ku-Cloud Computing? [Ividiyo Nombuzo]

Impendulo emfushane: I-AI ku-cloud computing imayelana nokusebenzisa amapulatifomu efu ukugcina idatha, ukuqasha ama-computing, ukuqeqesha amamodeli, ukuwasebenzisa njengezinsizakalo, nokuwagcina eqashwe ekukhiqizeni. Kubalulekile ngoba ukwehluleka okuningi kuhlangana nedatha, ukuthunyelwa, kanye nokusebenza, hhayi izibalo. Uma udinga ukukala okusheshayo noma ukukhishwa okuphindaphindwayo, i-cloud + MLOps iyindlela ewusizo.

Izinto ezibalulekile okufanele uzicabangele:

Umjikelezo Wokuphila: Lahla idatha, yakha izici, uqeqeshe, usakaze, bese uqapha ukuzulazula, ukubambezeleka, kanye nezindleko.

Ukubusa: Yakha izilawuli zokufinyelela, izingodo zokuhlola, kanye nokuhlukaniswa kwemvelo kusukela ekuqaleni.

Ukuphindaphindwa: Izinguqulo zedatha yokurekhoda, ikhodi, amapharamitha, kanye nezindawo ukuze ukusebenza kuhlale kuphindaphindwa.

Ukulawula izindleko: Sebenzisa i-batching, i-caching, i-autoscaling caps, kanye nokuqeqeshwa okuqondile/okungafanele kusetshenziswe ukuze ugweme ukushaqeka kwezikweletu.

Amaphethini okusetshenziswa: Khetha amapulatifomu aphethwe, imisebenzi ye-lakehouse, ama-Kubernetes, noma i-RAG ngokusekelwe eqinisweni leqembu.

Izihloko ongase uthande ukuzifunda ngemva kwalesi:

🔗 Amathuluzi okuphatha ibhizinisi aphezulu efwini le-AI
Qhathanisa amapulatifomu efu ahamba phambili aqondisa ukusebenza, ezezimali, kanye namaqembu.

🔗 Kudingeka ubuchwepheshe bokukhiqiza i-AI enkulu
Ingqalasizinda ebalulekile, idatha, kanye nokuphatha kuyadingeka ukuze kusetshenziswe i-GenAI.

🔗 Amathuluzi e-AI wamahhala okuhlaziya idatha
Izixazululo ze-AI ezingcono kakhulu zamahhala zokuhlanza, ukwenza imodeli, nokubona ngeso lengqondo amasethi edatha.

🔗 Iyini i-AI njengesevisi?
Kuchaza i-AIaaS, izinzuzo, amamodeli entengo, kanye namacala okusetshenziswa kwebhizinisi avamile.

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I-AI ku-Cloud Computing: Incazelo Elula 🧠☁️

Empeleni, i-AI ku-cloud computing isho ukusebenzisa amapulatifomu amafu ukufinyelela:

• Amandla okubala (ama-CPU, ama-GPU, ama-TPU) I-Google Cloud: Ama-GPU e-AI Cloud TPU docs

• Indawo yokugcina idatha (amachibi edatha, izindawo zokugcina impahla, indawo yokugcina izinto) I-AWS: Iyini ichibi ledatha? I-AWS: Iyini indawo yokugcina idatha? I-Amazon S3 (indawo yokugcina izinto)

• Izinsizakalo ze-AI (ukuqeqeshwa kwemodeli, ukuthunyelwa, ama-API ombono, inkulumo, i-NLP) Izinsizakalo ze-AWS ze-AI Ama-API e-Google Cloud AI

• Ukufakwa kwamathuluzi e-MLOps (amapayipi, ukuqapha, ukubhaliswa kwamamodeli, i-CI-CD ye-ML) I-Google Cloud: Iyini i-MLOps? I-Vertex AI Model Registry

Esikhundleni sokuthenga ihadiwe yakho ebizayo, uqasha okudingayo, uma ukudinga NIST SP 800-145. Njengokuqasha ijimu ukuze uzivocavoce kakhulu esikhundleni sokwakha ijimu egaraji lakho bese ungaphinde usebenzise i-treadmill. Kwenzeka kithina esingcono kakhulu 😬

Ngokusobala: yi-AI ekala, ithumela, ibuyekeze, futhi isebenze ngengqalasizinda yamafu i-NIST SP 800-145.

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Kungani i-AI + i-Cloud Kuyindaba Enkulu Kangaka 🚀

Masikhulume iqiniso - amaphrojekthi amaningi e-AI awaphumeleli ngoba izibalo zinzima. Ayahluleka ngoba "izinto ezizungeze imodeli" ziyaphambana:

• idatha ihlakazekile

• izindawo azifani

• imodeli isebenza kwi-laptop yomunye umuntu kodwa ayikho enye indawo

• ukuthunyelwa kuphathwa njengokucabanga kwangemva kwalokho

• ezokuphepha kanye nokuthobela imithetho zifika sekwephuzile njengomzala ongamenywanga 😵

Amapulatifomu efu ayasiza ngoba anikeza:

1) Isikali sokunwebeka 📈

Qeqesha imodeli eqenjini elikhulu isikhashana, bese uyivala i- NIST SP 800-145.

2) Ukuhlola okusheshayo ⚡

Shintsha ama-notebook aphethwe, amapayipi akhiwe ngaphambilini, kanye nezimo ze-GPU ngokushesha I-Google Cloud: Ama-GPU e-AI.

3) Ukufakwa okulula 🌍

Sebenzisa amamodeli njengama-API, imisebenzi ye-batch, noma izinsizakalo ezifakiwe I-Red Hat: Iyini i-REST API? I-SageMaker Batch Transform.

4) Izindawo zemvelo zedatha ezihlanganisiwe 🧺

Amapayipi akho edatha, izindawo zokugcina impahla, kanye nokuhlaziya kuvame ukuba khona kakade efwini. I-AWS: Indawo yokugcina idatha vs ichibi ledatha.

5) Ukubambisana kanye nokuphatha 🧩

Izimvume, amalogi okuhlola, ukwenziwa kwenguqulo, kanye namathuluzi okwabelwana ngawo kubhakwa (ngezinye izikhathi kuyabuhlungu, kodwa noma kunjalo) ku- Azure ML registries (MLOps).

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Indlela i-AI esebenza ngayo ku-Cloud Computing ngokuzijwayeza (Ukugeleza Kwangempela) 🔁

Nansi indlela yokuphila evamile. Akuyona inguqulo "yedayagramu ephelele" ... leyo ehlala ngaphakathi.

Isinyathelo 1: Idatha ifika kwisitoreji samafu 🪣

Izibonelo: amabhakede okugcina izinto, amachibi edatha, izizindalwazi zamafu i- Amazon S3 (ukugcina izinto) i-AWS: Iyini ichibi ledatha? Ukubuka konke kwe-Google Cloud Storage.

Isinyathelo 2: Ukucubungula idatha + ukwakha izici 🍳

Uyayihlanza, uyishintshe, udale izici, mhlawumbe uyisakaze.

Isinyathelo 3: Ukuqeqeshwa kwemodeli 🏋️

Usebenzisa i-cloud compute (ngokuvamile ama-GPU) ukuqeqesha i-Google Cloud: ama-GPU e-AI:

• amamodeli e-ML akudala

• amamodeli okufunda okujulile

• ukushuna okuhle kwemodeli yesisekelo

• izinhlelo zokubuyisa (ukusethwa kwesitayela se-RAG) Iphepha Lokukhiqizwa Okwengeziwe Kokubuyiswa (i-RAG)

Isinyathelo 4: Ukuthunyelwa 🚢

Amamodeli ayapakishwa futhi akhonzwa nge:

• Ama-REST APIs I-Red Hat: Iyini i-REST API?

• ama-endpoint angenaseva i-SageMaker I-Serverless Inference

• Iziqukathi ze-Kubernetes I-Kubernetes: I-Horizontal Pod Autoscaling

• amapayipi okucabanga kwe-batch SageMaker Batch Transform Vertex AI batch izibikezelo

Isinyathelo 5: Ukuqapha + izibuyekezo 👀

Ithrekhi:

• ukubambezeleka

• i- SageMaker Model Monitor yokunemba

• Ukuqapha Imodeli ye-AI ye-Vertex drift

• izindleko ngokubikezela ngakunye

• amacala asemaphethelweni akwenza uhlebe uthi “lokhu akufanele kwenzeke…” 😭

Yileyo injini. Yileyo i-AI ku-Cloud Computing esebenza, hhayi nje njengencazelo.

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Yini Eyenza Inguqulo Enhle Ye-AI Ku-Cloud Computing? ✅☁️🤖

Uma ufuna ukusetshenziswa "okuhle" (hhayi nje idemo ekhangayo), gxila kulokhu:

A) Ukuhlukaniswa okucacile kwezinkinga 🧱

• ungqimba lwedatha (isitoreji, ukubusa)

• ungqimba lokuqeqesha (izivivinyo, amapayipi)

• isendlalelo sokukhonza (ama-API, ukukala)

• isendlalelo sokuqapha (amamethrikhi, amalogi, izexwayiso) I-SageMaker Model Monitor

Uma konke kuhlanganiswe ndawonye, ​​ukulungisa amaphutha kuba umonakalo ongokomzwelo.

B) Ukuphinda kukhiqizwe ngokuzenzakalelayo 🧪

Uhlelo oluhle lukuvumela ukuthi uthi, ngaphandle kokuqhweba ngesandla:

• idatha eqeqeshe lo modeli

• inguqulo yekhodi

• ama-hyperparameter

• imvelo

Uma impendulo ithi “uhh, ngicabanga ukuthi bekuyi-Lwesibili lokugijima…” usuvele usenkingeni 😅

C) Umklamo oqaphela izindleko 💸

I-Cloud AI inamandla, kodwa futhi iyindlela elula yokwenza ngephutha umthethosivivinywa okwenza uzibuze ngezinketho zakho zokuphila.

Izilungiselelo ezinhle zihlanganisa:

• ukukala ngokuzenzakalela kwe- Kubernetes: I-Pod Evundlile Ukukala ngokuzenzakalela kwe-Pod

• ukuhlela izibonelo

• izinketho ezingaba khona uma kungenzeka i- Amazon EC2 Spot Instances Google Cloud Preemptible VMs

• ukugcinwa kwesikhashana kanye nokuphetha nge- batch SageMaker Batch Transform

D) Ukuphepha nokuthobela imithetho kufakwe ku-🔐

Akuboshelwanga kamuva njengetheyiphu yepayipi evuzayo.

E) Indlela yangempela kusukela kumodeli wokuqala kuya ekukhiqizweni 🛣️

Lena enkulu. "Inguqulo" enhle ye-AI efwini ifaka phakathi ama-MLOp, amaphethini okusetshenziswa, kanye nokuqapha kusukela ekuqaleni i-Google Cloud: Iyini i-MLOps?. Ngaphandle kwalokho kuyiphrojekthi yesayensi enombhalo omuhle.

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Ithebula Lokuqhathanisa: Izinketho Ezidumile Ze-AI-in-Cloud (Nokuthi Zingobani) 🧰📊

Ngezansi kunethebula elisheshayo, elinemibono eminingana. Amanani abanzi ngamabomu ngoba amanani efu afana noku-oda ikhofi - intengo eyisisekelo ayiyona intengo 😵💫

Ithuluzi / Ipulatifomu	Izithameli	Intengo-ngokufanayo	Kungani kusebenza (kufaka phakathi amanothi angavamile)
I-AWS SageMaker	Amaqembu e-ML, amabhizinisi	Khokha njengoba uhamba	Ipulatifomu ye-ML egcwele i-stack - ukuqeqeshwa, ama-endpoints, amapayipi. Inamandla, kodwa amamenyu yonke indawo.
I-Google Vertex AI	Amaqembu e-ML, izinhlangano zesayensi yedatha	Khokha njengoba uhamba	Ukuqeqeshwa okuphethwe kahle + ukubhaliswa kwamamodeli + ukuhlanganiswa. Kuzwakala kubushelelezi uma kuchofozwa.
Ukufunda Komshini We-Azure	Amabhizinisi, izinhlangano ezigxile ku-MS	Khokha njengoba uhamba	Idlala kahle ne-Azure ecosystem. Izinketho ezinhle zokuphatha, izinkinobho eziningi.
Ama-Databricks (ML + Lakehouse)	Amaqembu aqinile obunjiniyela bedatha	Okubhaliselwe + ukusetshenziswa	Kuhle kakhulu ekuxubeni amapayipi edatha + i-ML endaweni eyodwa. Ngokuvamile kuyathandwa amaqembu asebenzayo.
Izici ze-AI ze-Snowflake	Izinhlangano zokuqala ze-Analytics	Kusekelwe ekusetshenzisweni	Kuhle uma umhlaba wakho usuvele usesitolo. Kuncane “i-ML lab,” kuncane “i-AI ku-SQL-ish.”
I-IBM watsonx	Izimboni ezilawulwayo	Amanani ebhizinisi	Ukuphatha kanye nokulawula amabhizinisi kuyizinto ezigxilwe kakhulu. Ngokuvamile kukhethwa ukusethwa okunezinqubomgomo eziningi.
Ama-Kubernetes aphethwe (i-DIY ML)	Onjiniyela beplatifomu	Okuguquguqukayo	Kuguquguqukayo futhi kwenziwa ngokwezifiso. Futhi… ubuhlungu buhlala bukhona uma buphuka 🙃
Ukuphetha okungenaseva (imisebenzi + ama-endpoints)	Amaqembu omkhiqizo	Kusekelwe ekusetshenzisweni	Kuhle kakhulu uma kunethrafikhi ebukhali. Bukela ukuqala okubandayo kanye nokubambezeleka njengoklebe.

Lokhu akukhona ukukhetha “okungcono kakhulu” - kumayelana nokufanisa isimo seqembu lakho. Yileyo imfihlo ethule.

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Amacala Okusetshenziswa Okuvamile kwe-AI ku-Cloud Computing (Nezibonelo) 🧩✨

Nakhu lapho ukusethwa kwe-AI-in-cloud kugqama khona:

1) Ukwenziwa okuzenzakalelayo kokusekelwa kwamakhasimende 💬

• abasizi bengxoxo

• umzila wamathikithi

• ukufingqa

• I-Cloud Natural Language API yokuthola imizwa kanye nenhloso

2) Izinhlelo zokuncoma 🛒

• iziphakamiso zomkhiqizo

• okuphakelayo kokuqukethwe

• “abantu nabo bathenge”
  Lokhu kuvame ukudinga ukuqagela okunwebekayo kanye nokubuyekezwa okuseduze nesikhathi sangempela.

3) Ukutholwa kokukhwabanisa kanye nokuthola amaphuzu engcupheni 🕵️

I-Cloud yenza kube lula ukuphatha ukuqhuma, ukusakaza imicimbi, nokuqhuba amaqembu.

4) Ubuhlakani bedokhumenti 📄

• Amapayipi e-OCR

• ukukhishwa kwento

• ukuhlaziywa kwenkontileka

• Ukuhlaziya i-invoyisi Imisebenzi ye-Snowflake Cortex AI
  Kuma-org amaningi, yilapho isikhathi sibuyiselwa khona buthule.

5) Ukubikezela kanye nokwenza ngcono ikhono 📦

Ukubikezela izidingo, ukuhlela impahla, nokwenza ngcono imizila. Ifu liyasiza ngoba idatha inkulu futhi ukuqeqeshwa kabusha kuvame kakhulu.

6) Izinhlelo zokusebenza ze-AI ezikhiqizayo 🪄

• ukubhalwa kokuqukethwe

• usizo lwekhodi

• ama-bot olwazi lwangaphakathi (i-RAG)

• lokukhiqizwa kwedatha yokwenziwa kwe -Retrieval-Augmented Generation (RAG)
  Lesi yisikhathi lapho izinkampani zithi khona: “Sidinga ukwazi ukuthi imithetho yethu yokufinyelela idatha ihlala kuphi.” 😬

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Amaphethini Okwakha Ozowabona Yonke Indawo 🏗️

Iphethini 1: Ipulatifomu ye-ML ephethwe (indlela ethi “sifuna amakhanda ambalwa”) 😌

• layisha idatha

• qeqesha ngemisebenzi ephethwe

• thumela kuma-endpoints aphethwe

• ukuqapha kumadeshibhodi epulatifomu i-SageMaker Model Monitor I-Vertex AI Model Monitoring

Isebenza kahle uma isivinini sibalulekile futhi awufuni ukwakha amathuluzi angaphakathi kusukela ekuqaleni.

Iphethini 2: I-Lakehouse + ML (umzila "wokuqala ngedatha") 🏞️

• hlanganisa ubunjiniyela bedatha + imisebenzi ye-ML

• sebenzisa ama-notebook, amapayipi, ubunjiniyela bezici eduze kwedatha

• kuqinile kuma-org asevele ehlala ezinhlelweni ezinkulu zokuhlaziya i-Databricks Lakehouse

Iphethini 3: I-ML efakwe ebhodleleni ku-Kubernetes (umzila othi "sifuna ukulawula") 🎛️

• amamodeli ephakheji ezitsheni

• isikali ngezinqubomgomo zokukala ngokuzenzakalela I-Kubernetes: I-Pod Evundlile Ukukala ngokuzenzakalela

• hlanganisa i-service mesh, ukubonwa, izimfihlo mgmt

Okwaziwa nangokuthi: “Siyazethemba, futhi sithanda ukulungisa amaphutha ngezikhathi ezingajwayelekile.”

Iphethini 4: I-RAG (Ukuthola Okungeziwe) (indlela ethi “sebenzisa ulwazi lwakho”) 📚🤝

• amadokhumenti kusitoreji samafu

• ukushumeka + isitolo sevektha

• isendlalelo sokubuyisa sinikeza umongo kumodeli

• izivikelo zokuvikela + ukulawula ukufinyelela + ukuqopha iphepha le-Retrieval-Augmented Generation (RAG)

Lokhu kuyingxenye enkulu yezingxoxo zesimanje ze-AI-in-cloud ngoba yingakho amabhizinisi amaningi angempela esebenzisa i-AI ekhiqizayo ngokuphephile.

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Ama-MLOps: Ingxenye Wonke Umuntu Ayithatha Kabi 🧯

Uma ufuna i-AI efwini iziphathe kahle ekukhiqizeni, udinga ama-MLOp. Hhayi ngoba imfashini - ngoba amamodeli ayashintshashintsha, idatha iyashintsha, futhi abasebenzisi banobuhlakani ngendlela embi kakhulu. I -Google Cloud: Iyini i-MLOp?

Izingcezu ezibalulekile:

• Ukulandelela kokuhlola: yini esebenze, yini engazange isebenze Ukulandelela kwe-MLflow

• Ukubhaliswa kwemodeli: amamodeli avunyiwe, izinguqulo, imethadatha -MLflow Model Registry I-Vertex AI Model Registry

• I-CI-CD ye-ML: ukuhlola + ukwenza ngokuzenzakalelayo kokufakwa kwe -Google Cloud MLOps (i-CD kanye nokwenza ngokuzenzakalelayo)

• Isitolo sezici: izici ezihambisanayo phakathi kokuqeqeshwa kanye nokucabanga Isitolo sezici seSageMaker

• Ukuqapha: ukuzulazula kokusebenza, izimpawu zokukhetha, ukubambezeleka, izindleko -SageMaker Model Monitor I-Vertex AI Model Monitor

• Isu lokubuyisela emuva: yebo, njengesofthiwe evamile

Uma ungakunaki lokhu, uzogcina “une-model zoo” 🦓 lapho konke kuphila khona, kungekho lutho olubhalwe uphawu, futhi wesaba ukuvula isango.

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Ukuphepha, Ubumfihlo, kanye Nokuthobela Imithetho (Akuyona Ingxenye Ejabulisayo, Kodwa... Yebo) 🔐😅

I-AI ku-cloud computing iphakamisa imibuzo embalwa ebabayo:

Ukulawulwa kokufinyelela kwedatha 🧾

Ubani ongafinyelela idatha yokuqeqeshwa? Amalogi okucabanga? Izeluleko? Imiphumela?

Ukubethela kanye nezimfihlo 🗝️

Okhiye, amathokheni, kanye neziqinisekiso kudinga ukuphathwa ngendlela efanele. Ukuthi “Kufayela lokucushwa” akusebenzi.

Ukuzihlukanisa kanye nokuqasha 🧱

Amanye ama-org adinga izindawo ezihlukile zokuthuthukisa, ukukhiqiza, kanye nokukhiqiza. Ifu liyasiza - kodwa kuphela uma ulisetha kahle.

Ukuhlolwa Kwama-akhawunti 📋

Izinhlangano ezilawulwayo zivame ukudinga ukukhombisa:

• ukuthi iyiphi idatha esetshenzisiwe

• indlela izinqumo ezenziwe ngayo

• ubani owasebenzisa lokho

• lapho ishintsha i-IBM watsonx.governance

Ukuphathwa kwengozi yemodeli ⚠️

Lokhu kuhlanganisa:

• ukuhlolwa kokukhetha

• ukuhlolwa okuphikisayo

• ukuzivikela okusheshayo kokujova (kwe-AI yokukhiqiza)

• ukuhlunga okuphephile kokukhipha

Konke lokhu kubuyela emuva ephuzwini: akuyona nje "i-AI ebanjwe ku-inthanethi." Isebenza ngaphansi kwemingcele yangempela.

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Amathiphu Ezindleko Nokusebenza (Ukuze Ungakhali Kamuva) 💸😵💫

Amathiphu ambalwa avivinywe yimpi:

• Sebenzisa imodeli encane kakhulu ehlangabezana nesidingo.
  Okukhulu akuhlali kungcono. Ngezinye izikhathi kumane nje... kukhulu.

• Ukuqagela kweqembu uma kungenzeka
  eshibhile futhi esebenza kahle kakhulu I-Batch Transform.

• I-Cache ngobudlova
  Ikakhulukazi yemibuzo ephindaphindwayo kanye nokushumeka.

• Ukukala okuzenzakalelayo, kodwa vala
  Ukukala okungenamkhawulo kungasho ukuchitha imali okungenamkhawulo I-Kubernetes: I-Pod Evundlile Ukukala okuzenzakalelayo. Ngibuze ukuthi ngazi kanjani… empeleni, ungakwenzi 😬

• Landelela izindleko nge-endpoint ngayinye kanye nesici ngasinye
  Ngaphandle kwalokho uzokwenza ngcono into engalungile.

• Sebenzisa ukubala okungabonakali kahle ekuqeqesheni
  Ukonga okuhle uma imisebenzi yakho yokuqeqesha ingaphatha ukuphazamiseka I-Amazon EC2 Spot Instances I-Google Cloud Preempible VMs.

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Amaphutha Abantu Abawenzayo (Ngisho Namaqembu Ahlakaniphile) 🤦♂️

• Ukuphatha i-AI yamafu njengokuthi "vele uxhume imodeli"

• Ukunganaki ikhwalithi yedatha kuze kube umzuzu wokugcina

• Ukuthumela imodeli ngaphandle kokuqapha I-SageMaker Model Monitor

• Angihleli ukuqeqesha kabusha i-cadence I-Google Cloud: Iyini i-MLOps?

• Ukukhohlwa ukuthi amaqembu okuphepha akhona kuze kube yisonto lokwethulwa 😬

• Ubunjiniyela obudlulele kusukela osukwini lokuqala (ngezinye izikhathi ukuphumelela okulula kwesisekelo)

Futhi, okunonya kancane: amaqembu awanaki ukuthi abasebenzisi bayakwenyanya kangakanani ukubambezeleka. Imodeli enganembile kancane kodwa esheshayo ivame ukunqoba. Abantu abanaso isineke sezimangaliso ezincane.

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Izinto Ezibalulekile Okufanele Uzicabangele 🧾✅

I-AI ku-Cloud Computing iwumkhuba ophelele wokwakha nokusebenzisa i-AI kusetshenziswa ingqalasizinda yamafu - ukukhuliswa kokuqeqeshwa, ukwenza lula ukuthunyelwa, ukuhlanganisa amapayipi edatha, kanye nokusebenzisa amamodeli nge-MLOps, ukuphepha, kanye nokuphatha i-Google Cloud: Iyini i-MLOps? I- NIST SP 800-145.

Isifinyezo esisheshayo:

• I-Cloud inikeza i-AI ingqalasizinda yokukhulisa nokuthumela 🚀 I-NIST SP 800-145

• I-AI inikeza "ubuchopho" bomthwalo wemisebenzi yamafu ozenzakalela izinqumo 🤖

• Umlingo awugcini nje ngokuqeqesha - uwukuthunyelwa, ukuqapha, kanye nokuphatha 🧠🔐 I-SageMaker Model Monitor

• Khetha amapulatifomu ngokusekelwe ezidingweni zeqembu, hhayi ekukhangiseni okungenangqondo 📌

• Izindleko zokubuka kanye nezenzo njengokhozi olugqoke izibuko 🦅👓 (isifaniso esibi, kodwa uyasiqonda)

Uma ufike lapha ucabanga ukuthi “i-AI kuma-cloud computing iyimodeli nje ye-API,” cha - kuyi-ecosystem yonke. Ngezinye izikhathi inobuhle, ngezinye izikhathi iyaxaka, ngezinye izikhathi zombili ntambama efanayo.

Isibonelo sangempela: Ukwakha umsizi wokusekela we-AI yamafu kanye nohlelo lokuhlola amathikithi 🎫☁️

Isimo

Cabanga ngenkampani ye-SaaS yabantu abangu-40 ethola amathikithi okusekela amakhasimende angaba ngu-180 ngesonto. Ithimba lokusekela lisebenzisa ithuluzi le-helpdesk, kodwa njalo ngoMsombuluko ekuseni othile kusadingeka afunde amathikithi amasha, anqume isigaba, abeke ukuphuthuma, ahlole ukuthi ikhasimende lisehlelweni olukhokhelwayo yini, bese edlulisela inkinga ekukhokheni, emkhiqizweni, ebunjiniyelani, noma ekusekelweni okuvamile.

Le nkampani ayidingi uhlelo olukhulu lwe-AI. Idinga uhlelo lokusebenza oluncane lwe-AI lwamafu olungahlukanisa amathikithi, lufingqe inkinga, luphakamise isinyathelo esilandelayo, futhi luveze amacala ayingozi ukuze abuyekezwe ngabantu.

Ukusetha okusebenzayo kungabonakala kanje:

amathikithi athunyelwa kwisitoreji samafu njalo ngehora

umsebenzi ongenaseva uhlanza umbhalo wethikithi futhi ususa idatha yomuntu siqu engadingekile

imodeli yokuhlela noma imodeli yolimi olusingathiwe ilebula ithikithi

imiphumela ibhalwa emuva ohlelweni lwe-helpdesk

ideshibhodi ilandelela ukubambezeleka, amaphuzu okuzethemba, ukunemba komzila, kanye nezindleko ngethikithi ngalinye

Iphuzu elibalulekile: i-AI ayithathi indawo yethimba lokusekela. Inciphisa umsebenzi wokuhlunga ophindaphindwayo ukuze abantu bachithe isikhathi esiningi bexazulula inkinga yangempela.

Lokho okudingwa umsizi

Ukuze lokhu kusebenze kahle, ithimba kufanele lilungiselele:

uhlu lwezigaba zamathikithi, njenge-Billing, Login, Bug, Feature Request, Khansela, Security, kanye ne-General

izibonelo zamathikithi angempela angu-20-50 ngesigaba ngasinye

imithetho yokuqondisa yomnyango ngamunye

imithetho ebaluleke kakhulu, njengokuthi “inkinga yokuphepha = okuphuthumayo” noma “ukuphela kwamakhasimende ebhizinisi = okuphuthumayo”

uhlu olufushane lwezinto umsizi okungafanele azenze, njengokuthembisa ukubuyiselwa kwemali, ukuvuma iphutha elisemthethweni, noma ukushintsha izilungiselelo ze-akhawunti

izilawuli zokufinyelela ukuze ukuhamba komsebenzi we-AI kubone kuphela izinkambu zamathikithi ezidingayo ngempela

umthetho wokubuyela emuva kwamacala angaqinisekile

Umthetho olula wokubuyela emuva ungaba:

Uma ukuzethemba kungaphansi kuka-80%, noma ithikithi likhuluma ngezomthetho, ezokuphepha, ukubuyiselwa kwemali, ukukhansela, ukwephulwa kwedatha, noma ukulimala kwezokwelapha/kwezezimali, lithumele kumbuyekezi ongumuntu esikhundleni sokulihambisa ngokuzenzakalelayo.

Isibonelo semiyalelo

Ungumsizi wokusiza amathikithi okusiza enkampanini ye-B2B SaaS.

Funda umlayezo wekhasimende bese uwubuyisela:

1. Isifinyezo somusho owodwa salolu daba

2. Isigaba esisodwa kulolu hlu: Ukukhokhisa, Ukungena ngemvume, Isiphazamisi, Isicelo Sesici, Ukukhansela, Ukuphepha, Okuvamile

3. Okubalulekile: Okuphansi, Okuphakathi, Okuphezulu, noma Okuphuthumayo

4. Ithimba elingcono kakhulu lokubhekana nalo: Ukusekela, Ukukhokhisa, Umkhiqizo, Ubunjiniyela, Ukuphepha, noma Impumelelo Yamakhasimende

5. Kungakhathaliseki ukuthi kudingeka isibuyekezo somuntu: Yebo noma Cha

6. Isizathu esifushane sesinqumo sakho

Imithetho:

Ungathembisi ukubuyiselwa kwemali.
Ungahlonzi umthwalo wemfanelo wezomthetho noma wezokuphepha.
Ungaqambi imininingwane ye-akhawunti.
Uma umlayezo ungacacile, khetha okuthi Okuvamile bese ucela ukubuyekezwa komuntu.
Uma ikhasimende likhuluma ngokudalulwa kwedatha, ukuthathwa kwe-akhawunti, ukwehluleka kokukhokha, noma ukuvalwa kwesevisi, kudinga ukubuyekezwa komuntu.

Indlela yokuyihlola

Ngaphambi kokufaka lokhu ekukhiqizweni, kuvivinye ngesethi encane yamathikithi omlando angempela noma angadalulwanga.

Sebenzisa amathikithi ayi-100 edlule bese uqhathanisa indlela yomsizi nesinqumo sokuqala sethimba sokuqondisa.

Isheke:

zingaki izigaba ezifanelana nelebula lomuntu

mangaki amathikithi aphuthumayo anyuswe ngendlela efanele

Mangaki amathikithi angabalulekile aphawulwe ngephutha ngokuthi ayaphuthuma

ukuthi amathikithi abucayi athunyelwe yini ekubuyekezweni komuntu

isilinganiso sesikhathi sokucubungula ngethikithi ngalinye

izindleko ngamathikithi ayi-100

Bese uqhuba isivivinyo sesibili ngezibonelo ezingahlelekile:

ikhasimende libhala ngamagama amakhulu

ithikithi liqukethe izinkinga ezintathu ngesikhathi esisodwa

umyalezo ungamagama amabili kuphela, njengokuthi “angikwazi ukungena ngemvume”

umsebenzisi ucela ukubuyiselwa imali futhi usongela ngokuthatha isinyathelo somthetho

ikhasimende libika isigameko sokuphepha esingaba khona

Lezi zivivinyo zibalulekile ngoba amathikithi e-demo ahlanzekile alula. Abasebenzisi bangempela babhala ngokungahlelekile, umongo ongacacile, kanye nezimpawu zokubhala ezingalindelekile.

Umphumela

Umphumela obonisayo: ngokusekelwe ekubekeni isikhathi kwesampula yokuhlunga imisebenzi emihlanu ngesandla ngaphambi nangemva kokusebenzisa lo msebenzi.

Inqubo eyenziwa ngesandla:

Amathikithi ayi-180 ngesonto
Isikhathi esimaphakathi sokuhlola ngesandla: imizuzu emi-2 imizuzwana engama-30 ithikithi ngalinye
Isikhathi esiphelele sokuhlola: imizuzu engama-450 ngesonto, noma amahora angu-7.5

Inqubo esizwa yi-AI yamafu:

Isikhathi esimaphakathi sokucubungula i-AI: ngaphansi kwemizuzwana eyi-10 ngethikithi ngalinye
Isikhathi esimaphakathi sokubuyekezwa komuntu kwamathikithi afakwe ifulegi: umzuzu o-1 nemizuzwana engama-30
Izinga lokubuyekezwa komuntu: 25% wamathikithi
Isikhathi esilinganiselwe sokuhlolwa kwamasonto onke: imizuzu engama-67.5

Lokho kunikeza ukonga okulinganiselwa kumahora angu-6.4 ngesonto.

Ukunemba kufanele kulinganiswe ngokwehlukana. Esivivinyweni esingokoqobo, ithimba lingase libeke umthetho wokuqalisa ofana nalokhu:

okungenani isigaba esingu-90% sifana namalebula abantu

Amathikithi ahlobene nokuphepha ayi-100% athunyelwe ekubuyekezweni komuntu

amathikithi angaphansi kuka-5% athuthelwe emnyangweni ongalungile

isilinganiso sezindleko esingaphansi kuka-£0.05 ngethikithi ngalinye

Uma umsizi engahlangabezani nalezo zinombolo kusethi yokuhlola, kufanele ahlale kwimodi yokubuyekeza kunokuba athumele amathikithi abukhoma ngokuzenzakalela.

Yini engase ihambe kabi

Ukwehluleka okuvame kakhulu yizigaba ezingacacile. Uma “Iphutha”, “Inkinga Yobuchwepheshe”, kanye “Nenkinga Yomkhiqizo” konke kusho into efanayo, umsizi uzohlukanisa ngokungaguquki.

Enye ingozi ukwenza izinto ngokuzenzakalela ngokweqile. Ithikithi mayelana nokuthi “i-akhawunti yami ifinyelelwe ngomunye umuntu” akufanele lidluliselwe kalula njeyinkinga evamile yokungena ngemvume. Lidinga ukukhushulwa, ukulogwa, futhi mhlawumbe nomsebenzi wokuphepha.

Ukungena ngemvume okungalungile kungadala nezinkinga zobumfihlo. Imiyalelo, umbhalo wamathikithi, imiphumela yemodeli, kanye nokulandelwa kwamaphutha kungase kube nedatha yamakhasimende ebucayi. Gcina kuphela lokho okudingekayo, khawulela ukufinyelela, futhi usethe imithetho yokugcina.

Izindleko nazo zingakhuphuka kakhulu. Uma ithikithi ngalinye lithunyelwa kumodeli enkulu lapho i-classifier encane ingasebenza, uhlelo luba lubiza kakhulu ngokungadingekile. Qala ngenketho encane kakhulu ethembekile, bese uthuthukisa kuphela lapho ukunemba kuthuthukisa khona ngempela.

Ukudla okuwusizo

Ukusethwa okuhle kwe-AI yamafu kuqala kancane: ukuhamba komsebenzi okukodwa, imithetho ecacile, idatha yokuhlola, ukubuyekezwa kwabantu, kanye nezinhloso ezilinganiswayo. Ukuze uthole ukwesekwa kokuhlola, ukunqoba akusikho ukuthi "i-AI iphatha konke". Ukunqoba kuwukuhlunga okusheshayo, amathikithi ambalwa aphuthiwe aphuthumayo, ukunikezwa okuhlanzekile, kanye nohlelo iqembu elingaluqapha esikhundleni sokuluthemba ngokungazi.

Imibuzo Evame Ukubuzwa

Okushiwo "i-AI ku-cloud computing" ngokwezinto zansuku zonke

I-AI ekubaleni kwamafu isho ukuthi usebenzisa amapulatifomu amafu ukugcina idatha, ukujikeleza ama-computing (ama-CPU/ama-GPU/ama-TPU), ukuqeqesha amamodeli, ukuwasebenzisa, nokuwaqapha - ngaphandle kokuba nehadiwe. Empeleni, ifu liba yindawo lapho yonke impilo yakho ye-AI isebenza khona. Uqasha lokho okudingayo uma ukudinga, bese wehlisa uma usuqedile.

Kungani amaphrojekthi e-AI ehluleka ngaphandle kwengqalasizinda yesitayela samafu kanye nama-MLOp

Ukwehluleka okuningi kwenzeka eduze kwemodeli, hhayi ngaphakathi kwayo: idatha engahambisani, izindawo ezingahambisani, ukuthunyelwa okubuthakathaka, kanye nokungabikho kokuqapha. Amathuluzi efu asiza ukwenza kube sezingeni elifanayo isitoreji, ukubala, kanye namaphethini okuthunyelwa ukuze amamodeli angabambeki ku-"isebenze kwi-laptop yami." Ama-MLOps aneza iglue engekho: ukulandelela, ukubhalisa, amapayipi, kanye nokubuyiselwa emuva ukuze uhlelo luhlale luphinde lukhiqizwe futhi lunakekelwe.

Ukuhamba komsebenzi okuvamile kwe-AI ku-cloud computing, kusukela kudatha kuya ekukhiqizweni

Ukugeleza okuvamile yilokhu: idatha ifika esitorejini samafu, icutshungulwa ibe izici, bese amamodeli eqeqeshwa ku-compute enwebekayo. Okulandelayo, uyisebenzisa nge-API endpoint, umsebenzi we-batch, ukusetha okungenaseva, noma isevisi ye-Kubernetes. Okokugcina, uqapha ukubambezeleka, ukukhukhuleka, kanye nezindleko, bese uphinda ngokuqeqeshwa kabusha kanye nokufakwa okuphephile. Amapayipi amaningi angempela ajikeleza njalo kunokuthunyelwa kanye.

Ukukhetha phakathi kweSageMaker, iVertex AI, i-Azure ML, iDatabricks, kanye neKubernetes

Khetha ngokusekelwe eqinisweni leqembu lakho, hhayi umsindo wokumaketha "wepulatifomu engcono kakhulu". Amapulatifomu e-ML aphethwe (i-SageMaker/Vertex AI/Azure ML) anciphisa izinhlungu zokusebenza ngemisebenzi yokuqeqesha, ama-endpoints, amarejista, kanye nokuqapha. Ama-Databricks avame ukufanela amaqembu anamandla obunjiniyela bedatha afuna i-ML isondele kumapayipi kanye nokuhlaziya. Ama-Kubernetes anikeza ukulawula okuphezulu kanye nokwenza ngokwezifiso, kodwa futhi unezinqubomgomo zokuthembeka, ukukala, kanye nokulungisa amaphutha lapho izinto ziphuka.

Amaphethini okwakha abonakala kakhulu ekusethweni kwamafu e-AI namuhla

Uzobona amaphethini amane njalo: amapulatifomu e-ML aphethwe ngesivinini, i-lakehouse + i-ML yama-orgs okuqala idatha, i-ML equkethwe kuma-Kubernetes ukulawula, kanye ne-RAG (ukukhiqiza okungeziwe kokuthola ulwazi) "kokusebenzisa ulwazi lwethu lwangaphakathi ngokuphephile." I-RAG ivame ukufaka amadokhumenti kusitoreji samafu, ukushumeka + isitolo se-vector, ungqimba lokubuyisa, kanye nezilawuli zokufinyelela ngokungena ngemvume. Iphethini oyikhethayo kufanele ihambisane nokuphatha kwakho kanye nokuvuthwa kwe-ops.

Indlela amaqembu asebenzisa ngayo amamodeli e-AI yamafu: ama-REST API, imisebenzi ye-batch, i-serverless, noma ama-Kubernetes

Ama-REST API avamile ekubikezeleni kwesikhathi sangempela lapho ukubambezeleka komkhiqizo kubalulekile. Ukuqagela kwe-batch kuhle kakhulu ekutholeni amaphuzu okuhleliwe kanye nokusebenza kahle kwezindleko, ikakhulukazi lapho imiphumela ingadingeki ibe ngokushesha. Ama-endpoint angenaseva angasebenza kahle kuthrafikhi e-spiky, kodwa ukuqala okubandayo kanye nokubambezeleka kudinga ukunakwa. I-Kubernetes ilungele uma udinga ukukala okuhle kanye nokuhlanganiswa namathuluzi epulatifomu, kodwa inezela ubunzima bokusebenza.

Okufanele ukuqaphe ekukhiqizeni ukuze kugcinwe izinhlelo ze-AI ziphilile

Okungenani, landela ukubambezeleka, amazinga amaphutha, kanye nezindleko ngokubikezela ngakunye ukuze ukuthembeka kanye nesabelomali kuhlale kubonakala. Ohlangothini lwe-ML, qapha ukuzulazula kwedatha kanye nokuzulazula kokusebenza ukuze ubambe lapho iqiniso lishintsha ngaphansi kwemodeli. Amacala okungena kokungena kanye nemiphumela emibi nayo ibalulekile, ikakhulukazi ezimweni zokusetshenziswa okukhiqizayo lapho abasebenzisi bangaba ngabaphikisi ngobuhlakani. Ukuqapha okuhle kusekela nezinqumo zokubuyela emuva lapho amamodeli ebuyela emuva.

Ukunciphisa izindleko ze-AI yamafu ngaphandle kokusebenza kahle kwe-tanking

Indlela evamile ukusebenzisa imodeli encane kakhulu ehlangabezana nesidingo, bese kulungiswa ukuqagela nge-batching kanye ne-caching. Ukukala ngokuzenzakalela kuyasiza, kodwa kudinga imikhawulo ukuze "ukunwebeka" kungabi "ukusetshenziswa kwemali okungenamkhawulo." Ekuqeqeshweni, ukubala okubonakalayo/okungabekezeleleki kungonga okuningi uma imisebenzi yakho ibekezelela ukuphazamiseka. Ukulandelela izindleko nge-endpoint ngayinye kanye nesici ngasinye kukuvimbela ekwenzeni ngcono ingxenye engafanele yesistimu.

Izingozi ezinkulu zokuphepha nokuthobela imithetho nge-AI efwini

Izingozi ezinkulu ukufinyelela idatha okungalawulwa, ukuphathwa kwezimfihlo ezibuthakathaka, kanye nezindlela zokuhlola ezingekho zokuthi ubani oqeqeshe futhi wasebenzisa ini. I-AI ekhiqizayo inezela izinkinga ezengeziwe njengokujova okusheshayo, imiphumela engaphephile, kanye nedatha ebucayi ebonakala kumalogi. Amapayipi amaningi adinga ukuhlukaniswa kwemvelo (dev/staging/prod) kanye nezinqubomgomo ezicacile zemiyalezo, imiphumela, kanye nokubhalisa okuqondile. Amasethingi aphephile aphatha ukubusa njengemfuneko yesistimu eyinhloko, hhayi i-patch yesonto lokuqalisa.

Izinkomba

1. Isikhungo Sikazwelonke Sezindinganiso Nobuchwepheshe (i-NIST) - SP 800-145 (Okokugcina) - csrc.nist.gov

2. I-Google Cloud - Ama-GPU e-AI - cloud.google.com

3. I-Google Cloud - Idokhumenti ye-TPU Yamafu - docs.cloud.google.com

4. Izinsizakalo Zewebhu ze-Amazon (AWS) - I-Amazon S3 (indawo yokugcina izinto) - aws.amazon.com

5. Izinsizakalo Zewebhu ze-Amazon (AWS) - Iyini ichibi ledatha? - aws.amazon.com

6. Izinsizakalo Zewebhu ze-Amazon (AWS) - Iyini indawo yokugcina idatha? - aws.amazon.com

7. Izinsizakalo zewebhu ze-Amazon (AWS) - Izinsizakalo ze-AI ze-AWS - aws.amazon.com

8. I-Google Cloud - Ama-API e-AI e-Google Cloud - cloud.google.com

9. I-Google Cloud - Iyini i-MLOps? - cloud.google.com

10. I-Google Cloud - I-Vertex AI Model Registry (Isingeniso) - docs.cloud.google.com

11. I-Red Hat - Iyini i-REST API? - redhat.com

12. Imibhalo ye-Amazon Web Services (AWS) - SageMaker Batch Transform - docs.aws.amazon.com

13. Izinsizakalo Zewebhu ze-Amazon (AWS) - Indawo yokugcina idatha vs i-data lake vs i-data mart - aws.amazon.com

14. I-Microsoft Learn - Ukubhaliswa kwe-Azure ML (MLOps) - learn.microsoft.com

15. I-Google Cloud - Ukubuka konke kwe-Google Cloud Storage - docs.cloud.google.com

16. i-arXiv - Iphepha le-Retrieval-Augmented Generation (RAG) - arxiv.org

17. Imibhalo ye-Amazon Web Services (AWS) - I- SageMaker Serverless Inference - docs.aws.amazon.com

18. Kubernetes - Horizontal Pod Autoscaling - kubernetes.io

19. ze-Google Cloud - Vertex AI batch - docs.cloud.google.com

20. Imibhalo ye-Amazon Web Services (AWS) - I- SageMaker Model Monitor - docs.aws.amazon.com

21. I-Google Cloud - Ukuqapha Imodeli ye-Vertex AI (Ukusebenzisa ukuqapha imodeli) - docs.cloud.google.com

22. Izinsizakalo Zewebhu ze-Amazon (AWS) - I-Amazon EC2 Spot Instances - aws.amazon.com

23. I-Google Cloud - Ama-VM Angalungiselelwa - docs.cloud.google.com

24. Imibhalo ye-Amazon Web Services (AWS) - -AWS SageMaker: Indlela esebenza ngayo (Ukuqeqeshwa) - docs.aws.amazon.com

25. I-Google Cloud - I-Google Vertex AI - cloud.google.com

26. I-Microsoft Azure - Ukufunda Komshini we-Azure - azure.microsoft.com

27. I-Databricks - I-Databricks Lakehouse - i-databricks.com

28. Imibhalo ye-Snowflake - Izici ze-AI ze-Snowflake (Umhlahlandlela wokubuka konke) - docs.snowflake.com

29. IBM - IBM watsonx - ibm.com

30. I-Google Cloud - Idokhumenti ye-API yolimi lwemvelo lwefu - docs.cloud.google.com

31. Imibhalo ye-Snowflake - Imisebenzi ye-Snowflake Cortex AI (AI SQL) - docs.snowflake.com

32. I-MLflow - Ukulandelela kwe-MLflow - mlflow.org

33. I-MLflow - Irejista Yemodeli ye-MLflow - mlflow.org

34. I-Google Cloud - MLOps: Ukulethwa okuqhubekayo kanye namapayipi okuzenzakalelayo ekufundeni komshini - cloud.google.com

35. Izinsizakalo Zewebhu ze-Amazon (AWS) - Isitolo Sezici se-SageMaker - aws.amazon.com

36. IBM - IBM watsonx.governance - ibm.com

Thola i-AI Yakamuva Esitolo Esisemthethweni Somsizi we-AI

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