M'ndandanda wazopezekamo[Bisani][Show]
- 1. Mukutanthauza chiyani ndi MLOps?
- 2. Kodi asayansi a data, mainjiniya a data, ndi mainjiniya a ML amasiyana bwanji wina ndi mnzake?
- 3. Kodi chosiyanitsa MLOps ndi ModelOps ndi AIOps ndi chiyani?
- 4. Kodi mungandiuzeko zina mwazabwino za MLOps?
- 5. Kodi mungandiuze zigawo za MLOps?
- 6. Ndi zoopsa ziti zomwe zimabwera pogwiritsa ntchito sayansi ya data?
- 7. Kodi mungafotokoze, kodi drift ndi chiyani?
- 8. Kodi ma MLOps angagwiritsidwe ntchito bwanji m'malingaliro anu?
- 9. Ndi chiyani chomwe chimalekanitsa kuyika kwa static ndi kuyika kwamphamvu?
- 10. Ndi njira zotani zoyesera zomwe mukudziwa?
- 11. Ndi chiyani chomwe chimasiyanitsa mtsinje wa processing ndi batch processing?
- 12. Mukutanthauza chiyani mukamati Training Serving Skew?
- 13. Mukutanthauza chiyani ndi Model Registry?
- 14. Kodi mungafotokoze zambiri za ubwino wa Model Registry?
- 15. Kodi mungafotokoze kuti njira ya Champion-Challenger imagwira ntchito?
- 16. Fotokozani ntchito zamabizinesi a MLOps lifecycle?
- Kutsiliza
Makampani akugwiritsa ntchito matekinoloje omwe akubwera monga nzeru zamakono (AI) ndi kuphunzira pamakina (ML) nthawi zambiri kuti awonjezere kupezeka kwa anthu ku chidziwitso ndi ntchito.
Ukadaulo uwu ukugwiritsidwa ntchito kwambiri m'magawo osiyanasiyana, kuphatikiza mabanki, azachuma, ogulitsa, opanga, ngakhale azaumoyo.
Asayansi a data, mainjiniya ophunzirira makina, ndi mainjiniya anzeru zopangira akufunika kuchokera kumakampani omwe akuchulukirachulukira.
Kudziwa zotheka makina kuphunzira Mafunso ofunsidwa ntchito omwe olemba ntchito akulemba ntchito ndi olemba ntchito angakufunseni ndi ofunikira ngati mukufuna kugwira ntchito m'minda ya ML kapena MLOps.
Mutha kuphunzira momwe mungayankhire ena mwa mafunso oyankhulana a MLOps mu positiyi pamene mukuyesetsa kupeza ntchito yamaloto anu.
1. Mukutanthauza chiyani ndi MLOps?
Mutu wogwiritsa ntchito mitundu ya ML ndi cholinga cha MLOps, chomwe chimatchedwanso Machine Learning Operations, gawo lomwe likutukuka mkati mwa mabwalo akuluakulu a AI/DS/ML.
Cholinga chachikulu cha njira yopangira mapulogalamu ndi chikhalidwe chodziwika kuti MLOps ndikuphatikiza kupanga makina ophunzirira makina / zitsanzo za sayansi ya data ndi machitidwe awo otsatila (Ops).
Ma DevOps Okhazikika ndi MLOps amagawana zofanana, komabe, MLOps imasiyananso kwambiri ndi ma DevOps achikhalidwe.
MLOps imawonjezera zovuta zatsopano poyang'ana zambiri, pomwe DevOps imayang'ana kwambiri pakugwiritsa ntchito ma code ndi mapulogalamu omwe sangakhale omveka.
Kuphatikiza kwa ML, Data, ndi Ops ndizomwe zimapatsa MLOps dzina lodziwika bwino (kuphunzirira pamakina, uinjiniya wa data, ndi DevOps).
2. Kodi asayansi a data, mainjiniya a data, ndi mainjiniya a ML amasiyana bwanji wina ndi mnzake?
Zimasiyanasiyana, mwa lingaliro langa, kutengera olimba. Chilengedwe choyendetsa ndi kusintha kwa deta, komanso kusungirako kwake, kumamangidwa ndi akatswiri opanga deta.
Asayansi a data ndi akatswiri pakugwiritsa ntchito njira zasayansi ndi ziwerengero kusanthula deta ndikupeza mfundo, kuphatikiza kulosera zam'tsogolo motengera zomwe zikuchitika.
Akatswiri opanga mapulogalamu anali kuphunzira ntchito ndi kuyang'anira ntchito zoyendetsera ntchito zaka zingapo zapitazo. Magulu a Ops, kumbali ina, anali kuphunzira zachitukuko pomwe akugwiritsa ntchito zomangamanga monga ma code. Malo a DevOps adapangidwa ndi mitsinje iwiriyi.
MLOps ali m'gulu lomwelo Deta Scientist ndi Data Engineer. Akatswiri opanga ma data akupeza chidziwitso cha zomangamanga zomwe zimafunikira kuti zithandizire mayendedwe amoyo ndikupanga mapaipi ophunzirira mosalekeza.
Asayansi a data akufuna kukulitsa luso lawo la kuyika kwachitsanzo ndi kugoletsa.
Mapaipi opangira data amapangidwa ndi mainjiniya a ML omwe amagwiritsa ntchito zida zomwe zimasinthira data yosasinthika kukhala zolowetsa zomwe zimafunikira ndi mtundu wa sayansi ya data, zowongolera ndikuyendetsa modeliyo, ndikutulutsa zidziwitso zopezeka pamakina otsika.
Onse opanga ma data ndi asayansi a data amatha kukhala mainjiniya a ML.
3. Kodi chosiyanitsa MLOps ndi ModelOps ndi AIOps ndi chiyani?
Pomanga kumapeto mpaka kumapeto makina kuphunzira algorithms.
Kugwiritsiridwa ntchito kwa DevOps pogwira ntchito yonse ya ma algorithms aliwonse, monga Rule-Based Models, amadziwika kuti ModelOps.
AIOps ikugwiritsa ntchito mfundo za DevOps kupanga mapulogalamu a AI kuyambira pachiyambi.
4. Kodi mungandiuzeko zina mwazabwino za MLOps?
- Asayansi a data ndi opanga MLOps amatha kuyambiranso kuyesa mwachangu kuti awonetsetse kuti zitsanzo zaphunzitsidwa ndikuyesedwa moyenera popeza MLOps imathandiza kupanga zonse kapena zambiri za ntchito/masitepe mu MDLC (chitsanzo chachitukuko chamoyo). Komanso zilolezo deta ndi chitsanzo versioning.
- Kuyika malingaliro a MLOps kuchitapo kanthu kumathandizira Opanga Ma Data ndi Data Scientists kukhala ndi mwayi wopanda malire pazosungidwa zomwe zidalimidwa komanso zosanjidwa, zomwe zimafulumizitsa chitukuko cha zitsanzo.
- Asayansi a data adzatha kubwereranso pa chitsanzo chomwe chinachita bwino ngati kubwereza komweku sikukugwirizana ndi zomwe akuyembekezera chifukwa chokhoza kukhala ndi zitsanzo ndi ma dataset omasuliridwa, zomwe zidzakulitsa kwambiri njira yowunikira chitsanzo.
- Monga njira za MLOps zimadalira kwambiri DevOps, zimaphatikizanso malingaliro angapo a CI/CD, omwe amakulitsa khalidwe ndi kudalirika kwa code.
5. Kodi mungandiuze zigawo za MLOps?
Design: MLOps kwambiri kuphatikizapo kapangidwe kaganizidwe. Kuyambira ndi mtundu wa nkhaniyo, kuyesa zongoyerekeza, kamangidwe, ndi kutumizidwa
Kumanga chitsanzo: Kuyesa kwachitsanzo ndi kutsimikizira ndi gawo la sitepe iyi, pamodzi ndi mapaipi opangira deta ndi kuyesa kukhazikitsa makina abwino kwambiri ophunzirira makina.
ntchito: Mtunduwu uyenera kukhazikitsidwa ngati gawo la ntchito ndikuwunika ndikuwunikidwa mosalekeza. Njira za CI/CD zimawunikidwa ndikuyamba kugwiritsa ntchito chida choyimba.
6. Ndi zoopsa ziti zomwe zimabwera pogwiritsa ntchito sayansi ya data?
- Ndizovuta kukulitsa mtundu wonsewo pakampani.
- Popanda chenjezo, chitsanzocho chimatseka ndikusiya kugwira ntchito.
- Nthawi zambiri, kulondola kwamitundu kumakulirakulira ndi nthawi.
- Chitsanzochi chimapanga maulosi olakwika potengera zomwe tawonapo zomwe sizingaunikenso.
- Asayansi a data ayeneranso kusunga zitsanzo, koma ndizokwera mtengo.
- MLOps angagwiritsidwe ntchito kuchepetsa zoopsazi.
7. Kodi mungafotokoze, kodi drift ndi chiyani?
Pamene magwiridwe antchito a gawo lachitsanzo (pogwiritsa ntchito zidziwitso zenizeni) atsika kuchokera pakuchita kwake kwa gawo lophunzitsira, izi zimatchedwa model drift, zomwe zimadziwikanso kuti idea drift (pogwiritsa ntchito mbiri yakale, yolembedwa).
Kuchita kwachitsanzo kumasokonekera poyerekeza ndi magawo ophunzitsira ndi othandizira, chifukwa chake amatchedwa "sitima/service skew."
Zinthu zambiri, kuphatikizapo:
- Njira yofunikira yomwe deta imagawira yasintha.
- Maphunzirowa adayang'ana pamagulu ochepa, komabe, kusintha kwa chilengedwe komwe kunachitika kumene kunawonjezera gawo lina.
- Muzovuta za NLP, deta yapadziko lonse lapansi imakhala ndi ma tokeni ochulukirapo kuposa momwe amaphunzitsira.
- Zochitika zosayembekezereka, monga chitsanzo chomangidwa pa data ya pre-COVID yomwe ikunenedweratu kuti idzachita zoyipa kwambiri pazomwe zasonkhanitsidwa panthawi ya mliri wa COVID-19.
Kuwunika mosalekeza kachitidwe kachitsanzo kumafunika nthawi zonse kuti muzindikire kusuntha kwachitsanzo.
Kubwerezanso kwachitsanzo kumafunika nthawi zonse ngati njira yothetsera vuto pamene pali kuchepa kwachitsanzo; chifukwa cha kuchepa chiyenera kudziwika ndipo njira zoyenera zothandizira ziyenera kugwiritsidwa ntchito.
8. Kodi ma MLOps angagwiritsidwe ntchito bwanji m'malingaliro anu?
Pali njira zitatu zogwiritsira ntchito MLOps:
MLOps mlingo 0 (Manual Process): Pa mlingo uwu, masitepe onse-kuphatikizapo kukonzekera deta, kufufuza, ndi kuphunzitsa-amachitidwa pamanja. Gawo lirilonse liyenera kuchitidwa pamanja, komanso kusintha kuchokera kumodzi kupita kwina.
Mfundo yaikulu ndi yakuti gulu lanu la sayansi ya deta limangoyang'anira zitsanzo zochepa zomwe sizisinthidwa kawirikawiri.
Zotsatira zake, palibe Continuous Integration (CI) kapena Continuous Deployment (CD), ndipo kuyesa kachidindo kameneka kumaphatikizidwa mu script execution kapena notebook execution, ndipo kutumizidwa kumachitika mu microservice yokhala ndi REST API.
MLOps mlingo 1 (automation of the ML pipeline): Pogwiritsa ntchito njira ya ML, cholinga chake ndikuphunzitsa mosalekeza (CT). Mutha kukwaniritsa mosalekeza zolosera zautumiki motere.
Kutumiza kwathu payipi yonse yophunzitsira kumatsimikizira kuti mtunduwo umaphunzitsidwa zokha kupanga pogwiritsa ntchito deta yatsopano kutengera zoyambitsa mapaipi.
MLOps mlingo 2 (automation of the CI/CD pipeline): Imapita sitepe imodzi pamwamba pa mlingo wa MLOps. Dongosolo lolimba la CI/CD lokhazikika limafunikira ngati mukufuna kusintha mapaipi opangidwa mwachangu komanso modalirika:
- Mumapanga ma source code ndikuchita mayeso angapo pagawo lonse la CI. Phukusi, zoyeserera, ndi zinthu zakale ndizotuluka pasiteji, zomwe zidzatumizidwa mtsogolo.
- Zomwe zimapangidwa ndi gawo la CI zimatumizidwa kumalo omwe akukhudzidwa panthawi ya CD. Chitoliro chogwiritsidwa ntchito ndi kukhazikitsidwa kwachitsanzo chokonzedwanso ndicho zotsatira za siteji.
- Paipi isanayambe kubwereza kwatsopano kwa kuyesako, asayansi a data ayenerabe kusanthula deta ndi gawo lachitsanzo pamanja.
9. Ndi chiyani chomwe chimalekanitsa kuyika kwa static ndi kuyika kwamphamvu?
Mtunduwu umaphunzitsidwa popanda intaneti Static Deployment. Mwa kuyankhula kwina, timaphunzitsa chitsanzocho kamodzi kokha ndikuchigwiritsa ntchito kwa kanthawi. Pambuyo pophunzitsidwa kumaloko, amasungidwa ndikutumizidwa ku seva kuti agwiritsidwe ntchito popanga maulosi a nthawi yeniyeni.
Chitsanzocho chimagawidwa ngati pulogalamu yokhazikika yokhazikika. pulogalamu yomwe imalola kuwerengera kwa zopempha, monga fanizo.
Chitsanzocho chimaphunzitsidwa pa intaneti Dynamic Deployment. Ndiko kuti, deta yatsopano ikuwonjezeredwa nthawi zonse ku dongosolo, ndipo chitsanzocho chimasinthidwa mosalekeza kuti chiziwerengera.
Zotsatira zake, mutha kulosera pogwiritsa ntchito seva pakufunika. Pambuyo pake, chitsanzocho chimagwiritsidwa ntchito poperekedwa ngati mapeto a API omwe amayankha mafunso a ogwiritsa ntchito, pogwiritsa ntchito intaneti monga Flask kapena FastAPI.
10. Ndi njira zotani zoyesera zomwe mukudziwa?
Kuyesa kwamagulu: Poyesa mayeso m'malo osiyana ndi malo omwe amaphunzitsidwa, imatsimikizira chitsanzocho. Pogwiritsa ntchito ma metric omwe mungasankhe, monga kulondola, RMSE, ndi zina zotero, kuyesa kwa batch kumachitika pagulu la zitsanzo za data kuti zitsimikizire kutengera kwachitsanzo.
Kuyesa kwamagulu kumatha kuchitika pamapulatifomu osiyanasiyana apakompyuta, monga seva yoyeserera, seva yakutali, kapena mtambo. Kawirikawiri, chitsanzocho chimaperekedwa ngati fayilo yosakanizidwa, yomwe imayikidwa ngati chinthu ndikuchokera ku deta yoyesera.
A / B kuyezetsa: Amagwiritsidwa ntchito nthawi zambiri posanthula makampeni otsatsa komanso kupanga ntchito (mawebusayiti, mapulogalamu am'manja, ndi zina).
Kutengera ndi kampani kapena ntchito, njira zowerengera zimagwiritsidwa ntchito kusanthula zotsatira za kuyesa kwa A/B kuti zisankhe mtundu womwe ungachite bwino popanga. Nthawi zambiri, kuyesa kwa A/B kumachitika motere:
- Zomwe zikuchitika kapena zenizeni zenizeni zimagawidwa kapena kugawidwa m'magulu awiri, Set A ndi Set B.
- Seti A data imatumizidwa ku mtundu wakale, pomwe Seti B imatumizidwa ku mtundu womwe wasinthidwa.
- Kutengera momwe bizinesi imagwiritsidwira ntchito kapena njira, njira zingapo zowerengera zingagwiritsidwe ntchito poyesa magwiridwe antchito achitsanzo (mwachitsanzo, kulondola, kulondola, ndi zina zotero) kuti muwone ngati chitsanzo chatsopano (chitsanzo B) chikuposa chitsanzo chakale (chitsanzo A).
- Kenako timayesa zowerengera zowerengera: Zongoganiza zopanda pake zimati mtundu watsopanowu ulibe mphamvu pamtengo wapakati wazizindikiro zamabizinesi zomwe zikuyang'aniridwa. Malinga ndi malingaliro ena, njira yatsopanoyi imawonjezera mtengo wapakati pa zowunikira zamabizinesi.
- Pomaliza, timayesa ngati chitsanzo chatsopanocho chimapangitsa kuti ma KPI amalonda apite patsogolo kwambiri.
Mayeso a mthunzi kapena siteji: Chitsanzo chimawunikidwa mu chibwereza cha malo opangira chisanayambe kugwiritsidwa ntchito popanga (malo ochitira masewera).
Izi ndizofunikira kuti mudziwe momwe chitsanzocho chimagwirira ntchito ndi deta yeniyeni komanso kutsimikizira kulimba kwa chitsanzocho. imachitika popereka deta yofanana ndi payipi yopanga ndikupereka nthambi yotukuka kapena chitsanzo kuti chiyesedwe pa seva ya staging.
Chotsalira chokha ndichakuti palibe zosankha zamabizinesi zomwe zidzapangidwe pa seva yokhazikika kapena zowonekera kwa ogwiritsa ntchito chifukwa cha nthambi yachitukuko.
Kulimba mtima ndi machitidwe a chitsanzo kudzawunikidwa mowerengera pogwiritsa ntchito zotsatira za malo owonetsera pogwiritsa ntchito miyeso yoyenera.
11. Ndi chiyani chomwe chimasiyanitsa mtsinje wa processing ndi batch processing?
Titha kuwongolera zomwe timagwiritsa ntchito popanga zolosera zathu zenizeni pogwiritsa ntchito njira ziwiri: batch ndi stream.
Ndondomeko yamagulu zinthu zomwe zachitika kale pa chinthu china, zomwe zimagwiritsidwa ntchito kupanga maulosi a nthawi yeniyeni.
- Apa, timatha kuwerengera mozama za mawonekedwe osalumikizana ndi intaneti ndikukhala ndi chidziwitso chokonzekera kufotokozera mwachangu.
- Mbali, komabe, zaka kuyambira pomwe zidakonzedweratu kale. Izi zitha kukhala zovuta kwambiri ngati zomwe zachitika posachedwa. (Mwachitsanzo, kuzindikira zochitika zachinyengo mwamsanga momwe zingathere.)
Pokhala pafupi ndi nthawi yeniyeni, zowonetsera zamtundu wina, zomwe zimapangidwira zimapangidwira pamitsinje pamitsinje pamtundu wina wa zolowetsa.
- Apa, popereka chitsanzocho nthawi yeniyeni, mawonekedwe akukhamukira, tikhoza kupeza maulosi olondola kwambiri.
- Komabe, zowonjezera zowonjezera zimafunikira pakukonza mitsinje ndikusunga ma data (Kafka, Kinesis, etc.). (Apache Flink, Beam, etc.)
12. Mukutanthauza chiyani mukamati Training Serving Skew?
Kusiyanitsa pakati pa ntchito potumikira ndi kugwira ntchito panthawi ya maphunziro kumadziwika kuti skew-serving skew. Izi skew akhoza kuyambitsidwa ndi zinthu zotsatirazi:
- Kusiyana kwa momwe mumagwiritsira ntchito deta pakati pa mapaipi operekera ndi kuphunzitsa.
- Kusintha kwa data kuchokera kumaphunziro anu kupita ku ntchito yanu.
- Njira yofotokozera pakati pa algorithm yanu ndi mtundu wanu.
13. Mukutanthauza chiyani ndi Model Registry?
Model Registry ndi malo apakati pomwe opanga zitsanzo amatha kusindikiza zitsanzo zomwe zili zoyenera kugwiritsidwa ntchito popanga.
Madivelopa atha kugwirizanitsa ndi magulu ena ndi omwe ali nawo kuti azitha kuyang'anira moyo wamitundu yonse mkati mwabizinesi pogwiritsa ntchito kaundula. Zitsanzo zophunzitsidwa zikhoza kutumizidwa ku registry yachitsanzo ndi wasayansi wa data.
Zitsanzozo zimakonzedwa kuti ziyesedwe, zitsimikizidwe, ndi kutumizidwa kuti zipangidwe zikakhala mu kaundula. Kuphatikiza apo, zitsanzo zophunzitsidwa zimasungidwa m'ma registries achitsanzo kuti zifike mwachangu ndi pulogalamu iliyonse yophatikizika kapena ntchito.
Pofuna kuyesa, kuyesa, ndi kutumiza chitsanzo pakupanga, opanga mapulogalamu ndipo owunikira amatha kuzindikira mwachangu ndikusankha mitundu yabwino kwambiri yamitundu yophunzitsidwa bwino (kutengera zowunikira).
14. Kodi mungafotokoze zambiri za ubwino wa Model Registry?
Izi ndi zina mwa njira zomwe kaundula wa ma model amawongolera kasamalidwe ka moyo wanu:
- Kuti kutumiza kusakhale kosavuta, sungani zofunikira pa nthawi yothamanga ndi metadata yamamodeli anu ophunzitsidwa.
- Mitundu yanu yophunzitsidwa, yotumizidwa, komanso yopuma pantchito iyenera kulembetsedwa, kutsatiridwa, ndi kusinthidwa m'malo apakati, osakasaka.
- Pangani mapaipi odzipangira okha omwe amathandizira kutumiza mosalekeza, kuphunzitsa, ndi kuphatikiza mtundu wanu wopanga.
- Fananizani zitsanzo zophunzitsidwa kumene (kapena zotsutsa) m'malo ochitira masewerawa ndi omwe akugwira ntchito pakupanga (opambana).
15. Kodi mungafotokoze kuti njira ya Champion-Challenger imagwira ntchito?
Ndizotheka kuyesa zisankho zosiyanasiyana zogwirira ntchito popanga pogwiritsa ntchito njira ya Champion Challenger. Mwinamwake mwamvapo za kuyesa kwa A / B pankhani ya malonda.
Mwachitsanzo, mutha kulemba mizere iwiri yosiyana ndikuyigawa mwachisawawa kwa anthu omwe mukufuna kuti muwonjezere kutsegulira kwa kampeni ya imelo.
Dongosolo limasunga momwe maimelo amagwirira ntchito (mwachitsanzo, maimelo otseguka) mogwirizana ndi mutu wake, kukulolani kuti mufananize kuchuluka kotseguka kwa mutu uliwonse kuti muwone chomwe chili chothandiza kwambiri.
Champion-Challenger ikufanana ndi kuyesa kwa A/B pankhaniyi. Mutha kugwiritsa ntchito malingaliro oganiza kuti muwunikire chotsatira chilichonse ndikusankha yothandiza kwambiri pamene mukuyesa njira zosiyanasiyana kuti mupange chisankho.
Chitsanzo chopambana kwambiri chimagwirizana ndi ngwazi. Wotsutsa woyamba ndi mndandanda wofananira wa otsutsa tsopano ndizo zonse zomwe zilipo mu gawo loyamba la kuphedwa m'malo mwa ngwazi.
Wopambana amasankhidwa ndi dongosolo kuti awonjezere ntchito.
Otsutsawo amasiyana wina ndi mzake. Wopambana watsopano ndiye amatsimikiziridwa ndi wotsutsa yemwe amatulutsa zotsatira zabwino kwambiri.
Ntchito zomwe zikuphatikizidwa mu kufananitsa kwa akatswiri ndi otsutsa zalembedwa pansipa mwatsatanetsatane:
- Kuwunika mtundu uliwonse wa opikisana nawo.
- Kuwunika zotsatira zomaliza.
- Kufananiza zotsatira zowunikira kuti mutsimikizire wopambana wopambana.
- Kuwonjezera ngwazi yatsopano kumalo osungira
16. Fotokozani ntchito zamabizinesi a MLOps lifecycle?
Tiyenera kusiya kuganizira kuphunzira pamakina ngati kuyesa kobwerezabwereza kuti zitsanzo zamakina azitha kupanga. MLOps ndi mgwirizano wa uinjiniya wamapulogalamu ndi kuphunzira pamakina.
Chotsatira chomalizidwa chiyenera kuganiziridwa motero. Chifukwa chake, kachidindo kazinthu zamatekinoloje ziyenera kuyesedwa, kugwira ntchito, komanso modula.
MLOps ili ndi nthawi ya moyo yomwe ikufanana ndi kayendedwe ka makina ophunzirira, kupatulapo kuti chitsanzocho chimasungidwa mpaka kupanga.
A MLOps Engineers ndiye amayang'anitsitsa izi kuti atsimikizire kuti mtundu wamtunduwu ndi womwe umapangidwira.
Nazi zina zogwiritsira ntchito matekinoloje angapo a MLOps:
- Ma Model Registries: Ndi zomwe zikuwoneka. Magulu akuluakulu amasunga ndikuyang'anira zitsanzo zamawonekedwe mu registries yachitsanzo. Ngakhale kubwereranso ku mtundu wakale ndi mwayi.
- Sitolo Yazinthu: Mukamagwira ntchito ndi ma data akuluakulu, pakhoza kukhala mitundu yosiyana ya ma dataset ndi magawo azinthu zinazake. Malo ogulitsira ndi njira yotsogola, yokoma yogwiritsira ntchito ntchito yokonzekera deta kuyambira kale kapena magulu enanso.
- Masitolo a Metadata: Ndikofunikira kuyang'anira metadata molondola nthawi yonse yomwe ikupanga ngati zomwe sizinapangidwe, monga zithunzi ndi zolemba, ziyenera kugwiritsidwa ntchito bwino.
Kutsiliza
Ndikofunika kukumbukira kuti nthawi zambiri, wofunsayo akuyang'ana njira, pamene wofunsayo akufunafuna yankho.
Yoyamba imatengera luso lanu laukadaulo, pomwe yachiwiri ikukhudza njira yomwe mumagwiritsa ntchito kuti muwonetse luso lanu.
Pali njira zingapo zomwe muyenera kuchita poyankha mafunso oyankhulana a MLOps kuti muthandize wofunsayo kuti amvetse bwino momwe mukufuna kuyesa ndikuthana ndi vuto lomwe lilipo.
Kuyikira kwawo kumangoyang'ana pakuchita kolakwika kuposa koyenera. Yankho limafotokoza nkhani, ndipo dongosolo lanu ndi chithunzi chabwino kwambiri cha chidziwitso chanu ndi kuthekera kolumikizana.
Siyani Mumakonda