Zviri Mukati[Viga][Ratidza]
- 1. Unorevei neMLOps?
- 2. Masayendisiti e data, mainjiniya e data, uye mainjiniya eML anosiyana sei kubva kune mumwe?
- 3. Chii chinosiyanisa MLOps kubva kuModelOps neAIOps?
- 4. Unogona here kundiudza zvimwe zvinobatsira zveMLOps?
- 5. Ungandiudza here zvikamu zveMLOps?
- 6. Ndedzipi njodzi dzinouya nekushandisa data sainzi?
- 7. Unogona here kutsanangura, chii chinonzi model drift?
- 8. Ndedzipi nzira dzakasiyana dzingashandiswa maMLOps, semaonero ako?
- 9. Chii chinoparadzanisa static deployment kubva kune dynamic deployment?
- 10. Ndeapi maitiro ekuyedza ekugadzira aunoziva?
- 11. Chii chinosiyanisa kuyerera kwerukova kubva pakugadzira batch?
- 12. Unorevei neKudzidzisa Kushumira Skew?
- 13. Unorevei neModel Registry?
- 14. Unogona here kutsanangura pamusoro pezvakanakira Model Registry?
- 15. Unogona here kutsanangura nzira yeChampion-Challenger inoshanda?
- 16. Rondedzera mashandisirwo emabhizimusi-chikamu cheMLOps lifecycle?
- mhedziso
Makambani ari kushandisa matekinoroji ari kusimukira akaita seartificial intelligence (AI) uye kudzidza muchina (ML) kakawanda kuwedzera kuwaniswa kweveruzhinji kuruzivo nemasevhisi.
Aya matekinoroji ari kuwedzera kushandiswa muzvikamu zvakasiyana, kusanganisira kubhengi, mari, zvitoro, kugadzira, uye kunyangwe hutano hwehutano.
Masayendisiti edata, mainjiniya ekudzidza muchina, uye mainjiniya muhungwaru hwekugadzira ari kudiwa kubva kuhuwandu huri kuwedzera hwemakambani.
Kuziva zvinogoneka machine learning Oparesheni mibvunzo yebvunzurudzo inogona kuunza kwauri yekuhaya mamaneja nevanopinza basa yakakosha kana uchida kushanda muminda yeML kana MLOps.
Unogona kudzidza kupindura kune mamwe emibvunzo yeMLOps yekubvunzurudza mune ino positi paunenge uchishanda kuti uwane basa rako rekurota.
1. Unorevei neMLOps?
Musoro wekushandisa modhi yeML ndiyo inotariswa neMLOps, inozivikanwawo seMuchina Kudzidza Operations, munda uri kusimukira mukati meiyo mikuru AI/DS/ML nhandare.
Chinangwa chikuru cheinjiniya yesoftware maitiro uye tsika inozivikanwa seMLOps kubatanidza kusikwa kwemuchina wekudzidza / data sainzi modhi uye kunotevera kwavo kushanda (Ops).
Conventional DevOps uye MLOps vanogovana zvimwe zvakafanana, zvisinei, MLOps zvakare inosiyana zvakanyanya kubva kune yechinyakare DevOps.
MLOps inowedzera hutsva hwekuomarara nekutarisa pane data, nepo DevOps inonyanya kutarisisa pakuita kodhi kodhi uye software kuburitswa iyo isingagone kutaura.
Iko kusanganiswa kweML, Data, uye Ops ndiko kunopa MLOps zita rayo rakajairika (muchina kudzidza, data engineering, uye DevOps).
2. Masayendisiti e data, mainjiniya e data, uye mainjiniya eML anosiyana sei kubva kune mumwe?
Inosiyana, mumaonero angu, zvichienderana nekambani. Nzvimbo yekufambisa uye shanduko yedata, pamwe nekuchengetedza kwayo, inovakwa nevainjiniya ve data.
Masayendisiti edata inyanzvi mukushandisa hunyanzvi hwesainzi uye hwehuwandu kuongorora data uye kuita mhedziso, kusanganisira kufanotaura nezvemaitiro emangwana zvichienderana nemaitiro avapo.
Mainjiniya eSoftware aidzidza mashandiro uye maneja ekuisa zvivakwa makore mashoma apfuura. Zvikwata zveOps, kune rumwe rutivi, zvaidzidza budiriro apo vachishandisa zvivakwa sekodhi. Nzvimbo yeDevOps yakagadzirwa nenzizi mbiri idzi.
MLOps iri muchikamu chimwe chete ne Data Scientist uye Data Engineer. Mainjiniya edata vari kuwana ruzivo nezve zvivakwa zvinodiwa kutsigira modhi yehupenyu uye kugadzira mapaipi ekuenderera mberi kudzidziswa.
Data masayendisiti anotsvaka kuvandudza modhi yavo yekuendesa uye kugona zvibodzwa.
Pombi yekugadzira-giredhi yedata pombi inovakwa nevainjiniya veML vachishandisa masisitimu anoshandura data rakasvibira kuita mupiro unodiwa nesainzi data modhi, inotambira uye inomhanyisa modhi, uye inoburitsa dataset yakapihwa kune yakadzika masisitimu.
Vese mainjiniya edata uye masayendisiti edata vanokwanisa kuve mainjiniya eML.
3. Chii chinosiyanisa MLOps kubva kuModelOps neAIOps?
Paunenge uchigadzira kupera-kumagumo muchina kudzidza algorithms, MLOps iDevOps application iyo inosanganisira kuunganidza data, data pre-processing, kugadzirwa kwemhando, kuendesa modhi mukugadzira, modhi yekutarisa mukugadzira, uye modhi periodic kusimudzira.
Kushandiswa kweDevOps mukubata kuitiswa kwese kwechero algorithms, senge Rule-Based Models, inozivikanwa seModelOps.
AI Ops iri kushandisa DevOps misimboti kugadzira AI apps kubva kutanga.
4. Unogona here kundiudza zvimwe zvinobatsira zveMLOps?
- Masayendisiti edata uye vagadziri veMLOps vanogona kukurumidza kumhanyisa miedzo kuti vaone kuti mamodheru akadzidziswa uye nekuongororwa zvakakodzera sezvo MLOps ichibatsira otomatiki ese kana mazhinji emabasa/matanho muMDLC (model development lifecycle). Kuwedzera mvumo data uye modhi shanduro.
- Kuisa mazano eMLOps mukuita kunoita kuti maInjiniya eData neData Sainzi vawane mukana usina kuganhurirwa kumaseti akarimwa uye akachekwa, ayo anowedzera kukurumidza kuvandudzwa kwemamodheru.
- Data masayendisiti anozokwanisa kuwira kumashure pamuenzaniso wakaita zvirinani kana iyo yazvino iteration isingaenderane nezvinotarisirwa nekuda kwekugona kuve nemamodheru uye dhatabheti rakashandurwa, izvo zvinonyanya kuwedzera modhi yekuongorora nzira.
- Sezvo nzira dzeMLOps dzichivimba zvakanyanya neDevOps, dzinosanganisira akati wandei eCI/CD pfungwa, iyo inosimudzira iyo kunaka uye kuvimbika kwekodhi.
5. Ungandiudza here zvikamu zveMLOps?
patani: MLOps zvakanyanya zvinosanganisira kugadzira kufunga. Kutanga nemhando yenyaya, kuyedza hypotheses, dhizaini, uye kutumira
Model kuvaka: Kuyedzwa kwemuenzaniso uye kusimbiswa chikamu chedanho iri, pamwe nemapaipi einjiniya yedata uye kuyedza kumisikidza masisitimu ekudzidza emuchina.
Operations: Iyo modhi inofanirwa kuitwa sechikamu chemaitiro uye inogara ichiongororwa nekuongororwa. Iyo CI/CD maitiro anobva atariswa uye otanga kushandisa orchestration chishandiso.
6. Ndedzipi njodzi dzinouya nekushandisa data sainzi?
- Zvakaoma kuyera modhi mukambani yese.
- Pasina yambiro, modhi inodzima uye inomira kushanda.
- Kazhinji, kurongeka kwemamodeli kunowedzera kuipa nekufamba kwenguva.
- Iyo modhi inoita fungidziro dzisiri dzechokwadi zvichibva pane imwe cherechedzo isingagone kuongororwa zvakare.
- Data masayendisiti anofanirwawo kuchengetedza mamodheru, asi ane mutengo.
- MLOps inogona kushandiswa kuderedza njodzi idzi.
7. Unogona here kutsanangura, chii chinonzi model drift?
Kana dhizaini remuenzaniso (uchishandisa data repasirese) richidzikira kubva pakuita kwayo chikamu chekudzidzira, izvi zvinozivikanwa semodhi drift, inozivikanwawo sepfungwa Drift (uchishandisa nhoroondo, yakanyorwa data).
Kuita kwemuenzaniso kwakamisikidzwa mukuenzanisa nekudzidziswa uye nhanho dzekushandira, nekudaro zita rekuti "chitima / shumira skew."
Zvakawanda zvinhu, zvinosanganisira:
- Iyo yakakosha nzira iyo data inogoverwa yakachinja.
- Kudzidziswa kwakanangana nenhamba shoma yezvikamu, zvisinei, shanduko yezvakatipoteredza ichangobva kuitika yakawedzera imwe nzvimbo.
- Mumatambudziko eNLP, iyo chaiyo-yepasi data ine yakakura zvisingaite yenhamba tokeni pane iyo data yekudzidziswa.
- Zviitiko zvisingatarisirwi, senge modhi yakavakirwa pane pre-COVID data iri kufanotaurwa kuita zvakanyanya kuipa pane data rakaunganidzwa panguva yeCOVID-19 denda.
Kuramba uchitarisa maitiro eiyo modhi inogara ichidikanwa kuti uone modhi yekudonha.
Model retraining inoda kugara ichidikanwa semushonga kana paine kuramba kuderera mukuita kwemuenzaniso; chikonzero chekuderera chinofanira kuonekwa uye nzira dzakakodzera dzekurapa dzinofanira kushandiswa.
8. Ndedzipi nzira dzakasiyana dzingashandiswa maMLOps, semaonero ako?
Pane nzira nhatu dzekuisa MLOps mukuita:
MLOps level 0 (Manual process): Mune iyi nhanho, matanho ese-kusanganisira kugadzirira data, kuongorora, uye kudzidziswa-anoitwa nemaoko. Nhanho imwe neimwe inofanira kuitwa nemaoko, pamwe chete neshanduko kubva kune imwe kuenda kune inotevera.
Icho chiripo ndechekuti timu yako yesainzi yedata inongobata nhamba diki yemamodheru asina kuvandudzwa kazhinji.
Nekuda kweizvozvo, hapana Kuenderera Kubatanidzwa (CI) kana Kuenderera Kuenderera mberi (CD), uye kuyedza iyo kodhi inowanzobatanidzwa mukuita script kana kuuraya notebook, uye kutumirwa kunoitika mumicroservice ine VAMWE API.
MLOps nhanho 1 (otomatiki yeML pombi): Nekuita otomatiki maitiro eML, chinangwa ndechekuramba uchidzidzisa modhi (CT). Iwe unogona kuzadzisa inoenderera modhi yekufungidzira sevhisi yekuendesa nenzira iyi.
Kuendesa kwedu kwepombi yekudzidzisa yese kunovimbisa kuti modhi inodzidziswa otomatiki mukugadzira uchishandisa data nyowani zvichienderana neanoshanda pombi inokonzeresa.
MLOps level 2 (otomatiki yeCI/CD pombi): Inoenda nhanho imwe pamusoro peMLOps level. Iyo yakasimba automated CI/CD system inodiwa kana iwe uchida kugadzirisa mapaipi mukugadzira nekukurumidza uye nekuvimbika:
- Iwe unogadzira kodhi kodhi uye unoita akawanda bvunzo mukati meCI nhanho. Packages, executables, uye maartifacts ndizvo zvinobuda padanho, izvo zvinozoiswa pane imwe nguva inotevera.
- Izvo zvigadzirwa zvakagadzirwa neCI nhanho zvinoiswa kune inotarirwa nharaunda panguva yeCD nhanho. Pombi yakaiswa ine yakagadziridzwa modhi yekumisikidza ndiyo inobuda padanho.
- Pamberi pepombi isati yatanga kudzokorora kutsva kwekuyedza, data masayendisiti anofanirwa kuita iyo data uye modhi yekuongorora chikamu pamaoko.
9. Chii chinoparadzanisa static deployment kubva kune dynamic deployment?
Iyo modhi inodzidziswa pasina Indaneti Static Deployment. Mune mamwe mazwi, isu tinodzidzisa modhi yacho kamwe chete uye tozoishandisa kwenguva yakati. Mushure mekunge modhi yakadzidziswa munharaunda, inochengetwa uye inotumirwa kune server kuti ishandiswe kugadzira chaiyo-nguva kufanotaura.
Iyo modhi inozogovaniswa seinogadzika application software. chirongwa chinobvumira batch scoring yezvikumbiro, semuenzaniso.
Iyo modhi inodzidziswa online Dynamic Deployment. Ndokunge, data nyowani inogara ichiwedzerwa kune sisitimu, uye modhi inovandudzwa nguva nenguva kuti izvidavirire.
Nekuda kweizvozvo, iwe unogona kuita fungidziro uchishandisa sevha pane zvinodiwa. Mushure meizvozvo, modhi yacho inoiswa mukushandiswa nekupihwa seyekupedzisira API inopindura mibvunzo yemushandisi, uchishandisa webhu chimiro senge. Flask kana FastAPI.
10. Ndeapi maitiro ekuyedza ekugadzira aunoziva?
Batch test: Nekuita bvunzo munzvimbo dzakasiyana neiyo yenzvimbo yekudzidzira, inosimbisa iyo modhi. Uchishandisa metrics esarudzo, senge chokwadi, RMSE, nezvimwewo, kuyedzwa kwebatch kunoitwa paboka remasampuli edatha kuratidza modhi inference.
Kuyedzwa kweBatch kunogona kuitwa pamapuratifomu akasiyana ekombuta, senge test server, sevha iri kure, kana gore. Kazhinji, iyo modhi inopiwa seyakaomeswa faira, iyo inotakurwa sechinhu uye inotaridzwa kubva kubvunzo data.
A / B yokuedzwa: Inowanzo shandiswa pakuongorora mishandirapamwe yekushambadzira pamwe nekugadzira masevhisi (mawebhusaiti, nharembozha, nezvimwewo).
Zvichienderana nekambani kana mashandiro, nzira dzenhamba dzinoshandiswa kuongorora mhedzisiro yekuyedzwa kweA/B kusarudza kuti ndeipi modhi ichaita zvirinani mukugadzira. Kazhinji, kuongororwa kweA / B kunoitwa nenzira inotevera:
- Hupenyu kana chaiyo-nguva data yakakamurwa kana kupatsanurwa kuita maviri seti, Seta A uye Set B.
- Seta A data inotumirwa kune yekare modhi, nepo Set B data inotumirwa kune yakagadziridzwa modhi.
- Zvichienderana nekesi yekushandiswa kwebhizinesi kana maitiro, nzira dzinoverengeka dzenhamba dzinogona kushandiswa kuongorora maitiro emhando (somuenzaniso, kurongeka, kurongeka, zvichingodaro) kuona kana iyo modhi itsva (modhi B) inokunda yekare modhi (modhi A).
- Isu tobva taita statistical hypothesis yekuyedza: Iyo null hypothesis inoti iyo modhi nyowani haina mhedzisiro paavhareji kukosha kwebhizinesi zviratidzo zviri kuongororwa. Zvinoenderana neimwe hypothesis, iyo nyowani modhi inowedzera avhareji kukosha kwekutarisa bhizinesi zviratidzo.
- Chekupedzisira, isu tinoongorora kana iyo modhi nyowani ichiguma nekuvandudzwa kwakakosha mune mamwe bhizinesi KPIs.
Mumvuri kana dariro bvunzo: Modhi inoongororwa mune yakapetwa yenzvimbo yekugadzira isati yashandiswa mukugadzira (staging nharaunda).
Izvi zvakakosha pakuona mashandiro eiyo modhi nechaiyo-nguva data uye kusimbisa kusimba kweiyo modhi. inoitwa nekuisa iyo data yakafanana sepombi yekugadzira uye kuendesa bazi rakagadziridzwa kana modhi kuti iedzwe pane inomira sevha.
Iyo yega dhizaini ndeyekuti hapana sarudzo dzebhizinesi dzichaitwa pane yekumisikidza server kana kuoneka kune vashandisi vekupedzisira semhedzisiro yebazi rekuvandudza.
Kusimba uye kuita kwemuenzaniso kuchaongororwa nenhamba uchishandisa mhedzisiro yenzvimbo yekutandarira uchishandisa ma metrics akakodzera.
11. Chii chinosiyanisa kuyerera kwerukova kubva pakugadzira batch?
Isu tinogona kushandura hunhu hwatinoshandisa kugadzira yedu chaiyo-nguva yekufungidzira tichishandisa nzira mbiri dzekugadzirisa: batch uye rwizi.
Batch process maficha kubva pane yakatangira panguva yechinhu chakati, chinozoshandiswa kugadzira fungidziro yenguva chaiyo.
- Pano, isu tinokwanisa kuita yakadzika maficha maverengero pasina Indaneti uye tine data rakagadzirirwa kukurumidza kufungidzira.
- Zvimiro, zvisinei, zera kubvira zvakafanotemerwa kare. Izvi zvinogona kunge zviri hombe dhizaini kana fungidziro yako ichibva pane zvichangobva kuitika. (Semuenzaniso, kuziva kutengeserana kwehutsotsi nekukurumidza sezvinobvira.)
Iine pedyo-chaiyo-nguva, yekutepfenyura maficha echimwe chinhu, iyo inference inoitwa mukuyerera kwerukova pane yakapihwa seti yezvipo.
- Pano, nekupa iyo modhi chaiyo-nguva, yekufambisa maficha, tinogona kuwana mamwe echokwadi ekufungidzira.
- Nekudaro, zvimwe zvivakwa zvinodiwa pakuyerera kwerukova uye kuchengetedza data hova (Kafka, Kinesis, nezvimwewo). (Apache Flink, Beam, nezvimwewo)
12. Unorevei neKudzidzisa Kushumira Skew?
Kusiyana pakati pekuita paunenge uchishumira uye kushanda panguva yekudzidziswa kunozivikanwa sekudzidzira-kushandira skew. Iyi skew inogona kukonzerwa nezvinhu zvinotevera:
- Musiyano pamabatiro aunoita data pakati pemapaipi ekushumira nekudzidziswa.
- Shanduko yedata kubva pakudzidziswa kwako kuenda kune yako sevhisi.
- Nzira yemhinduro pakati pealgorithm yako uye modhi.
13. Unorevei neModel Registry?
Model Registry inzvimbo yepakati apo vagadziri vemodhi vanogona kuburitsa mamodheru akakodzera kushandiswa mukugadzira.
Vagadziri vanogona kushandira pamwe nezvimwe zvikwata uye vanobatana kuti vatore hupenyu hweese mamodheru mukati mebhizinesi vachishandisa registry. Iwo mamodheru akadzidziswa anogona kuiswa kune yemuenzaniso registry nemusayendisiti wedata.
Iwo mamodheru akagadzirirwa kuyedza, kusimbiswa, uye kuendeswa kune kugadzirwa kana angove murejista. Pamusoro pezvo, mamodheru akadzidziswa anochengetwa mumodhi registries kuti uwane nekukurumidza nechero yakabatanidzwa application kana sevhisi.
Kuti uedze, kuongorora, uye kuendesa modhi pakugadzira, software developers uye vaongorori vanogona kukurumidza kuziva uye kusarudza chete yakanakisa vhezheni yemhando dzakadzidziswa (zvichienderana nemaitiro ekuongorora).
14. Unogona here kutsanangura pamusoro pezvakanakira Model Registry?
Idzi dzinotevera ndidzo dzimwe nzira idzo modhi registry inotsikisa modhi yehupenyu hwekutarisira:
- Kuita kuti kutumira kuve nyore, chengetedza zvinodiwa zvenguva yekumhanya uye metadata yemhando dzako dzakadzidziswa.
- Mamodheru ako akadzidziswa, akaiswa, uye akasiya basa anofanirwa kunyoreswa, kuteverwa, uye kushandurirwa munzvimbo yepakati, inotsvakwa repository.
- Gadzira otomatiki mapaipi anogonesa kuenderera mberi kuburitsa, kudzidziswa, uye kubatanidzwa kweiyo yako yekugadzira modhi.
- Enzanisa mamodheru achangobva kudzidziswa (kana anopikisa mamodheru) munzvimbo yekutandarira kune mamodheru ari kushanda mukugadzira (mamodheru eshasha).
15. Unogona here kutsanangura nzira yeChampion-Challenger inoshanda?
Zvinogoneka kuyedza sarudzo dzakasiyana dzekushanda mukugadzira uchishandisa Champion Challenger maitiro. Iwe unogona kunge wakambonzwa nezve A / B yekuyedza mumamiriro ekushambadzira.
Semuenzaniso, unogona kunyora mitsara miviri yezvidzidzo zvakasiyana uye woigovera chero kune yako chinangwa chedemographic kuitira kuti uwedzere chiyero chakavhurika chemushandirapamwe weemail.
Iyo sisitimu inoisa mashandiro eemail (kureva, email yakavhurika chiitiko) zvine chekuita nemutsara wemusoro wenyaya, zvichikubvumidza kuti uenzanise mwero wakavhurika wemutsara wega wega kuti uone kuti ndeipi inonyanya kushanda.
Champion-Challenger inofananidzwa neA/B bvunzo mune izvi. Iwe unogona kushandisa sarudzo yehungwaru kuongorora mhedzisiro yega yega uye sarudza iyo inoshanda zvakanyanya sezvaunoyedza nenzira dzakasiyana kuti uuye pasarudzo.
Iyo yakanyanya kubudirira modhi inopindirana kune shasha. Wekutanga anokwikwidza uye rondedzero yevanopikisa ndiyo yese iripo muchikamu chekutanga chekuuraya panzvimbo pemukwikwidzi.
Shasha inosarudzwa nehurongwa hwekuwedzera danho rekuita basa.
Vanopikisa vanosiyaniswa mumwe nemumwe. Shasha itsva inozotemwa neanopikisa anoburitsa mibairo mikuru.
Mabasa anosanganisirwa mumakwikwi-anopikisa maitiro akanyorwa pazasi zvakadzama:
- Kuongorora imwe neimwe yeanokwikwidza modhi.
- Kuongorora zvibodzwa zvekupedzisira.
- Kuenzanisa mhedzisiro yekuongorora kuti usimbise anokunda anokwikwidza.
- Kuwedzera shasha nyowani kudura
16. Rondedzera mashandisirwo emabhizimusi-chikamu cheMLOps lifecycle?
Isu tinofanirwa kumira kufunga muchina kudzidza sechiyedzo chekudzokorora kuitira kuti modhi dzekudzidza dzemuchina dzipinde mukugadzira. MLOps mubatanidzwa wesoftware engineering nekudzidza muchina.
Mhedzisiro yakapedzwa inofanira kufungidzirwa saizvozvo. Naizvozvo, iyo kodhi yechigadzirwa chetekinoroji inofanirwa kuyedzwa, inoshanda, uye modular.
MLOps ine hupenyu hunoenderana neyakajairwa muchina kudzidza kuyerera, kunze kwekuti modhi inochengetwa mukuita kusvika kugadzirwa.
Iwo MLOps Mainjiniya anozoramba akatarisa pane izvi kuti ave nechokwadi chekuti mhando yemhando mukugadzira ndiyo inotarirwa.
Heano mamwe ekushandisa-kesi kune akati wandei eMLOps matekinoroji:
- Model Registries: Ndizvo zvazvinoita. Zvikwata zvihombe zvinochengeta uye zvinochengeta mamodheru emhando mumarejistri emhando. Kunyangwe kudzokera kune yakapfuura vhezheni isarudzo.
- Feature Store: Paunenge uchibata neakakura data seti, panogona kuve neakasiyana mavhezheni eiyo analytical datasets uye subsets emamwe mabasa. Chitoro chechimiro inzira yekucheka-kumucheto, inonaka yekushandisa data rekugadzirira basa kubva pakutanga kumhanya kana kubva kune zvimwe zvikwata zvakare.
- Zvitoro zveMetadata: Izvo zvakakosha kuti utarise metadata nemazvo panguva yese yekugadzirwa kana isina kurongeka data, senge mufananidzo uye data data, ichizoshandiswa zvinobudirira.
mhedziso
Zvakakosha kuyeuka kuti, kazhinji, mubvunzurudzo ari kutsvaga hurongwa, asi mumiriri ari kutsvaga mhinduro.
Chekutanga chakavakirwa pahunyanzvi hwako hwehunyanzvi, nepo chechipiri chiri pamusoro penzira yaunoshandisa kuratidza kugona kwako.
Pane nzira dzakati wandei dzaunofanirwa kutora kana uchipindura mibvunzo yeMLOps yekubvunzurudza kubatsira mubvunzurudzo kuti anzwisise zviri nani kuti unoda kuongorora uye kugadzirisa dambudziko riripo.
Kuisa pfungwa kwavo kuri pakuita zvisirizvo pane kuita kwacho. Mhinduro inotaura nyaya, uye system yako ndiyo yakanakisa mufananidzo weruzivo rwako uye kugona kwekutaurirana.
Leave a Reply