- Mapoka ekuongorora emazuva ano anosanganisa maturusi eML ekare (DVC, DeepChecks, maraibhurari ekururama uye kusimba) nemapuratifomu eLLM anobata kuona zvinhu zvisipo, kuchengetedzeka uye mashandiro ebasa evamiririri.
- Mapuratifomu akaita seOpenlayer, LangSmith, Braintrust, Arize Phoenix, Maxim AI naLangfuse akasiyana pakutarisa—kutonga, kucherechedzwa, code‑first kana open source—saka kusarudza zvishandiso zvinoenderana zvakanyanya nezvinodiwa nechikwata.
- Vaongorori vakagadzirira mabhizinesi vanobatanidza bvunzo, kucherechedzwa uye kutonga mukushanda kumwe chete, zvichiita kuti kuongororwa kwakaitwa, kuongororwa, uye kudzokororwa kwemaitiro eML neLLM echinyakare.
- Sezvo maLLM achipa simba kuRAG, vamiririri uye maturusi ekodhi anotungamirwa neAI, kuongororwa kwakarongeka muNLP yese, mabhenji einjiniya yesoftware uye telemetry yekugadzira zvinova zvakakosha pakuvimbika uye kutevedzera mitemo.
Mapuratifomu ekuongorora mamodheru emitauro akavhurika akawedzera mumhando dzakasiyana-siyana uye hunyanzvi, uye nhasi ndiwo ari pakati pemhando dzese dzeAI dzakakomba. Matimu haachatumire mamodheru emitauro mikuru (LLMs) kana vamiririri kana vachinzwa vari vega: vanoda kuyedza kunodzokororwa, zviyero zve otomatiki, kuongororwa kwekururama, kucherechedzwa, uye kutonga kunomira nekuongorora. Kubva pakushandisa ML tooling yekare senge DVC kana TensorBoard kusvika kune vanoongorora LLM vatsva vakaita seOpenlayer, LangSmith kana Arize Phoenix, ecosystem yave yakaoma uye dzimwe nguva inovhiringidza.
Chinyorwa chino chinosanganisa nzwisiso kubva muzvishandiso zvakawanda zvemutauro weChirungu nematurusi ekugadzira mapuratifomu akavhurika uye ekutengeserana asi ari nyore kugadzira ekuongorora mamodheru emitauro nemasisitimu ehunyanzvi. Tichatarisa kuyedzwa kwemuenzaniso nedata, maraibhurari ekururama uye kusimba, marongero eLLM sekutonga, mapuratifomu ekucherechedza mabhizinesi, uye mhinduro dzakazara dzinobata masisitimu eAI senge software yekugadzira. Munzira, uchaona kuti ndeapi maturusi anokodzera vamiririri veML vekare neLLM, kuti vanoenzanisa sei, uye kuti vanobatana sei nema workflows chaiwo.
Kubva pakuyedzwa kweML kwekare kusvika kuLLM yemazuva ano uye kuongororwa kwevamiririri
MaLLM asati atanga kutariswa, kuongororwa kweAI kwaive pamusoro pemamodheru aitarisirwa, madata akarongwa, uye zviyero zvakanyatsotsanangurwa zvakaita sekururama, AUC kana F1. Zvishandiso zvekare zvakaita seTensorBoard, Weka neMockServer zvakabatsira matimu kuona maitiro ekudzidzira, ma prototype models, uye ma test APIs, asi hazvina kugadzirwa kuti zvive nyore kugadzira, kuona zvinhu zvisipo kana kufunga zvinhu nenzira dzakawanda. Nekufamba kwenguva, musiyano uyu wakakonzera mashandisirwo eMLOps akatarisana nekushandura, kudzokorodza, kururamisira uye kusimba.
Munguva yekukura kweMLOps (inenge 2020-2022), maraibhurari akadai seDVC, DeepChecks, Aequitas, Fairlearn naAdversarial Robustness Toolbox akava bhokisi rezvishandiso zvemapaipi eML akavimbika. DVC yakaunza shanduro dzakaita seGit dzedata nemamodheru, DeepChecks automated data uye model sanity checks, Aequitas neFairlearn vakatarisa pakusarura nekururamisira, ukuwo ART yakatevedzera kurwisa kwevavengi kumamodheru mumaframework akaita sePyTorch, TensorFlow kana XGBoost. Zvishandiso izvi zvakaisa hwaro hwakawanda hwepfungwa iyo mapuratifomu ekuongorora eLLM emazuva ano ikozvino anoshandisazve uye anowedzera.
Muchizvarwa chemazuva ano, ongororo yachinja ichienda kumashoko asina kurongeka, hurukuro dzema "multi-turn dialogue", "retrieval-augmented generation" (RAG), uye "agent workflows" dzinoshandisa maturusi nemaAPI. Mapuratifomu matsva akadai seGiskard, ChainForge, EvalAI neBIG-bench airatidzika kunge akaenzana neLLMs mukufunga, kuchengetedzeka uye hunyanzvi hwenzvimbo, nepo mapuratifomu ekutengeserana akadai seOpenlayer, LangSmith, Braintrust, Arize Phoenix kana Maxim AI ikozvino anopa ma stacks akabatanidzwa ekuyedza, LLM semutongi wekuongorora, kutarisa uye kutonga.
Panguva imwe chete, mapuratifomu eNLP akafanana—Google Cloud Natural Language, IBM Watson NLU, Azure Text Analytics, Amazon Comprehend, spaCy, Stanford NLP, Hugging Face Transformers, TextRazor, MonkeyLearn kana Gensim—anoramba achisimudzira kupatsanurwa kwemavara, ongororo yemanzwiro, kutevedzera misoro uye kubviswa kwezvinhu padanho repamusoro. Aya haasi mapuratifomu ekuongorora chete, asi anowanzova ndiwo musoro wenyaya uye maturusi ekuongorora: mapoka anoashandisa kuvaka masisitimu uye dzimwe nguva kunyora mazita kana kuwana mamakisi kubva kune mamwe mamodheru.
Zvivako zvikuru: shanduro, mhando yedata uye zviyero
Chero gadziriro yekuongorora mhando yemutauro yakasimba inotanga nezvinhu zvekutanga: kuyedza kwakagadziriswa, data rinoteverwa, uye zviyero zvinodzokororwa. Pasina hwaro uhwu, mazano epamusoro akadai sekutsvaga agent kana LLM semutongi anokurumidza kuparara nekuti haugone kuziva zvakachinja pakati pema "run" maviri kana kuti nei functionality yakaderera.
DVC (Data Version Control) ndeimwe yenzira dzakakosha dzekushandisa ruzivo urwu. Inounza shanduro yeGit-style kumaseti edata uye mamodheru emhando, inotsigira mapaipi anotsanangura kuti data raw rinoshandurwa sei kuita data rekudzidzisa uye mamodheru, uye inoteedzera metrics uye checkpoints nekufamba kwenguva. Kune mamodheru emitauro, unogona kushandisa DVC kuomesa mufananidzo chaiwo wedata rako rekudzidzisa, prompt templates, evaluation corpora uye metrics, ichiva nechokwadi chekuti run yega yega inodzokororwa.
TensorBoard inoramba iri chinhu chakakosha pakuona, kunyanya pakudzidzisa mamodheru akadzika eNLP kana kugadzira makodhi. Inokubvumira kuti utarise macurve ekurasikirwa, kururama, magradients uye pfupiso dzemagwaro akagadzirwa panguva yekudzidziswa. Kunyange zvazvo isina kugadzirwa zvakananga kuongororwa kweLLM, inowanzo gara iri muchimiro chekufungidzira kuyedza pamwe chete nemadashboard matsva ekuongorora.
Mapuratifomu ekuenzanisa akadai seEvalAI, BIG-bench kana D4RL (yekudzidza kwekusimbisa) anopa madhata akagovaniswa uye kuongororwa kwemaitiro ebhodhi rekutungamira emitauro nemamodheru eRL. Kune maLLM anotarisa kodhi, SWE-bench nezvimwe zvakafanana zvave zvakakosha: zvinotevedzera mabasa ekugadzira software chaiwo apo mamodheru anofanirwa kuverenga, kugadzirisa uye kufunga munzvimbo dzese dzekuchengetedza. Mapuratifomu mazhinji ekuongorora emazuva ano anobatanidzwa zvakananga muzviyero izvi zveveruzhinji kana kuratidza maitiro avo kugadzira ma test suites emukati.
Pamusoro pezviyero zveveruzhinji, zvikwata zviri kuwedzera kuunganidza seti dzekuongorora dzakavanzika dzakagadzirirwa nzvimbo yavo - magwaro epamutemo, mishumo yemari, zvinyorwa zvekurapa, kana magwaro ekunyora - uye vozvibatanidza muzvishandiso zvekuedza zvinozvishandira. Mamwe mapoka anozvivakira zvivakwa izvi nema script nema dashboard, nepo mamwe achishandisa mapuratifomu ekuongorora akasarudzika akadai seOpenlayer, Braintrust, LangSmith kana Maxim AI kuti atarisire ma datasets, metrics uye test runs nenzira inokwanisika kukura.
Kusimbiswa kwedata, mhando yemhando uye kururamisira kweNLP neLLMs
Mapoka eML echinyakare ave achivimba nekusimbisa data uye kuona mafambiro edata kuti abate kutadza kwakanyarara, uye pfungwa idzi dzinoshandura zvakananga mukuongorora kweLLM—kunyangwe data iri kunyanya kunyorwa. Zvishandiso zvakaita seDeepChecks zvichiri kukosha: zvinogona kuona kuchinja kwekugoverwa kwezvinhu zvemavara, kusarongeka muma label, kana shanduko mukuoma kwebasa izvo zvaizokanganisa metrics.
DeepChecks inopa kuongororwa kwedata nemamodheru asati adzidziswa uye mushure mekudzidziswa, ichiratidza matambudziko akadai sekubuda kwelabel, kuchinja kwecovariate, kana hukama husingatarisirwi pakati pezvinhu zvinopinda uye fungidziro. Panyaya dzekushandiswa kwemitauro, izvi zvinogona kuoneka kuti ruzivo rwako rwekudzidzisa modhi yemanzwiro runodzorwa nemutsara mumwe wechigadzirwa, kana kuti mamwe mazwi anowirirana zvakanyanya nechiratidzo chakati netsaona, zvichikonzera kufanotaura kwakarerekera.
Weka, kunyange zvazvo ari mukuru uye aine ruzivo rwakawanda, achiri kuita basa rinobatsira pakugadzira nekukasika uye kudzidzisa nezvekupatsanura magwaro, hunyanzvi hwekugadzira zvinhu, uye kuenzanisa kuongorora. Iyo graphical interface inobatsira vasiri nyanzvi kunzwisisa kunyatsojeka, kurangarira, maROC curves uye confusion matrices, pfungwa dzinoramba dzichikosha kana ukaongorora mapaipi akaomarara eLLM.
Maraibhurari eruenzaniso rwakarurama akadai seAequitas neFairlearn akakosha pese panobata mitauro yakaita sehutano, mari, kuhaya kana kururamisira. Aequitas inotarisa pakuongorora rusaruro mumapoka akachengetedzwa, ma computing group uye disparity based metrics kuitira kuti uone kana text classifier yako kana ranking model inobata demographics dzakasiyana nguva dzose. Fairlearn inoita nhanho imwe nekupa ma retivation algorithms anokutendera kuti uchinje kururama kwese uye zvipingamupinyi zvekururamisira.
Bhokisi reZvishandiso zveKusimba kweVadzivisi (ART) rinowedzera ongororo munzvimbo dzekuchengetedza nekusimba, richitevedzera kurwiswa kunoedza kusundidzira mamodheru mukusarongeka kana maitiro anokuvadza. Kunyange zvazvo mienzaniso yakawanda yakanyorwa iri mifananidzo kana matafura, misimboti imwecheteyo inoshanda zvakanyanya kuNLP neLLMs—kupinza nekukurumidza, kukanganisa rugwaro rwemushandisi, kana mienzaniso yevanopikisa yakagadzirirwa kunzvenga mafirita ezvinyorwa. ART inobatsira mapoka kuona kuti mamodheru avo haana kusimba sei pakugadzirisa kwakadaro.
Vaongorori veLLM: LangSmith, Braintrust, Arize Phoenix, Galileo, Fiddler, Maxim AI uye magadzirirwo akagadzirwa nemaoko
Paunongochinja kubva pakushandisa ML yekare kuenda kuLLM - machatbots, masisitimu eRAG, maagents - miganhu yekushandisa ML evaluation tooling inova pachena. Zviyero zvakaita seBLEU kana ROUGE zvinokundikana kubata hunhu hwemashoko, kururama kana kuchengetedzeka kwemavara akagadzirwa nechimiro chakasununguka, uye bvunzo dzemayuniti hadzina kukwana kusimbisa zvinhu zvine matanho akawanda. Apa ndipo panopinda mapuratifomu ekuongorora akatarisana neLLM.
LangSmith yakabatana zvakanyanya neLangChain uye inoyevedza kumapoka anovaka maLLM application pamusoro pehurongwa ihwohwo. Inopa kuteverwa kwezvikumbiro, matanho ari pakati uye mafoni ekushandisa, inokutendera kuti utarise ma "agent runs" ese, uye inotsigira ma "evaluation runs" padatasets apo ma outputs anowanikwa ne "heuristics", "labels" kana "LLM" se "a judge". Dambudziko rayo guru nderekuti inonzwa yakamanikidzwa kana usiri kushandisa LangChain zvakanyanya kana kuti uchida nzira isina kujeka.
Braintrust ipuratifomu inotarisa vagadziri vemapurogiramu inoongorora nekuyedza otomatiki. Zvinoita kuti zvive nyore kutsanangura madata ekuongorora, kuisa mabasa ekukora (kusanganisira LLM semutongi), uye kuita ongororo dzakawanda mumamodheru akasiyana-siyana kana kuti zvinokurudzira. Zvakasimba kumapoka einjiniya anoda kunyora mashandiro avo uye kubatana zvakadzama muCI/CD, kunyange zvazvo isina kunyanya kutarisa pazvigadzirwa kana mabasa evanobata vakawanda.
Arize Phoenix inomiririra nzvimbo yakavhurika yeArize AI's observability stack, ichipa ruzivo rwakakura rwekunyora, kutevedza uye ongororo yemasystem eML neLLM. Phoenix inonyanya kuratidza maitiro anoita mamodheru mukugadzira: unogona kuongorora latency, maitiro ekukanganisa, kugoverwa kwe embedding, uye kutopinda mumaboka ekukundikana. Chinangwa chayo chinonyanya kutarisa kune mamodheru emazinga emuenzaniso uye kutaridzika kukuru pane kurongeka kwemabasa emumiriri akacheneswa.
Galileo anotarisira kuongororwa nekukurumidza, kunobva mudata uye kuyedza pane kungotarisa hupenyu hwese hwemuenzaniso. Zvinorerutsa kugadzirisa ongororo nekukurumidza pamusoro pemadata ezvinyorwa ane mazita, kuratidza nzvimbo dzinokanganisa uye kukupa ruzivo rwekuti mamodheru ako anokundikana kupi. Chinochinjana ndechekuti Galileo haaedzi kufukidza chikamu chega chega chehupenyu hweAI, saka unowanzoibatanidza nezvimwe zvishandiso zvekutarisa nguva yekushandisa kana kutonga.
Fiddler inopa kucherechedzwa uye kutevedzera mamodheru ebhizinesi, akavakirwa zvakanyanya muML yechinyakare asi anonyanya kukosha pakushandiswa kweLLM. Inopa kutarisa, kuona kusvetuka kwemvura, tsananguro uye nzira dzekuongorora, zvichiita kuti ive inokwezva zvikuru kumaindasitiri anotongwa nemutemo. Zvisinei, chinangwa chayo chekare chiri paML yetafura uye yekare pane masisitimu emagetsi kana mapaipi ekukurumidza akadzika midzi.
Maxim AI inosimudzira nzira yekubatanidza zvese: kugadzira shanduro nekukurumidza, kuyedza usati watanga uye mushure mekuvhura, kutevedzera, kuongorora nenzwi, uye kucherechedzwa munzvimbo imwe chete. Yakagadzirwa zvakajeka kuitira kuti mainjiniya nevatariri vezvigadzirwa vagone kushanda pamwe chete pakuongorora nekugadzirisa. Sepuratifomu itsva uye yakatarisana nemabhizinesi, inokwikwidza uko masangano anoda hutongi, kushandira pamwe uye kuyedzwa kwegiredhi rekugadzira pane kungova matoyi evagadziri chete.
Mamwe mapoka anosarudza kugadzira ongororo yavo vega vachishandisa marogging, ma dashboard uye LLM-as-a-judge scripts zvakasonerwa pamwe chete ne custom code. Izvi zvinogona kuchinjika zvakanyanya—unogona kugadzirisa zviyero, nzvimbo yekuchengetedza uye kuona zvinoenderana nezvaunoda—asi mari yekugadzirisa uye kuomarara kwakavanzika kunowedzera nekukurumidza. Nekufamba kwenguva, akawanda eaya magadzirirwo emumba anogona kuchinja kuita chimwe chinhu chakafanana nepuratifomu yemukati kana kuti anotsiviwa nezvishandiso zvisiri pasherufu kana kuwedzera uye kutevedzera mitemo kwava kunetseka kukuru.
Kana zvikaonekwa pamwe chete, gwara rakasununguka rinobuda: kana chinangwa chako chiri chetsika dzeML, zvishandiso zvakaita seFiddler, Galileo naArize zvinopenya; kana uri kugadzira maapplication nevamiririri veLLM, LangSmith, Maxim AI naBraintrust vanowanzo enderana zviri nani; uye kana mashandiro ebasa akasiyana-siyana achikosha, Maxim AI nemapuratifomu akafanana anosimbisa kushandira pamwe anowanzo kunda.
Openlayer: puratifomu yakabatana yekuongorora uye yekutonga yeLLMs neML
Openlayer ndeimwe yenzira dzakasimba dzekushandura LLM neML kuongororwa kuita hunyanzvi hwepamusoro, hwakarongeka hweinjiniya pane kuunganidzwa kwezvinyorwa nemadhibhodhi zvisina kurongeka. Panzvimbo pekubata mamodheru semabhokisi matema anoedzwa dzimwe nguva, Openlayer inoabata sesoftware: ane mavhezheni, bvunzo, integration inoenderera mberi uye mamiriro ekupasa/kukundikana akabatana neshanduko yega yega.
Chimwe chinhu chinowanzo vhiringidza izita rekuti: "Openlayer" pano rinoreva puratifomu yekuongorora nekutonga kweAI, kwete "OpenLayers", raibhurari yeJavaScript inovhura-sosi yemamepu anopindirana. Kuzvisanganisa kunogona kukutungamira kumagwaro kana mapakeji asiri iwo, saka zvakakosha kuti urambe uchifunga nezvemusiyano uyu pese paunotsvaga kana kubatanidza.
Pachinyanya kukosha, Openlayer inopa puratifomu yakabatana inofukidza zvinhu zvitatu muhurongwa hweAI: kuongorora, kutarisisa uye kutonga. Inotsigira mamodheru eML ekare uye masisitimu emazuva ano eLLM, anosanganisira maRAG pipelines uye multi-step agents. Kukosha kwayo kuri nyore asi kune simba: tsiva kugadzirisa nekukurumidza uye kutarisa zvisina kurongeka nemapoinzi ekuongorora akarongeka, anotungamirirwa nedata anotaridzika uye anonzwa sekuyedza software yemazuva ano.
Chinyorwa chekuongorora chinopa raibhurari yakakura yemiedzo inogadziriswa—inopfuura zana, maererano netsananguro dzeveruzhinji—ichifukidza nyaya dzakadai sekuona zvinhu zvisipo, kubuda kwePII, chepfu, rusaruro, chokwadi uye kutevedzera mitemo yebhizinesi. Chinhu chikuru iLLM semutongi: Openlayer inogona kudana LLM yakasimba kuti iongorore zvinobuda mumodhi yako zvichienderana nemitauro yechisikigo, ichipa mamakisi akakwana ehukuru hwakadai sekururama, kutendeka kune zviri kutaurwa, hunhu hwakanaka, kana kupedzisa basa.
Mbiru yekutarisa inotarisa pane zvinoitika mukugadzirwa: kurondwa kwakadzama kwechikumbiro chega chega, kuteverwa kwematanho ese mumabasa akaomarara emumiriri, zviyero zvakaita sekunonoka, mutengo, uye kudonha kwedata, uye kuzivisa kana zvinhu zvikasafamba zvakanaka. Izvi zvinoita kuti zvikwanisike kubatanidza maitiro epanguva yebvunzo nemaitiro aripo, kuona kudzoka kwezviitiko nekukurumidza, uye kuferefeta zviitiko zvine mamiriro akazara kuburikidza nekukumbira, magwaro akatorwa, maturusi ekufona uye zvinobuda.
Nheyo yekutonga inotaura zvakananga nezvezvinodiwa nemabhizinesi: kutonga kwekuwana, magwaro ekuongorora, kutevedzera mitemo yeSOC 2 Type II, SAML SSO, uye kuvharwa kwedata riri kufambiswa uye riri paAWS infrastructure. Pane kuti ive pfungwa yekuzotevera, hutongi hwakavakirwa pamaitiro ekutarisira mapurojekiti, datasets, bvunzo uye mamodheru, izvo zvinonyanya kukosha kumaindasitiri ari kutarisana nemitemo mitsva uye hurongwa hwemukati hweAI risk.
Zviri pachena kuti Openlayer yakanangana nemapoka akasiyana-siyana: masayendisiti edata nemainjiniya eML vanosimbisa mhando yemuenzaniso, vatariri vezvigadzirwa vanotevera zviyero zvine chekuita nebhizinesi uye nzira dzekukundikana, uye vatungamiriri veinjiniya kana maCTO vanoshandisa madhibhodhi nemishumo kuti vagadzirise njodzi nekutevedzera mitemo. UI inogadziriswa nemaune kuti ive nyore kusvika kune vasiri mainjiniya, nepo maSDK nemaAPI zvichibvumira vagadziri kuisa ongororo muCI/CD uye maturusi akagadzirwa.
Panyaya yemitengo, Openlayer inotevera modhi yefreemium ine Basic/Trial tier inopa mukana wepamusoro wekufungidzira pamwedzi pamwe nekuwana mukana wekuenda kuraibhurari yekuongorora uye kucherechedzwa kwepakati. Masangano makuru anogona kushandisa zvirongwa zvemabhizinesi zvinowedzera zvinhu zvakaita sekudzora mukana wekuwana mabasa, sarudzo dzekuisa zvinhu panzvimbo uye rutsigiro rwakatsaurirwa; mitengo yezvikamu izvozvo inowanzo tauriranwa kuburikidza nekutengesa.
Maitiro eOpenlayer anopesana nevamwe vanoongorora LLM
Nekuti Openlayer iri munzvimbo yakazara vanhu uye inofamba nekukurumidza, zvinobatsira kuienzanisa zvakananga nedzimwe nzira dzinozivikanwa: Confident AI (inotsigirwa ne open-source DeepEval framework), Arize AI naLangfuse. Mumwe nemumwe anosvika padambudziko iri neparutivi rwakasiyana—kuongorora kutanga, kucherechedzwa kutanga kana kuti kuvhura-sosi kutanga—uye sarudzo chaiyo inoenderana zvakanyanya nezvaunokoshesa.
AI ine chivimbo, yakavakirwa pamusoro peDeepEval, inotsamira paruzivo rwevagadziri vekodhi yekutanga apo bvunzo dzinoitwa zvidimbu zvePython uye metrics dzinotsanangurwa mukodhi. Inorumbidzwa nekuita kuti zvive nyore kugadzira zviyero zvekuongorora zvakagadzirwa, kusanganisira zvekushandisa zvinhu zvakasiyana-siyana uye zvekushandisa zvakasiyana-siyana, uye nekugadzira mishumo yakadzama yeA/B test. Zvichienzaniswa neizvi, Openlayer inonzwa sechigadzirwa chakazara: chakareruka, asi chakabatana uye chine hushamwari kune timu dzinoshanda pamwe chete.
Arize AI yakatanga senzvimbo ine simba rekuona ML zvakanyanya uye kubvira ipapo yakawedzera kusvika pakuongorora LLM uye kuongorora vamiririri. Inonyatsogona kugadzirisa zviitiko zvakawanda zvekugadzira, kutarisa mafambiro ebasa uye mashandiro, uye kupa ongororo yezvikonzero. Chirongwa chayo chePhoenix chinopa zvikwata mukana wekuzvigamuchira uye wakareruka wekushanda ikoko. Kusiyana neizvi, Openlayer inoisa ongororo nekutonga pedyo nepakati, nepo kutarisisa—kunyangwe kwakasimba—kuri imwe yenzvimbo dzakati wandei.
Langfuse inotora nzira yakasiyana nezvigadzirwa zvakawanda zveSaaS: inovhura-vhura zvizere pasi perezinesi rinobvumidza (MIT) uye inozivikanwa zvikuru pakati pezvikwata zvinoda kutonga uye pachena. Inopa kuronda, kunyora pasi uye kuongorora maapplication eLLM uye inogona kuzvishandira yoga. Kune masangano anoda kudzivirira vatengesi kuti vasavharire uye vachifara kugadzirisa ma infra awo, Langfuse inoyevedza. Openlayer panzvimbo pezvo inosarudza nzvimbo yekutengeserana ine vatengi vanovhura masource uye kubatanidzwa, ichichinjana pachena zvizere kuti iwane ruzivo rwakanaka uye runotsigira SaaS uye maficha ebhizinesi.
Muchidimbu, Openlayer inonyanya kukodzera kana uchida nzvimbo yakabatana, inotongwa inobata ongororo, kutarisa uye kutevedzera mitemo pamwe chete, kunyanya munzvimbo dzakarongwa kana dzine njodzi. Kana uchinyanya kuda kuchinjika kwevagadziri vemapurogiramu uye kukweshana kushoma, DeepEval/Confident AI inogona kunzwa yakareruka; kana uchida telemetry yakakura uye uine MLOps dzakasimba, Arize inogona kuva yakanaka; uye kana kutonga uye open source zvisingakurukurwe, Langfuse yakaoma kukunda.
Kuongororwa kweRAG nema agents neOpenlayer
Kuti unzwisise kuti kushanda nemuongorori wemazuva ano kunotaridzika sei mukuita, fungidzira uri kuyedza sisitimu yekutora zvinhu zvakawedzera (RAG) yakavakwa nehurongwa hwakaita seLlamaIndex kana LangChain. Une mibvunzo yakarongwa, ndima dzenyaya dzakatorwa kubva muchitoro chako chemagwaro, mhinduro dzemuenzaniso wako, uye chokwadi chakanyorwa nevanhu. Unoda kuziva: mhinduro dzinoenderana here nemamiriro ezvinhu, dzinoita kuti vanhu vafunge zvinhu zvisipo here, uye marongero akasiyana ekutora kana kukurumidza kuita zvinhu anokanganisa sei mashandiro uye mutengo?
MuOpenlayer, danho rekutanga kugadzira purojekiti kuburikidza neUI kana SDK, kutsanangura rudzi rwebasa (semuenzaniso LLM) uye tsananguro pfupi. Tevere, unoisa data rako rekusimbisa—kazhinji DataFrame ine makoramu akadai semubvunzo, mamiriro ezvinhu, mhinduro uye ground_truth—wobva wamaka kuti ndeapi makoramu anoenderana nezvinopinzwa, zvinobuda uye mareferensi. Openlayer inochengeta izvi sedata rakagadziridzwa raunogona kushandisazve mukudzokorora kwemuenzaniso.
Wobva watsanangura magadzirirwo emuenzaniso; yeRAG, unogona kubata pipeline se "shell" model, zvichireva kuti Openlayer haizoishandise zvakananga asi ichagamuchira zvinobuda zvayo uye ichazvibatanidza nemuenzaniso iwoyo. Metadata inogona kutsanangura zvinhu zvakaita sehukuru hwechunk kana ma embedding models, izvo zvinozokubatsira kubatanidza shanduko mu evaluation metrics ne configuration tweaks.
Chinhu chinonakidza chinouya kana uchigadzirisa bvunzo—kunyanya bvunzo dzeLLM semutongi dzinopa mamakisi akasiyana nezvinodiwa nemutauro chaiwo. Semuenzaniso, unogona kutsanangura bvunzo ye "kutendeka" inobvunza mutongi LLM kuti aone kuti mhinduro yega yega inonamira sei mumamiriro ezvinhu akapihwa uye kuti ape mhosva ruzivo rusina kutsigirwa. Unogona kuwedzera bvunzo dzekuchengetedza kana paine chepfu kana kubuda kwePII, bvunzo dzekubatsira, kupfupika, kana mitemo chaiyo yedomain.
Pakupedzisira, unozvipira wobva wasundidzira gadziriro iyi, uchitanga ongororo; mushure mekuitwa, dashboard yeOpenlayer inoratidza kuti ndedzipi bvunzo dzakapasa kana dzakakundikana, mamakisi akabatanidzwa, uye kupatsanurwa kwemuenzaniso. Unogona kutsvaga nyaya dzakakundikana kuti uone mubvunzo wekutanga, mamiriro akadzoserwa, mhinduro yako, chokwadi chepakutanga uye kufunga kwemutongi, wobva wadzokorora pane zvinokurudzirwa, nzira yekutora kana sarudzo yemuenzaniso. Nekuti kumhanya kwega kwega kwakagadzirwa, unogona kuenzanisa mamodheru kuburikidza nemabasa, sekunge kuenzanisa kuvaka mukubatanidzwa kunoramba kuripo.
Mashandisirwo eNLP akafara: maAPI egore, maraibhurari e-open-source uye mapuratifomu asina makodhi
Kuongororwa kwemuenzaniso wemutauro hakupo pasina chinhu: kuri pamusoro pe, uye kazhinji mukati, penzvimbo yakapfuma yeNLP APIs nemaraibhurari. Zvishandiso izvi ndizvo zvaunoshandisa kuvaka masisitimu ako, asi zvinogonawo kushandiswa kugadzira mazita, kugadzira data risati ragadziriswa, kana kuona zvinhu nemanzwiro sechikamu chehurongwa hwekuongorora.
Ma Cloud API akadai seGoogle Cloud Natural Language, IBM Watson Natural Language Understanding, Microsoft Azure Text Analytics neAmazon Comprehend anopa masevhisi akadzidziswa kare emanzwiro, kuzivikanwa kwenhengo, kubviswa kwemashoko akakosha, kuongorora masintakisi, kupatsanura magwaro nezvimwe. Zvinokura zviri nyore, zvinobatana nehurongwa hwegore hwakakura, uye kazhinji ndiyo nzira inokurumidza yekuti mabhizinesi awedzere kunzwisisa magwaro ekutanga kune zvigadzirwa.
Maraibhurari emahara akadai sespaCy, Stanford NLP, Hugging Face Transformers, TextRazor naGensim anopa huwandu hwakawanda hwemasisitimu eNLP akagadzirwa nevanhu. Opciones para alojar modelos de lenguaje con bajo presupuesto. spaCy yakagadzirirwa mapipeline ekugadzira uye inotsigira tokenization, POS tagging, dependency parsing uye named entity recognition ine mamodheru anokurumidza, ane simba reindasitiri. Stanford NLP inopa suite yekutsvagisa yekuongorora kwakadzama kwemitauro, nepo Transformers ichigadzira mamodheru epamusoro-soro akadzidziswa kare ekushandura, kupfupisa, Q&A nezvimwe zvichingodaro. Gensim inonyanya kugadzira misoro uye kufanana kwemagwaro, uye TextRazor inosanganisa kuburitswa kwezvikamu, kuburitswa kwehukama uye kupatsanurwa kwemisoro.
MonkeyLearn nedzimwe mapuratifomu akafanana asina kodhi kana kodhi yakaderera anovhura ongororo yemagwaro kumapoka asiri ehunyanzvi nekubatanidza maclassifiers, sentiment analyzers uye keyword extractors kumashure kwevisual interfaces. Kunyangwe zvisiri mapuratifomu ekuongorora, anowanzo shandiswa kugadzira ma prototype labelers kana kugadzira kutarisirwa kusina kusimba kunopa mukana wekuongorora kana kutarisa masisitimu epamusoro-soro.
Mumaindasitiri ese, NLP neLLM zvakabatanidzwa zvakanyanya muzvikamu zvekuongorora: makambani anoashandisa pakuongorora manzwiro padanho repamusoro, triage uye routing yetikiti, kuona misoro, kubvisa zvinhu zveruzivo, kupfupisa mishumo mirefu, kuona hutsotsi zvichibva pamapatani ezvinyorwa, uye kuongorora inzwi kubva kune zvinyorwa kunzvimbo dzekutaurirana. Imwe neimwe yeiyi mienzaniso yekushandisa inobatsira kubva mukuongorora kwakarongeka—zvose zviyero zvekare uye bvunzo dzinoziva LLM—kuona kuti kuvimbika, kururamisira uye kusimba.
Zvishandiso zvekuongorora kodhi, kuyedza kunotungamirirwa neAI uye chinongedzo chekuongororwa kweLLM
Mienzaniso yemitauro iri kuramba ichibatanidzwa muhupenyu hwekugadzira software—kwete sevashandi vekunyora makodhi chete, asi sezvishandiso zvekugadzira bvunzo, kudzokorora makodhi uye kufunga nezvenzvimbo dzekuchengetedza. Saka kuongorora mamodheru aya kunosangana zvakanyanya neongororo yekare yekodhi uye maturusi ekushandisa otomatiki ekuedza.
Zvishandiso zvekuongorora makodhi zvechinyakare nezvemazuva ano—Bhodhi rekuongorora, Crucible, zvikumbiro zveGitHub pull, Axolo, Collaborator, CodeScene, Visual Expert, Gerrit, Rhodecode, Veracode, Reviewable uye Peer Review yeTrac—zvinotarisa pakuita kuti ongororo yevanhu ishande zvakanaka uye yakarongeka. Vanotsigira macomments ari mukati, maonero akasiyana, zviyero pakuongorora, uye kubatanidzwa nemaitiro ekudzora shanduro uye masisitimu eCI. Mamwe, seCodeScene, anowedzera ongororo yemaitiro uye kuona nzvimbo dzinowanikwa vanhu vachishandisa machine learning pamusoro penhoroondo yekudzora shanduro.
Mabhuku ekutsvagisa anotarisisa ramangwana kubva kumayunivhesiti (semuenzaniso Purdue kana Missouri) anosimbisa kukosha kwekuongorora kwakasimba, kwezvibodzwa zvakawanda pakusarudza maturusi ekuyedza AI—kutarisa mashandiro, kudzama kwekubatanidza, kuchengetedza, ruzivo rwevagadziri uye kukosha. Kufunga kumwe chete uku kunoshanda zvakananga kumapuratifomu ekuongorora eLLM pachawo: haafanire kutongwa kwete chete nematanho avanoverenga, asiwo kuti anobatanidzwa zvakanaka sei muzvirongwa zvako zvekuvandudza nekutumira.
Sezvo maLLM achitora chikamu chikuru chehupenyu hwesoftware—kuverenga nekugadzirisa kodhi, kunyora bvunzo, matambudziko ekuongorora—kuongorora kunofanira kusanganisira zviyero zvemutauro wepanyama uye zvepfungwa dzekodhi, zvakaita seSWE-bench uye mabasa ekunzwisisa repository-scale. Mapuratifomu ekuongorora emazuva ano ari kuwedzera kushandisa zviyero izvi zvekunyora makodhi kuti vaone kuti mamodheru anoshanda sei nemapurojekiti esoftware chaiwo.
Tichidzokera shure, nzira yekuongorora mashandisirwo emitauro neoruzhinji ikozvino inosanganisira zvese zvinotevera: maraibhurari ekuongorora eML, zvishandiso zvekuchengetedza kodzero neushingi, vanoongorora LLM neLLM semutongi, mapuratifomu ekuona akawanda, kutevedza mashandisirwo emashoko neSaaS inotarisa pakutonga. Kune mabasa akawanda eML, zvishandiso zvakaita seDVC, DeepChecks, Aequitas, Fairlearn, ART, Fiddler, Galileo naArize zvinoramba zviri zvinhu zvakakosha; kune vamiririri veLLM uye masisitimu eRAG, mapuratifomu akadai saLangSmith, Braintrust, Arize Phoenix, Maxim AI, Openlayer naLangfuse anopa nzira yekuedza, kutarisa uye kutonga maitiro akaomarara. Matimu akasimba anosanganisa uye anobatanidza zvikamu izvi, achibata masisitimu eAI nehunyanzvi hwakafanana nesoftware yemazuva ano - yakagadzirwa, inoonekwa, inoongororwa uye inoongororwa nguva dzose.
