Nlele nsochi
Nleba ule bụ omume nke ịdekọ usoro mmụta igwe ọ bụla - koodu ya, data, hyperparameters, metrics, na nsonaazụ ya - yabụ nsonaazụ ya nwere ike imepụtaghachi yana atụnyere ya.
Nchịkọta
Without it, the question 'which version was best and how did we get it?' becomes nearly impossible to answer.
Ime miri emi
Ịzụ ihe nlereanya adịkarịghị usoro otu oge. Otu dị iche iche na-eme ọtụtụ narị ma ọ bụ puku kwuru puku nnwale, na-agbanwe ọnụego mmụta, nha batch, ụkpụrụ ụlọ, na nhazi data. Nleba nyocha na-ejide akara mkpịsị aka zuru oke nke ọsọ ọ bụla: Git nke koodu ahụ, hash nke dataset, hyperparameter ọ bụla, metrics na-agafe oge (mfu, izi ezi, F1), ozi sistemụ dị ka ụdị GPU, na arịa dị ka ihe atụ echekwara na atụmatụ. Ngwa dị ka MLflow, Arọ & Biases, Neptune, na Comet na-abanye nke a na-akpaghị aka site na ahịrị ole na ole nke oku API. Nkwụghachi ụgwọ ahụ bụ mmeghari (ị nwere ike ịmegharị nhazi nhazi nke emeri kpọmkwem), ntụnyere (ụdị na nzacha na-agba n'akụkụ n'akụkụ), na imekọ ihe ọnụ (ndị otu na-ahụ ihe a nwalere). Ọ na-atụgharị nnwale ad-hoc ka ọ bụrụ akụkọ a na-enyocha, enwere ike ịchọgharị.
Nghọta nka nka
Ọtụtụ ndị na-egwu egwu na-arụ ọrụ site na itinye oku nbanye n'ime loop ọzụzụ. A na-emepụta ọsọ, a na-abanye n'otu oge, na metrics na-abanyekwa ugboro ugboro n'otu nzọụkwụ ma ọ bụ oge, na-agbanye na nchekwa data azụ azụ. A na-echekwa ihe arụrụ arụ (faịlụ ihe atụ, onyonyo) iche na nchekwa ihe yana nrụtụ aka echekwara na ụlọ ahịa metadata. N'ụzọ dị mkpa, iwere ụdị koodu ahụ (Git SHA) yana ọdịnaya hash nke data ntinye bụ ihe na-eme ka ịgba ọsọ nwee ike ịtụgharị - koodu gbakwunyere data yana nhazi nha nhata nsonaazụ.
Mmetụta atụmatụ
Ọnụ ego na mmefu ego
Mkpebi ihe owuwu ụlọ na-akwalite arụmọrụ yana ọnụ ahịa ọrụ ruo ọtụtụ afọ.
Mkpebi doro anya
Nkà mmụta nka na-enyere ndị otu egwuregwu aka ịhọrọ nchịkọta ziri ezi, ọ bụghị naanị nke kachasị ọhụrụ.
Quality akara
Nhọrọ injinia ka mma na-ebelata ihe omume ntụkwasị obi na mmepụta.
Ọdịnihu nke nyocha nnwale
Nchọgharị nnwale na-ejikọta na nyiwe MLOps na LLMOps sara mbara. Dị ka ụdị ntọala na-achị, nsochi na-agbasa site na ọnụọgụ ọnụọgụ ruo na ngwa ngwa ụdị, nleba anya, na nsonaazụ qualitative. Ahịrị akpaghị aka - ijikọ nnwale na ezigbo dataset, koodu, na ụdị ewesara ala - na-aghọ ọkọlọtọ maka ọchịchị na nyocha nyocha. Na-atụ anya njikọta siri ike na ụlọ ahịa atụmatụ, ndebanye aha ụdị, na CI/CD, gbakwunyere nkwado ka ukwuu maka nkesa na nkesa ọtụtụ ebe a na-ewepụta ọtụtụ puku ule na atụnyere na-akpaghị aka.
Mmejuputa n'ezie n'ụwa
Ndị otu kọmputa na-ahụ maka ọhụụ na-eji Arọ & Ọhụụ na-atụnyere mkpochapụ hyperparameter 200 wee chọpụta usoro mmụta-ọnụego nke na-ebuli izizi nkwado.
Mmalite na-edekọ aha Git ziri ezi yana dataset hash maka ọsọ MLflow ọ bụla ka onye na-achịkwa nwere ike mechaa mepụtaghachi ụdị nke mere mkpebi kredit.
Ụlọ nyocha nyocha na-agbanye usoro ọnwụ oge ọ bụla na dashboard nkekọrịta ka ndị na-arụkọ ọrụ na mpaghara oge dị iche iche nwee ike nyochaa ogologo oge ọzụzụ.
Otu NLP na-eso ụdị ngwa ngwa yana akara nleba anya n'ofe nnwale nlegharị anya LLM iji họrọ nhazi kacha mma tupu ebuga ya.
Ihe ize ndụ & okporo ụzọ nche
Ịkwalite otu akara ngosi nwere ike zoo adịghị ike sistemụ sara mbara.
A na-eledakarị ihe akụrụngwa na ụgwọ ọrụ anya.
Ọdịiche nchekwa na nleba anya nwere ike itolite ka sistemu na-adịwanye mgbagwoju anya.
Map mmejuputa
Kọwaa latency, ịdịmma na ebumnuche ọnụ ahịa tupu mmejuputa ya.
Benchmark n'okpuru ibu dị adị na ọnọdụ data.
Nleba anya akụrụngwa maka mperi, ịkpafu na mmetụta onye ọrụ.
Kwadebe ụzọ nzaghachi azụghachi azụ na ihe omume tupu ịchachaa.
Nọgide na-eme nchọpụta
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Ntuziaka na-esote
MLflow na Model Lifecycle Tracking
Ajụjụ a na-ajụkarị
What is Experiment Tracking?
Nleba ule bụ omume nke ịdekọ usoro mmụta igwe ọ bụla - koodu ya, data, hyperparameters, metrics, na nsonaazụ ya - yabụ nsonaazụ ya nwere ike imepụtaghachi yana atụnyere ya. Na-enweghị ya, ajụjụ bụ 'nke version kasị mma na otú anyị si nweta ya?' na-aghọ ihe fọrọ nke nta ka ọ gaghị ekwe omume ịza.
Kedu ebumnuche bụ isi nke nyocha nnwale na mmụta igwe?
Nyochaa koodu ndekọ ndekọ, data, hyperparameters, na metrik ka enwere ike imepụtagharị ọsọ na atụnyere ibe ya.
Kedu ngwakọta dị mkpa iji mee ka otu ọzụzụ na-agba ọsọ n'ezie?
Nhazigharị chọrọ koodu ziri ezi (dịka, Git Committee), otu data na otu nhazi - ọnụ ha na-ekpebi nsonaazụ.
Kedu n'ime ihe ndị a bụ ngwa nyocha nnwale ama ama?
MLflow, yana Arọ & Biases, Neptune, na Comet, bụ usoro nyocha nnwale a na-ejikarị.
Kedu otu ọtụtụ ndị na-enyocha nnwale si ewepụtakarị metrik n'oge ọzụzụ?
Ndị na-egwu egwu na-ekpughe API ndị ị na-akpọ n'ime loop ọzụzụ ka ha dekọọ paramita otu ugboro na metrik ugboro ugboro, na-ebugharị ha na azụ nchekwa.
Ebee ka nnukwu arịa dị ka ihe atụ echekwara na-echekwakarị site na usoro nsochi?
Faịlụ buru ibu na-aga na ihe ma ọ bụ nchekwa ihe arụrụ arụ, ebe ụlọ ahịa metadata na-edobe ntụaka dị fechaa yana metrik na ọnụọgụgụ.