Pipeline injinia na ụdị data njiri mara
Pipeline injinia na-egosipụta na-agbanwe data raw ka ọ bụrụ ụdị akara ọnụọgụgụ n'ezie na-amụta na ya, ebe ụdị data na-enyocha kpọmkwem data na mgbanwe mepụtara ụdị ọ bụla.
Nchịkọta
Together they make machine learning reproducible, auditable, and safe to change.
Ime miri emi
Pipeline nke injinia njiri mara bụ usoro nke na-atụgharị ntinye ihe na-adịghị mma (akwụkwọ ndekọ, akara oge, ederede, azụmahịa) ka ọ bụrụ njiri dị ọcha nke ụdị nwere ike iri: ịkọ ụbọchị n'ime ụbọchị nke izu, imezi ọnụọgụgụ, otu ntinye koodu na-ekpo ọkụ, na-achịkọta akụkọ ihe mere eme onye ọrụ ka ọ bụrụ nkezi. A na-ede pipeline dị ka koodu ka ha na-agba ọsọ n'otu oge n'oge ọzụzụ na na mmepụta. Versiondị data na-edekọ nseta ihuenyo nke dataset yana koodu mgbanwe nke wuru ha, na-abụkarị site na hashes ọdịnaya. Ngwa dị ka DVC, LakeFS, na ụlọ ahịa atụmatụ dị ka Feast ma ọ bụ Tecton na-echekwa ụdị ndị a. Nkwụghachi ụgwọ ahụ: mgbe ihe nlereanya emejọrọ, ị nwere ike chepụta ụdị data na nke atụmatụ ezi uche mepụtara ya, mepụtaghachi nsonaazụ bit-for-bit, wee jiri obi ike tụgharịa azụ.
Nghọta nka nka
Ntugharị na-ekpochapụ ọdịnaya dataset (ọ bụghị naanị aha faịlụ) yabụ ewepu data data yana mgbanwe ọ bụla na-ewepụta NJ ọhụrụ na-enweghị mgbanwe. A na-egosiputa pipeline dị ka eserese acyclic (DAGs) nke usoro mgbanwe; ngwá ọrụ na-ejegharị DAG, na-enyocha ihe ntinye gbanwere site na hashes ha, ma na-emegharị naanị usoro ndị emetụtara. metadata nke ahịrị na-ejikọ uru njirimara ọ bụla laghachi na ahịrị isi mmalite, ụdị mgbanwe, yana akara oge, na-eme ka nrụpụta na nyocha.
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 pipeline njiri mara na ụdị data
Na-atụ anya njikọ siri ike nke ụlọ ahịa atụmatụ, ụdị data, na ndebanye aha ụdị n'ime nyiwe MLOps jikọtara ọnụ ebe amụma ọ bụla na-enweta akara mkpịsị aka data-gbakwunyere koodu. Nkọwa njiri mara ọkwa, nhazi oge na-akpaghị aka, yana njikọta na nkwekọrịta data ga-ebelata koodu gluu akwụkwọ ntuziaka. Ka iwu gburugburu AI auditability na-eto, usoro ahịrị na-enweghị ike ịgbanwe ga-abụ ihe a chọrọ, na nnukwu pipeline ụdị asụsụ ga-anakwere ụdị mbipụta ahụ maka mkpali, ntinye, na iweghachite ụlọ ọrụ.
Mmejuputa n'ezie n'ụwa
Otu ụlọ akụ na-edepụta atụmatụ nchọpụta aghụghọ ya nke edobere ka ndị na-enyocha ego wee nwee ike imepụtaghachi mkpokọta azụmahịa ejiri mee mkpebi ọkọlọtọ ọ bụla ka ọnwa gachara.
Ndị otu e-azụmahịa na-eji mmemme gbakọọ 'ọnụahịa nkezi n'usoro n'ime ụbọchị 30 gara aga' otu ugboro wee jeere ya ozi ma ọrụ ọzụzụ yana API ndụmọdụ ndụ.
Onye ọkà mmụta sayensị data na-eji DVC tụgharịa azụ na dataset ehichapụrụ n'izu gara aga ka ọ chọpụtachara usoro nhazi nke buggy mebiri atụmatụ dị ugbu a.
Otu nlekọta ahụike ML na-atụnye ntọhapụ ụdị ọ bụla gaa na foto nke ndekọ ndị ọrịa nwere ọdịnaya nwere ike ime ka enwere ike ịmegharị ọmụmụ ihe n'otu aka ahụ maka ndị nchịkwa.
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
Free newsletter
Get the daily AI briefing
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the Feature Engineering Pipelines and Data Versioning quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Ntuziaka na-esote
Njirimara Injinia
Ajụjụ a na-ajụkarị
What is Feature Engineering Pipelines and Data Versioning?
Pipeline injinia na-egosipụta na-agbanwe data raw ka ọ bụrụ ụdị akara ọnụọgụgụ n'ezie na-amụta na ya, ebe ụdị data na-enyocha kpọmkwem data na mgbanwe mepụtara ụdị ọ bụla. Ha jikọtara ọnụ na-eme ka mmụta igwe bụrụ nke a na-emegharịgharị, na-enyocha ya, yana nchekwa ịgbanwe.
Kedu ihe bụ isi ebumnuche nke pipeline engineering atụmatụ?
Pipeline injinịa atụmatụ bụ usoro mgbanwe mgbanwe nke na-agbanwe ngwa ngwa, ntinye adịghị mma ka ọ bụrụ njirimara ọnụọgụgụ dị ọcha nke ihe nlereanya nwere ike ịmụta na ya.
Kedu ihe kpatara ngwaọrụ nsụgharị data na-ejikarị ehichapụ ọdịnaya nke dataset karịa naanị aha faịlụ ya?
Hashing ọdịnaya pụtara otu data na-enweta otu ID ahụ (na-enye ohere iwepụ ya) na mgbanwe ọ bụla na-ewepụta NJ ọhụrụ, na-ekwe nkwa enweghị mgbanwe na mmụgharị.
A na-egosipụtakarị pipeline atụmatụ dị ka ụdị nhazi?
Emebere pipeline dị ka DAG ka sistemụ ahụ nwee ike ikpebi ndabere n'etiti nzọụkwụ wee malitegharịa naanị ọkwa nke ntinye ya gbanwere.
Kedu nsogbu mbipute data na-akacha edozi ozugbo mgbe ụdị etinyere na-amalite ịkpa àgwà ọjọọ?
Versioning na-edekọ ihe onyonyo dataset yana koodu mgbanwe wuru ihe nlereanya, yabụ ị nwere ike imepụtaghachi nsonaazụ wee tụgharịa gaa na steeti ama ama.
Kedu n'ime ihe ndị a bụ ihe atụ eji eme ihe maka ụlọ ahịa atụmatụ ma ọ bụ ụdị data?
Ememme na Tecton bụ ụlọ ahịa atụmatụ, ebe DVC na LakeFS bụ ngwaọrụ nsụgharị data nke a na-ejikarị na pipeline MLOps.