Apache Airflow maka ML Workflows
Apache Airflow bụ ikpo okwu mepere emepe maka ide akwụkwọ, nhazi oge na nyochaa usoro ọrụ dị ka koodu.
Nchịkọta
In machine learning it acts as the conductor that triggers data pipelines, retraining jobs, and batch predictions on a reliable schedule.
Ime miri emi
Emebere ikuku ikuku na Airbnb na 2014 ma ugbu a bụ ọrụ Apache. Abstraction ya bụ isi bụ DAG: eserese Acyclic eduzi akọwapụtara na Python, ebe akụkụ na-edozi usoro ogbugbu na ịdabere na ya. Onye nhazi oge na-atụgharị DAG ndị a, kpebie ọrụ ndị dị njikere, ma zigara ha ndị ọrụ na ndị ọrụ; UI webụ na-egosi akụkọ na-agba ọsọ, ndekọ, na ọkwa ọrụ. Maka ML, a na-eji Airflow eme ihe dị ka onye na-agụ egwú kama ịbụ injin compute: ọ naghị azụ ụdị onwe ya kama ọ na-ebute usoro dị ka iwepụta data, ịkwado ya, ịmalite ọrụ ọzụzụ na Spark ma ọ bụ Kubernetes pod, na ibuga nsonaazụ ya. Ndị na-arụ ọrụ na ihe mmetụta na-ahapụ ọrụ ka ọ kpọọ sistemu mpụga, chere faịlụ, ma ọ bụ mee arịa. Ike ya bụ nhazi oge a pụrụ ịdabere na ya, nwughachi, njupụta azụ, yana nhụsianya doro anya n'ime pipeline siri ike, dabere na oge.
Nghọta nka nka
Airflow DAG bụ naanị koodu Python, yabụ, a na-egosipụta nkwado ndabere na mpaghara yana ndị ọrụ ejiri eriri bitshift syntax ma ọ bụ API ọrụ kechie. Onye nhazi oge na-enyocha oge oge DAG ọ bụla yana dabere ọrụ, na-akwụ ụgwọ naanị ọrụ ndị dabere na elu nwere ihe ịga nke ọma. Ndị na-egbu ihe dị ka Celery ma ọ bụ Kubernetes na-arụ ọrụ ndị ahụ na ndị ọrụ ekesa. A na-eji steeti, ndekọ, na mgbagha nyochagharị ọrụ ọ bụla, na-echekwa metadata na nchekwa data nkwado maka nyocha zuru oke.
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 Apache Airflow maka ML Workflows
Airflow 2.x na 3.x na-emesi onye nhazi ngwa ngwa ngwa ngwa, TaskFlow API maka pipeline Python dị ọcha, yana nhazi oge data ebe DAG na-ebute na mmelite dataset kama ịmegharị elekere. Maka ML, na-atụ anya njikọ siri ike na ụlọ ahịa atụmatụ yana ọzụzụ mmemme na-akwalite. Ikuku ikuku na-edobe onwe ya dị ka oyi akwa orchestration nke na-ahazi ngwa ọrụ pụrụ iche dị ka dbt, Spark na Kubeflow, kama iso ha na-asọrịta mpi, na-eme ka ọrụ ya dị ka nhazi nhazi nke data ọgbara ọhụrụ na nchịkọta ML.
Mmejuputa n'ezie n'ụwa
Otu ụlọ ọrụ mgbasa ozi na-eme Airflow DAG kwa ụbọchị nke na-adọta ndekọ ntinye aka nke onye ọrụ, na-azụghachi ụdị nkwanye, ma na-eme ka cache na-enye ume ọhụrụ.
Otu e-azụmahịa na-eji sensọ chere faịlụ data onye na-ere ahịa ka ọ banye na nchekwa igwe ojii tupu ha amalite ọrụ ịkọ amụma ala.
Otu ụlọ ọrụ fintech na-ahazi ọrụ ị ga-enweta akara kwa elekere ebe Airflow na-ebute ihe atụ ejiri akpa mee ka ọ gosipụta azụmahịa na-enyo enyo.
Otu data na-eji ihe nkwụghachi azụ nke Airflow na-emegharị ọnwa nke data akụkọ ihe mere eme site na pipeline ọhụrụ-engineering mgbe mgbanwe mgbagha gasịrị.
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
Kubernetes maka ibu ọrụ ML
Ajụjụ a na-ajụkarị
What is Apache Airflow for ML Workflows?
Apache Airflow bụ ikpo okwu mepere emepe maka ide akwụkwọ, nhazi oge na nyochaa usoro ọrụ dị ka koodu. N'ịmụ igwe ọ na-arụ ọrụ dị ka onye nduzi nke na-akpalite pipeline data, na-azụghachi ọrụ, na amụma ogbe na usoro a pụrụ ịdabere na ya.
Na Apache Airflow, kedu ihe bụ isi abstraction ejiri kọwaa usoro ọrụ?
A kọwapụtara usoro ọrụ ikuku dị ka DAG na Python, ebe ọrụ bụ ọnụ na ọnụ na-akọwapụta usoro igbu.
Kedu ọrụ Airflow na-arụkarị na pipeline ML?
Airflow bụ onye na-agụ egwú: ọ na-akpalite ma na-ahazi usoro dị ka mmịpụta data na ọrụ ọzụzụ kama ịme mgbakọ dị arọ n'onwe ya.
Kedu ihe nrụpụta Airflow a na-eji chere ọnọdụ mpụga, dị ka faịlụ na-abata na nchekwa?
Sensọ bụ ndị ọrụ pụrụ iche na-akwụsịtụ usoro ọrụ ruo mgbe ọnọdụ ezutere, dị ka ọdịda faịlụ ma ọ bụ nkebi pụtara.
Kedu ihe Airflow 'backfill' na-enye gị ohere ime?
Ntugharị azụ na-eme DAG n'ofe oge gara aga, bara uru maka ịhazigharị akụkọ ihe mere eme mgbe mgbanwe mgbagha pipeline gasịrị.
Kedu akụrụngwa na-enyocha DAG na-aga n'ihu wee kpebie ọrụ ndị dị njikere ịrụ?
Onye nhazi oge na-enyocha DAG, na-enyocha oge nhazi oge na ihe ndabere, yana ọrụ kwụ n'ahịrị nke usoro mgbago elu nwere ihe ịga nke ọma.