Imọ Itọsọna
MLflow ati Awoṣe Lifecycle Àtòjọ
MLflow jẹ ipilẹ orisun-ìmọ fun ṣiṣakoso igbesi aye ẹkọ ẹrọ, lati ipasẹ idanwo si iṣakojọpọ awoṣe ati imuṣiṣẹ.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
O ṣe pataki nitori pe o mu aṣẹ ati reproducibility wa si idoti, ilana iterative ti awọn awoṣe kikọ.
Jin Dive
MLflow, created by Databricks and released in 2018, helps organize experiments and model artifacts. Tracking records run parameters, metrics, and artifacts so teams can compare results. Projects describes reusable code and environments, while the Model format packages supported framework flavors and metadata. The Model Registry stores registered models and their versions. Current registry workflows use tags and reassignable aliases; fixed stages such as Staging and Production are deprecated. A team still defines its own evaluation and approval process rather than assuming the registry automatically certifies a model. MLflow supports several machine-learning frameworks and also provides tools for tracing and evaluating generative-AI applications.
Real-World imuse
Ẹgbẹ imọ-jinlẹ data ṣe igbasilẹ gbogbo ṣiṣe ikẹkọ pẹlu Titọpa MLflow, lẹhinna ṣe afiwe awọn dosinni ti awọn ṣiṣe ni UI lati mu awoṣe ti n ṣiṣẹ dara julọ.
A team evaluates a registered model version before assigning its deployment alias to that approved version.
Ẹgbẹ kan ṣe akopọ awoṣe kan ni ọna kika MLflow lẹẹkan, lẹhinna mu ohun-ọṣọ kanna lọ si aaye ipari REST, iṣẹ ipele kan, ati pẹpẹ awọsanma kan.
Ẹgbẹ ohun elo LLM kan nlo wiwa kakiri MLflow lati ṣe igbasilẹ awọn itọsi, awọn idahun, ati lairi fun ipe kọọkan, n ṣatunṣe aṣiṣe aṣoju kan.
Ojo iwaju ti MLflow ati Awoṣe Igbesi aye Titele
MLflow n pọ si ni ibinu si AI ipilẹṣẹ, fifi wiwa kakiri fun awọn ohun elo LLM, iṣakoso iyara, ati ohun elo igbelewọn fun awọn ẹwọn ati awọn aṣoju. Reti atilẹyin ti o jinlẹ fun titọpa awọn abajade LLM ti kii ṣe ipinnu, ipilẹ data ati ẹya ti o tọ, ati iṣọpọ pẹlu akopọ akiyesi to gbooro. Bi iforukọsilẹ ti n dagba, o n ṣiṣẹ pọ si bi ibudo iṣakoso nibiti awọn ẹgbẹ ṣe fọwọsi, ṣayẹwo, ati yipo awọn awoṣe Ayebaye mejeeji ati awọn eto ipilẹṣẹ-AI kọja awọn agbegbe iṣelọpọ.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Iye owo ati isuna. Awọn ipinnu faaji ṣe awakọ iṣẹ ati idiyele iṣẹ fun awọn ọdun.
- Awọn ipinnu diẹ sii. Ẹkọ imọ-ẹrọ ṣe iranlọwọ fun awọn ẹgbẹ lati yan akopọ to tọ, kii ṣe ọkan tuntun nikan.
- Iṣakoso didara. Awọn yiyan imọ-ẹrọ to dara julọ dinku awọn iṣẹlẹ igbẹkẹle ni iṣelọpọ.
Ṣọra fun
- Ṣiṣepe ala-ilẹ kan le tọju awọn ailagbara eto ti o gbooro.
- Awọn ohun elo amayederun ati awọn idiyele itọju nigbagbogbo ni aibikita.
- Aabo ati awọn ela akiyesi le dagba bi awọn eto ṣe di eka sii.
Next awọn igbesẹ
- Ṣetumo lairi, didara, ati awọn ibi-afẹde idiyele ṣaaju imuse.
- Aṣepari labẹ ẹru ojulowo ati awọn ipo data.
- Abojuto ohun elo fun awọn aṣiṣe, fiseete, ati ipa olumulo.
- Mura ipadasẹhin pada ati awọn ipa ọna esi iṣẹlẹ ṣaaju iwọn.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the MLflow and Model Lifecycle Tracking quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
Kini MLflow ati Titele Igbesi aye Awoṣe?
MLflow jẹ ipilẹ orisun-ìmọ fun ṣiṣakoso igbesi aye ẹkọ ẹrọ, lati ipasẹ idanwo si iṣakojọpọ awoṣe ati imuṣiṣẹ. O ṣe pataki nitori pe o mu aṣẹ ati atunṣe wa si idoti, ilana aṣetunṣe ti awọn awoṣe kikọ.
Iṣoro akọkọ wo ni Ṣiṣayẹwo MLflow yanju fun awọn onimọ-jinlẹ data?
Ipasẹ awọn igbasilẹ awọn igbewọle ati awọn abajade ti ṣiṣe kọọkan ki awọn ẹgbẹ le ṣe afiwe awọn idanwo ati ṣe ẹda awoṣe to dara julọ.
Which MLflow component stores registered model versions and their tags or aliases?
The registry manages model versions and named references. Fixed lifecycle stages are deprecated in favor of workflows using aliases and tags.
Kini o tumọ si pe MLflow jẹ 'framework-agnostic'?
MLflow ṣe atilẹyin ọpọlọpọ awọn ilana, eyiti o jẹ idi pataki kan ti o di boṣewa de facto.
Kini idi ti ọna kika awoṣe MLflow?
Ọna kika Awoṣe ṣe iwọn iṣakojọpọ nitorinaa ohun elo kanna le ṣe kojọpọ pada tabi ṣe iranṣẹ si awọn ibi-afẹde pupọ laisi koodu atunkọ.
Ile-iṣẹ wo ni o ṣẹda MLflow?
MLflow jẹ ipilẹṣẹ nipasẹ Databricks ati idasilẹ bi orisun ṣiṣi ni ọdun 2018.
Tesiwaju kikọ
Jẹmọ awọn itọsọna
Awọn itọsọna diẹ sii ti a yan fun koko yii