{"id":478029,"date":"2023-08-09T09:26:05","date_gmt":"2023-08-09T09:26:05","guid":{"rendered":""},"modified":"2023-09-05T11:15:54","modified_gmt":"2023-09-05T11:15:54","slug":"mlflow","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/mlflow\/","title":{"rendered":"ML ak\u0131\u015f\u0131"},"content":{"rendered":"<p>MLflow hakk\u0131nda k\u0131sa bilgi<\/p>\n<p>MLflow, makine \u00f6\u011frenimi (ML) ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn tamam\u0131n\u0131 y\u00f6netmeyi ama\u00e7layan a\u00e7\u0131k kaynakl\u0131 bir platformdur. Deneyleri izlemekten tahminleri ba\u015fkalar\u0131yla payla\u015fmaya kadar her \u015feyi kapsar. MLflow&#039;un birincil hedefi, bilim adamlar\u0131n\u0131n ve m\u00fchendislerin \u00e7al\u0131\u015fmalar\u0131n\u0131 yinelemelerini, ilerlemelerini payda\u015flarla payla\u015fmalar\u0131n\u0131 ve modellerini \u00fcretime ge\u00e7irmelerini kolayla\u015ft\u0131rmakt\u0131r.<\/p>\n<h2>MLflow&#039;un K\u00f6keni ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>MLflow, veri i\u015fleme ve analiz alan\u0131nda lider bir \u015firket olan Databricks taraf\u0131ndan geli\u015ftirilmi\u015f ve tan\u0131t\u0131lm\u0131\u015ft\u0131r. Haziran 2018&#039;de Spark + Yapay Zeka Zirvesi&#039;nde resmi olarak duyuruldu. Ba\u015flang\u0131\u00e7tan itibaren \u00f6ncelikli odak noktas\u0131, \u00f6zellikle da\u011f\u0131t\u0131lm\u0131\u015f ortamlarda makine \u00f6\u011frenimi modellerinin geli\u015ftirilmesi, y\u00f6netilmesi ve da\u011f\u0131t\u0131lmas\u0131na ili\u015fkin karma\u015f\u0131k s\u00fcreci kolayla\u015ft\u0131rmakt\u0131.<\/p>\n<h2>MLflow Hakk\u0131nda Detayl\u0131 Bilgi: MLflow Konusunu Geni\u015fletme<\/h2>\n<p>MLflow d\u00f6rt ana bile\u015fene ayr\u0131lm\u0131\u015ft\u0131r:<\/p>\n<ol>\n<li><strong>MLflow Takibi<\/strong>: Bu bile\u015fen, denemeleri ve \u00f6l\u00e7\u00fcmleri g\u00fcnl\u00fc\u011fe kaydeder ve sorgular.<\/li>\n<li><strong>MLflow Projeleri<\/strong>: Kodun yeniden kullan\u0131labilir, \u00e7o\u011falt\u0131labilir bile\u015fenler halinde paketlenmesine yard\u0131mc\u0131 olur.<\/li>\n<li><strong>MLflow Modelleri<\/strong>: Bu b\u00f6l\u00fcm, modellerin \u00fcretime ta\u015f\u0131nmas\u0131 s\u00fcrecini standart hale getirir.<\/li>\n<li><strong>MLflow Kay\u0131t Defteri<\/strong>: \u0130\u015fbirli\u011fi i\u00e7in merkezi bir merkez sunar.<\/li>\n<\/ol>\n<p>MLflow, Python, R, Java ve daha fazlas\u0131 dahil olmak \u00fczere birden fazla programlama dilini destekler. Standart paket y\u00f6neticileri kullan\u0131larak kurulabilir ve pop\u00fcler makine \u00f6\u011frenimi kitapl\u0131klar\u0131yla entegre olabilir.<\/p>\n<h2>MLflow&#039;un \u0130\u00e7 Yap\u0131s\u0131: MLflow Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>MLflow, REST API&#039;leri, CLI&#039;ler ve yerel istemci kitapl\u0131klar\u0131 arac\u0131l\u0131\u011f\u0131yla eri\u015filebilen merkezi bir sunucu sa\u011flayarak \u00e7al\u0131\u015f\u0131r.<\/p>\n<ul>\n<li><strong>\u0130zleme Sunucusu<\/strong>: T\u00fcm deneyleri, \u00f6l\u00e7\u00fcmleri ve ilgili yap\u0131lar\u0131 saklar.<\/li>\n<li><strong>Proje Tan\u0131m Dosyalar\u0131<\/strong>: Y\u00fcr\u00fctme ortamlar\u0131na y\u00f6nelik yap\u0131land\u0131rmay\u0131 i\u00e7erir.<\/li>\n<li><strong>Model Ambalaj\u0131<\/strong>: Modellerin d\u0131\u015fa aktar\u0131m\u0131 i\u00e7in farkl\u0131 formatlar sunar.<\/li>\n<li><strong>Kay\u0131t defteri kullan\u0131c\u0131 aray\u00fcz\u00fc<\/strong>: T\u00fcm payla\u015f\u0131lan modelleri y\u00f6netmek i\u00e7in bir web aray\u00fcz\u00fc.<\/li>\n<\/ul>\n<h2>MLflow&#039;un Temel \u00d6zelliklerinin Analizi<\/h2>\n<p>MLflow&#039;un ana \u00f6zellikleri \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li><strong>Deneme Takibi<\/strong>: Farkl\u0131 \u00e7al\u0131\u015ft\u0131rmalar\u0131n kolayca kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131na olanak tan\u0131r.<\/li>\n<li><strong>Yeniden \u00fcretilebilirlik<\/strong>: Kodu ve ba\u011f\u0131ml\u0131l\u0131klar\u0131 kaps\u00fcller.<\/li>\n<li><strong>Model Sunumu<\/strong>: \u00c7e\u015fitli platformlarda da\u011f\u0131t\u0131m\u0131 kolayla\u015ft\u0131r\u0131r.<\/li>\n<li><strong>\u00d6l\u00e7eklenebilirlik<\/strong>: K\u00fc\u00e7\u00fck \u00f6l\u00e7ekli geli\u015ftirmeyi ve b\u00fcy\u00fck \u00f6l\u00e7ekli \u00fcretim ortamlar\u0131n\u0131 destekler.<\/li>\n<\/ul>\n<h2>Ne T\u00fcr MLflow Vard\u0131r: Yazmak i\u00e7in Tablolar\u0131 ve Listeleri Kullan\u0131n<\/h2>\n<p>MLflow&#039;un kendisi benzersiz olmas\u0131na ra\u011fmen bile\u015fenleri farkl\u0131 i\u015flevlere hizmet eder.<\/p>\n<table>\n<thead>\n<tr>\n<th>Bile\u015fen<\/th>\n<th>\u0130\u015flev<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>MLflow Takibi<\/td>\n<td>G\u00fcnl\u00fckler ve sorgu denemeleri<\/td>\n<\/tr>\n<tr>\n<td>MLflow Projeleri<\/td>\n<td>Paketler yeniden kullan\u0131labilir kod<\/td>\n<\/tr>\n<tr>\n<td>MLflow Modelleri<\/td>\n<td>Hareketli modelleri \u00fcretime standart hale getirir<\/td>\n<\/tr>\n<tr>\n<td>MLflow Kay\u0131t Defteri<\/td>\n<td>Model i\u015fbirli\u011fi i\u00e7in merkezi merkez<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>MLflow&#039;u Kullanma Yollar\u0131, Kullan\u0131ma \u0130li\u015fkin Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<p>MLflow&#039;un \u00e7e\u015fitli uygulamalar\u0131 vard\u0131r ancak baz\u0131 yayg\u0131n sorunlar ve \u00e7\u00f6z\u00fcmler \u015funlard\u0131r:<\/p>\n<ul>\n<li><strong>DevOps&#039;ta kullan\u0131n<\/strong>: Model da\u011f\u0131t\u0131m\u0131n\u0131 kolayla\u015ft\u0131r\u0131r ancak karma\u015f\u0131k olabilir.\n<ul>\n<li>\u00c7\u00f6z\u00fcm: Kapsaml\u0131 belgeler ve topluluk deste\u011fi.<\/li>\n<\/ul>\n<\/li>\n<li><strong>S\u00fcr\u00fcm Olu\u015fturma Sorunlar\u0131<\/strong>: De\u011fi\u015fiklikleri takip etme zorlu\u011fu.\n<ul>\n<li>\u00c7\u00f6z\u00fcm: MLflow izleme bile\u015fenini kullan\u0131n.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Entegrasyon Sorunlar\u0131<\/strong>: Baz\u0131 ara\u00e7larla s\u0131n\u0131rl\u0131 entegrasyon.\n<ul>\n<li>\u00c7\u00f6z\u00fcm: D\u00fczenli g\u00fcncellemeler ve topluluk odakl\u0131 uzant\u0131lar.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>Ana \u00d6zellikler ve Tablo ve Liste \u015eeklindeki Benzer Ara\u00e7larla Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>ML ak\u0131\u015f\u0131<\/th>\n<th>Di\u011fer Aletler<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Deneme Takibi<\/td>\n<td>Evet<\/td>\n<td>De\u011fi\u015fir<\/td>\n<\/tr>\n<tr>\n<td>Model Ambalaj\u0131<\/td>\n<td>Standartla\u015ft\u0131r\u0131lm\u0131\u015f<\/td>\n<td>Genellikle \u00d6zel<\/td>\n<\/tr>\n<tr>\n<td>\u00d6l\u00e7eklenebilirlik<\/td>\n<td>Y\u00fcksek<\/td>\n<td>De\u011fi\u015fir<\/td>\n<\/tr>\n<tr>\n<td>Dil deste\u011fi<\/td>\n<td>\u00c7oklu<\/td>\n<td>S\u0131n\u0131rl\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>MLflow ile \u0130lgili Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>MLflow s\u00fcrekli olarak geli\u015fmektedir. Gelecekteki trendler \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li><strong>Geli\u015fmi\u015f \u0130\u015fbirli\u011fi \u00d6zellikleri<\/strong>: Daha b\u00fcy\u00fck tak\u0131mlar i\u00e7in.<\/li>\n<li><strong>Daha \u0130yi Entegrasyon<\/strong>: Daha fazla \u00fc\u00e7\u00fcnc\u00fc taraf ara\u00e7 ve hizmetle.<\/li>\n<li><strong>Daha Fazla Otomasyon<\/strong>: ML ya\u015fam d\u00f6ng\u00fcs\u00fc i\u00e7inde tekrarlanan g\u00f6revlerin otomatikle\u015ftirilmesi.<\/li>\n<\/ul>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya MLflow ile \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy gibi proxy sunucular, MLflow ortamlar\u0131nda a\u015fa\u011f\u0131dakiler i\u00e7in kullan\u0131labilir:<\/p>\n<ul>\n<li><strong>G\u00fcvenlik<\/strong>: Hassas verilerin korunmas\u0131.<\/li>\n<li><strong>Y\u00fck dengeleme<\/strong>: \u0130steklerin sunucular aras\u0131nda da\u011f\u0131t\u0131lmas\u0131.<\/li>\n<li><strong>Giri\u015f kontrolu<\/strong>: \u0130zinleri ve rolleri y\u00f6netme.<\/li>\n<\/ul>\n<p>G\u00fcvenilir proxy sunucular\u0131n kullan\u0131lmas\u0131, \u00f6zellikle b\u00fcy\u00fck \u00f6l\u00e7ekli uygulamalarda MLflow&#039;un \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131 i\u00e7in g\u00fcvenli ve verimli bir ortam sa\u011flar.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/mlflow.org\/\" target=\"_new\" rel=\"noopener nofollow\">MLflow Resmi Web Sitesi<\/a><\/li>\n<li><a href=\"https:\/\/databricks.com\/product\/managed-mlflow\" target=\"_new\" rel=\"noopener nofollow\">Databricks MLflow Sayfas\u0131<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/mlflow\/mlflow\" target=\"_new\" rel=\"noopener nofollow\">MLflow GitHub Deposu<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy Web Sitesi<\/a><\/li>\n<\/ul>\n<p>Bu makale MLflow&#039;un, bile\u015fenlerinin, kullan\u0131mlar\u0131n\u0131n ve proxy sunucularla ili\u015fkisinin derinlemesine anla\u015f\u0131lmas\u0131n\u0131 sa\u011flar. Ayn\u0131 zamanda di\u011fer benzer ara\u00e7larla kar\u015f\u0131la\u015ft\u0131rmalar\u0131 da detayland\u0131r\u0131yor ve modern makine \u00f6\u011frenimi geli\u015fiminin bu ayr\u0131lmaz par\u00e7as\u0131n\u0131n gelece\u011fine bak\u0131yor.<\/p>","protected":false},"featured_media":468919,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478029","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>MLflow: A Comprehensive Overview<\/mark>","faq_items":[{"question":"What is MLflow and why was it created?","answer":"<p>MLflow is an open-source platform designed to manage the entire machine learning lifecycle. Created by Databricks and announced in 2018, it encompasses tracking experiments, packaging code, standardizing models, and providing a collaboration hub. Its primary goal is to simplify the processes involved in developing, managing, and deploying machine learning models.<\/p>"},{"question":"What are the main components of MLflow?","answer":"<p>The main components of MLflow are MLflow Tracking, which logs and queries experiments and metrics; MLflow Projects, which packages code into reusable components; MLflow Models, that standardizes the process of moving models to production; and MLflow Registry, a centralized hub for collaboration and model management.<\/p>"},{"question":"How does MLflow ensure reproducibility and scalability?","answer":"<p>MLflow ensures reproducibility by encapsulating code and dependencies, making it easy to replicate experiments. It offers scalability by supporting both small-scale development environments and large-scale production systems. The standardized model packaging and deployment features further enhance its scalability.<\/p>"},{"question":"What types of problems can arise while using MLflow, and how can they be resolved?","answer":"<p>Common problems with MLflow include complexity in deployment, versioning issues, and integration problems with some tools. These can be resolved through comprehensive documentation, utilizing the MLflow tracking component for versioning, and regular updates or community-driven extensions to enhance integration.<\/p>"},{"question":"How can proxy servers like OneProxy be associated with MLflow?","answer":"<p>Proxy servers like OneProxy can be utilized with MLflow for security by protecting sensitive data, load balancing by distributing requests across servers, and access control by managing permissions and roles. They ensure a secure and efficient environment for running MLflow, particularly in large-scale applications.<\/p>"},{"question":"What are the future perspectives and technologies related to MLflow?","answer":"<p>The future of MLflow includes enhanced collaboration features for larger teams, better integration with more third-party tools and services, and increased automation within the machine learning lifecycle. It continues to evolve to meet the needs of the rapidly advancing field of machine learning.<\/p>"},{"question":"Where can I find more information about MLflow?","answer":"<p>You can find more information about MLflow on the <a href=\"https:\/\/mlflow.org\/\" target=\"_new\">official website<\/a>, the <a href=\"https:\/\/databricks.com\/product\/managed-mlflow\" target=\"_new\">Databricks MLflow page<\/a>, and the <a href=\"https:\/\/github.com\/mlflow\/mlflow\" target=\"_new\">MLflow GitHub repository<\/a>. If you are interested in how it relates to proxy servers, you can also visit <a href=\"https:\/\/oneproxy.pro\" target=\"_new\">OneProxy's website<\/a>.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/478029","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/478029\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468919"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=478029"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}