Gecikme

"Doğrulama gecikmesi" adı verilen önemli bir terimi öğrendin.

Doğrulama gecikmesi aylar mertebesindeyse, modeli zamanında bozulmaya karşı dolaylı olarak izlemek için tek seçeneğin girdi verisi izlemesine başvurmaktır.

Şimdi, İnternet’teki çeşitli haber portalları ve API’lerden toplanan verilere dayanarak hisse senedi fiyatlarını tahmin eden ML tabanlı bir uygulama geliştirdiğini hayal et. Her tahmin yaptığında, gerçek değer saniyeler içinde erişilebilir oluyor.

Bu kullanım senaryosunda yine de girdi verisi izlemesi uygulamak mantıklı mı?

Bu egzersiz

MLOps Dağıtımı ve Yaşam Döngüsü

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

MLOps Dağıtımı ve Yaşam Döngüsü

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

This chapter gives a high-level overview of MLOps principles and framework components important for deployment and life cycling.

Exercise 1: The modern MLOps framework Exercise 2: ML workflows Exercise 3: MLOps benefits Exercise 4: Life-cycling stages Exercise 5: App vs. model Exercise 6: Decommissioning Exercise 7: The model life cycle: recap Exercise 8: MLOps components Exercise 9: Automated sequence Exercise 10: Stores and registries Exercise 11: DevOps or MLOps?

This chapter is dedicated to all the considerations we need to make already in the development phase, in order to ensure a smooth ride when we reach the operations. Our ultimate goal is to explain how to train the model using MLOps best practices and build a model package that enables smooth deployment, reproducibility and post-deployment monitoring.

Exercise 1: Deployment-driven development Exercise 2: Testing your machine learning model Exercise 3: Best time to start deploying Exercise 4: Profiling, versioning, and feature stores Exercise 5: Feature store properties Exercise 6: Profiling and feature store benefits Exercise 7: Ensuring reproducibility Exercise 8: Model build pipelines in CI/CD Exercise 9: Different pipelines Exercise 10: Model build pipeline properties Exercise 11: CI/CD integration Exercise 12: Model packaging Exercise 13: Model formats Exercise 14: Full package

This chapter deals with critical model operations questions such as: - What are the different ways in which we can serve our models? - What is an API, and what are its key functionalities? - How do we thoroughly test our service before making it available to the end users? - How do we update models in production without service disturbance? You will learn about batch prediction, real-time prediction, input and output data validation, unit testing, integration testing, canary deployment, and much more.

Exercise 1: Serving modes Exercise 2: Offline or online?Exercise 3: When time matters - a bit Exercise 4: Building the API Exercise 5: Client-server Exercise 6: API functionalities Exercise 7: Deployment progression and testing Exercise 8: Which test is it?Exercise 9: Progression through environments Exercise 10: Tests per environment Exercise 11: Model deployment strategies Exercise 12: A fitting deployment strategy Exercise 13: Order of risk Exercise 14: Shadow of the shadow

This final chapter is dedicated to monitoring and maintaining ML services after they are deployed, as well as to model governance. You will cover crucial concepts such as verification latency, covariate shift, concept drift, human-in-the-loop systems, and more.

Exercise 1: ML servislerini izleme Exercise 2: Shift ve drift Exercise 3: Gecikme

Geçerli Egzersiz

Exercise 4: Şimdiden mi?Exercise 5: İzleme ve uyarı Exercise 6: İzleme sistemi Exercise 7: Uyarı Mekanizması Exercise 8: Model bakımı Exercise 9: Veri-merkezli vs Model-merkezli Exercise 10: Döngüde İnsan (Human-in-the-Loop)Exercise 11: Model yönetişimi Exercise 12: Yönetişimin öğeleri Exercise 13: Yönetişim aşamaları Exercise 14: Risk sınıflandırması Exercise 15: Kapanış