Farklı hatlar

Önceki derste ML derleme hattını ve bunun klasik DevOps derleme hattından nasıl farklılaştığını öğrendin.

Ancak “pipeline” kelimesi, adım adım otomatikleştirilmiş her süreci anlatmak için sık ve genel bir terim olduğundan bazen kafa karışıklığına yol açabilir.

Örneğin, isimleri çok benzer olsa da, model hattı ile model derleme hattının tamamen farklı iki şey olduğunu gördün.

En önemli görevlerini ve özelliklerini hâlâ hatırlıyor musun?

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: Dağıtıma yönelik geliştirme Exercise 2: Machine learning modelini test etme Exercise 3: Dağıtıma başlamak için en doğru zaman Exercise 4: Profiling, sürümleme ve özellik depoları Exercise 5: Feature store özellikleri Exercise 6: Profil oluşturma ve feature store faydaları Exercise 7: Yine üretilebilirliği sağlamak Exercise 8: CI/CD'de model derleme ardışıkları Exercise 9: Farklı hatlar

Geçerli Egzersiz

Exercise 10: Model build pipeline özellikleri Exercise 11: CI/CD entegrasyonu Exercise 12: Model paketleme Exercise 13: Model formatları Exercise 14: Tam paket

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: Monitoring ML services Exercise 2: Shift vs drift Exercise 3: Latency Exercise 4: Already?Exercise 5: Monitoring and alerting Exercise 6: The monitoring system Exercise 7: Alerting Exercise 8: Model maintenance Exercise 9: Data-centric vs Model-centric Exercise 10: Human-in-the-Loop Exercise 11: Model governance Exercise 12: Elements of governance Exercise 13: Stages of governance Exercise 14: Risk classification Exercise 15: Wrap up