Geliştirme görevleri

Finance 2050 adlı küresel bir yatırım şirketi için farklı kişilerden oluşan bir MLOps proje ekibine liderlik ediyorsun. Hedef, enflasyon düzeltmeleri için 24 saatlik bir erken uyarı sistemi geliştirmek. İş ortağından onayı aldın, gereksinimleri topladın ve bir fizibilite çalışmasını tamamladın. Şimdi gerçek geliştirmeye başlıyorsun.

Bu egzersiz

İş Dünyası için MLOps

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

İş Dünyası için MLOps

kursunun bir parçasıdır

InicianteNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

The first chapter will introduce MLOps and why it is necessary for businesses that want to design, develop, and operate multiple machine learning applications simultaneously. You will learn about the main elements of MLOps, such as scaling and automation, its benefits, and why MLOps remain challenging. You will also explore what it takes to start the MLOps journey both from a technological and managerial perspective.

Exercise 1: What is MLOps?Exercise 2: MLOps: a set of practices to ...Exercise 3: What are characteristics of MLOps?Exercise 4: The business arguments for MLOps Exercise 5: Relevant MLOps fields Exercise 6: How to invest in MLOps Exercise 7: Reasons to invest in MLOps Exercise 8: Laying the foundation for MLOps Exercise 9: MLOps team members Exercise 10: Prerequisites of MLOps Exercise 11: Elements of MLOps

In the second chapter, you’ll learn about the entire MLOps life cycle from design to development, deployment, and operations. You’ll explore why monitoring is essential for productive machine learning applications and why we must regularly re-train machine learning models.

Exercise 1: Uçtan uca MLOps açıklaması Exercise 2: On önemli MLOps yaşam döngüsü adımı Exercise 3: Bir MLOps uygulamasının ana aşamaları Exercise 4: MLOps’un ana aşamalarındaki önemli görevler Exercise 5: Tasarım aşaması Exercise 6: Tasarım aşaması görevlerini ertelemek Exercise 7: Başarı İçin Plan: MLOps Projeni Tasarlama Exercise 8: Geliştirme aşaması Exercise 9: Geliştirme görevleri

Geçerli Egzersiz

Exercise 10: Geliştirme aşamasındaki roller Exercise 11: MLOps uygulamalarını izleme, yeniden eğitme ve değiştirme Exercise 12: MLOps yaşam döngüsünün özellikleri Exercise 13: Bir MLOps kullanım senaryosu geliştirirken bireysel sorumluluklar Exercise 14: MLOps yaşam döngüsü

In the third chapter, you will move from theory to practice and discover the main challenges and risks of deploying machine learning models. You’ll also learn how MLOps teams successfully operate and what management can do to foster successful scaling machine learning.

Exercise 1: Business challenges and risks related to MLOps Exercise 2: What makes MLOps challenging?Exercise 3: Traditional software vs. MLOps Exercise 4: Potential MLOps business risks Exercise 5: How MLOps teams successfully operate Exercise 6: DevOps-style teams Exercise 7: Challenges to implement MLOps Exercise 8: The state of MLOps today Exercise 9: A false statement about MLOps Exercise 10: Reasons that MLOps is challenging

The final chapter will demonstrate how to successfully jumpstart your business's MLOps journey by discussing best practices and pitfalls to avoid. Finally, you’ll examine the different levels of MLOps maturity and conclude the course with a real-life case study about designing, developing, and operating a machine learning application for critical production processes.

Exercise 1: How businesses can become fully MLOps ready Exercise 2: Valid MLOps KPIs Exercise 3: Order deployment maturity Exercise 4: MLOps maturity Exercise 5: MLOps best practices and pitfalls Exercise 6: What do we need to test?Exercise 7: (Not) A best practice Exercise 8: MLOps case study: Increasing profits with MLOps Exercise 9: Key points of the case study Exercise 10: Wrap-up