Geleneksel yazılım vs. MLOps

Geleneksel yazılım, machine learning uygulamalarına kıyasla girdi verisine çok daha az bağımlıdır ve çıktıları genellikle determinizmdir; yani ne bekleyeceğimizi biliriz. Bu nedenle, machine learning modellerini işletmek ve izlemek çok daha zorludur.

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: MLOps explained end-to-end Exercise 2: Ten important MLOps life cycle steps Exercise 3: The main stages of an MLOps application Exercise 4: Important tasks in the main MLOps stages Exercise 5: The design phase Exercise 6: Postponing design phase tasks Exercise 7: Blueprint for Success: Designing Your MLOps Project Exercise 8: The development phase Exercise 9: Development tasks Exercise 10: Roles in the development phase Exercise 11: Monitoring, re-training and replacing MLOps applications Exercise 12: Characteristics of the MLOps life cycle Exercise 13: Individual responsibilities in developing an MLOps use case Exercise 14: The MLOps life cycle

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: MLOps ile ilgili iş zorlukları ve riskler Exercise 2: MLOps’u zor yapan nedir?Exercise 3: Geleneksel yazılım vs. MLOps

Geçerli Egzersiz

Exercise 4: Olası MLOps iş riskleri Exercise 5: MLOps ekipleri nasıl başarıyla çalışır Exercise 6: DevOps tarzı ekipler Exercise 7: MLOps’u uygulamadaki zorluklar Exercise 8: Bugün MLOps’un durumu Exercise 9: MLOps hakkında yanlış bir ifade Exercise 10: MLOps'un zor olmasının nedenleri

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