Rôles pendant la phase de développement

La phase de développement d’une application MLOps comporte quatre étapes : préparation des données, développement du modèle, évaluation du modèle, puis tests et vérification.

Vous êtes toujours chargé de développer le système d’alerte précoce pour Finance 2050. Votre équipe compte trois personnes : un data scientist, un data engineer et un software engineer. Vous devez maintenant répartir les tâches entre les membres du projet.

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<cours>MLOps pour l’entreprise</cours>

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Cet exercice fait partie du cours

<cours>MLOps pour l’entreprise</cours>

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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 expliqué de bout en bout Exercise 2: Dix étapes clés du cycle de vie MLOps Exercise 3: Les principales étapes d’une application MLOps Exercise 4: Tâches clés aux principales étapes de l’MLOps Exercise 5: La phase de conception Exercise 6: Reporter certaines tâches de la phase de conception Exercise 7: Plan gagnant : concevoir votre projet MLOps Exercise 8: La phase de développement Exercise 9: Tâches de développement Exercise 10: Rôles pendant la phase de développement

Exercice actuel

Exercise 11: Supervision, ré-entraînement et remplacement des applications MLOps Exercise 12: Caractéristiques du cycle de vie MLOps Exercise 13: Responsabilités individuelles dans le développement d’un cas d’usage MLOps Exercise 14: Le cycle de vie MLOps

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