De MLOps-levenscyclus

Hoewel MLOps-toepassingen er niet allemaal hetzelfde uitzien, doorlopen ze wel vergelijkbare stappen die (enkele keren) in dezelfde volgorde moeten worden uitgevoerd om te zorgen dat modellen nauwkeurig werken, stabiel blijven en zakelijke waarde opleveren.

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MLOps voor bedrijven

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MLOps voor bedrijven

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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 van begin tot eind uitgelegd Exercise 2: Tien belangrijke stappen in de MLOps-levenscyclus Exercise 3: De belangrijkste fasen van een MLOps-toepassing Exercise 4: Belangrijke taken in de hoofdfasen van MLOps Exercise 5: De ontwerpfase Exercise 6: Taken uit de ontwerpfase uitstellen Exercise 7: Blauwdruk voor succes: ontwerp je MLOps-project Exercise 8: De ontwikkelfase Exercise 9: Ontwikkeltaken Exercise 10: Rollen in de ontwikkelfase Exercise 11: Monitoring, opnieuw trainen en vervangen van MLOps-toepassingen Exercise 12: Kenmerken van de MLOps-levenscyclus Exercise 13: Individuele verantwoordelijkheden bij het ontwikkelen van een MLOps-usecase Exercise 14: De MLOps-levenscyclus

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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