Ruoli nella fase di sviluppo

Nella fase di sviluppo di un’applicazione MLOps ci sono quattro passaggi: preparazione dei dati, sviluppo del modello, valutazione del modello e test e verifica.

Stai ancora lavorando allo sviluppo del sistema di allerta precoce per Finance 2050. Il tuo team è composto da tre persone: un data scientist, un data engineer e un software engineer. Ora devi assegnare i compiti ai membri del progetto.

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MLOps per il business

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MLOps per il business

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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 spiegato dall’inizio alla fine Exercise 2: Dieci passaggi importanti del ciclo di vita MLOps Exercise 3: Le fasi principali di un’applicazione MLOps Exercise 4: Attività importanti nelle principali fasi di MLOps Exercise 5: La fase di progettazione Exercise 6: Rinviare le attività della fase di design Exercise 7: Blueprint per il successo: progetta il tuo progetto di MLOps Exercise 8: La fase di sviluppo Exercise 9: Attività di sviluppo Exercise 10: Ruoli nella fase di sviluppo

Esercizio in corso

Exercise 11: Monitoraggio, ri-addestramento e sostituzione delle applicazioni MLOps Exercise 12: Caratteristiche del ciclo di vita MLOps Exercise 13: Responsabilità individuali nello sviluppo di un caso d’uso MLOps Exercise 14: Il ciclo di vita dell’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