Principes d'un bon cas d'utilisation

Dans cette leçon, vous avez appris les cinq principes d'un bon cas d'utilisation, qui peuvent être utilisés pour sélectionner une preuve de concept initiale. Chacun d'entre eux permet de mieux cerner le problème exact à résoudre, les personnes à aider, les exigences en matière de données et d'autres éléments importants de la solution.

Identifiez dans les propositions ci-dessous celles qui sont des idées issues de ces cinq principes.

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Learn what AI is, how it has evolved, and how it is different from other non-AI data solutions. In this chapter, you will begin building a framework for how to implement AI into your business, including key elements to an AI strategy, phases for implementation, and basic technical design. Finally, you'll learn about the importance of Responsible AI governance throughout the entire process.

Exercise 1: Overview of AI Exercise 2: Do you know AI?Exercise 3: The phases of an AI solution Exercise 4: 10,000 ft view: how does AI work?Exercise 5: Still know AI?Exercise 6: Generative AI vs. Other AI Exercise 7: Components of an AI solution Exercise 8: Responsible AI Exercise 9: Why is Responsible AI important?Exercise 10: Common Principles of Responsible AI Exercise 11: Assessing risk for a new AI solution

You now know what AI is and how to think about it responsibly. It's time to gain a better sense of its benefits, limitations, and use cases in business. In this chapter, you'll do just that! You'll understand the value AI can bring to an organization as well as the areas it lacks in. You'll go through some high-level use cases across different departments in a business. Finally, you'll learn about principles for choosing a great use case for an AI solution.

Exercise 1: Les avantages de l'IA pour une entreprise Exercise 2: Les piliers de valeur de l'IA Exercise 3: Valeur commerciale ou pas indispensable ?Exercise 4: Limites de l’IA Exercise 5: Identifier les limites de l'IA Exercise 6: Limite ou avantage ?Exercise 7: L'humain dans la boucle Exercise 8: Cas d'utilisation de l'IA Exercise 9: Cas d'utilisation métier courants Exercise 10: L'avantage de l'IA dans un scénario Exercise 11: Identifier un cas d'utilisation pour l'IA Exercise 12: Principes d'un bon cas d'utilisation

Exercice actuel

Exercise 13: Classez les cas d'utilisation Exercise 14: Décomposition d'une tâche

With use cases in mind, it's time to build a proof of concept! This is a small project that will determine the feasibility and estimate further business value if fully implemented. In this chapter, you will learn the phases of a POC, what makes them successful, and the important components to think about regarding technical infrastructure and data. Finally, you'll learn about the types of roles required for your POC project team.

Exercise 1: Getting started with a POC Exercise 2: Phases of a POC Exercise 3: What makes a successful POC?Exercise 4: Questions to clarify goals Exercise 5: Data Exercise 6: Importance of data Exercise 7: Profiling and evaluating data Exercise 8: Biased data Exercise 9: Infrastructure Exercise 10: Questions about the POC infrastructure Exercise 11: The AI development environment Exercise 12: Buy or build?Exercise 13: Building the project team Exercise 14: Types of roles Exercise 15: Responsibilities

In this final chapter of the course, you will learn the final steps of an AI solution implementation - assessing the POC, scaling, and aligning culture and skills. You will understand the areas in which a POC can be evaluated as well as what are good indicators for moving forward with scaling the solution. You will go over the big components to focus on for scaling to a full implementation, including requirements for culture and upskilling folks in your business.

Exercise 1: Measuring the POC Exercise 2: What makes a POC successful?Exercise 3: Classifying metrics in e-commerce scenario Exercise 4: Assessing the POC Exercise 5: Components for scaling Exercise 6: Balancing value and feasibility Exercise 7: Make a decision about scaling Exercise 8: Considerations for full implementation Exercise 9: Focus areas for a full implementation Exercise 10: Monitoring and model drift Exercise 11: DevOps, MLOps, or compliance?Exercise 12: Adoption and skilling Exercise 13: Reasons for no financial benefits Exercise 14: Resistance to AI Exercise 15: Benefits for change management, culture, and upskilling Exercise 16: Wrap-up