Yapay Zeka Değerinin Temelleri

Yapay zeka, bir işletmeyi desteklemek için birçok şekilde kullanılabilir. Kullanımı sayesinde, işletmeler genellikle dört alanda kategorize edilen faydalar elde edebilir.

Yapay zekanın sağlayabileceği temel değer alanları nelerdir?

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İşletmelerde Yapay Zeka Çözümlerinin Uygulanması

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İşletmelerde Yapay Zeka Çözümlerinin Uygulanması

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Kursa Ücretsiz Başla

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: Yapay zekanın bir işletme için faydaları Exercise 2: Yapay Zeka Değerinin Temelleri

Geçerli egzersiz

Exercise 3: İş değeri mi yoksa sahip olmak güzel mi?Exercise 4: Yapay Zekanın Sınırlamaları Exercise 5: Yapay zekanın sınırlamalarını tanımlama Exercise 6: Sınırlama mı yoksa fayda mı?Exercise 7: Döngü içinde insan Exercise 8: Yapay zeka için kullanım alanları Exercise 9: Yaygın iş kullanım durumları Exercise 10: Bir senaryoda yapay zeka avantajı Exercise 11: Yapay zeka için bir kullanım durumu belirleyin Exercise 12: İyi bir kullanım senaryosunun ilkeleri Exercise 13: Kullanım durumlarını sınıflandırın Exercise 14: Bir görevi parçalarına ayırma

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