Yapay zekayı biliyor musun?

Yapay Zeka (YZ) yeni bir teknoloji değildir, ancak son yıllarda heyecan verici gelişmeler ortaya çıkmıştır. YZ'nin en yeni biçimi, insan olarak bilgiyi işleme şeklimizden - hatta şu anda bu metni okuma şeklinizden bile - yola çıkılarak modelleniyor! İşletmelerde ve yaşamlarımızda birçok görev ve kullanım durumu için kullanılabilir.

Yapay zeka ve yapay zeka çözümleri ile ilgili aşağıdaki ifadelerden hangisi doğrudur?

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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: Yapay Zekaya Genel Bakış Exercise 2: Yapay zekayı biliyor musun?

Geçerli egzersiz

Exercise 3: Bir yapay zeka çözümünün aşamaları Exercise 4: 10.000 fit perspektifinden: Yapay zeka nasıl çalışır?Exercise 5: Hala yapay zekayı biliyor musun?Exercise 6: Üretken Yapay Zeka ile Diğer Yapay Zeka Exercise 7: Yapay zeka çözümünün bileşenleri Exercise 8: Sorumlu Yapay Zeka Exercise 9: Sorumlu Yapay Zeka neden önemlidir?Exercise 10: Sorumlu Yapay Zekanın Ortak İlkeleri Exercise 11: Yeni bir yapay zeka çözümü için risk değerlendirmesi

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: The benefits of AI for a business Exercise 2: Pillars of AI Value Exercise 3: Business value or nice to have?Exercise 4: Limitations of AI Exercise 5: Identify limitations of AI Exercise 6: Limitation or benefit?Exercise 7: Human-in-the-loop Exercise 8: Use cases for AI Exercise 9: Common business use cases Exercise 10: AI advantage in a scenario Exercise 11: Identify a use case for AI Exercise 12: Principles of a good use case Exercise 13: Classify use cases Exercise 14: Deconstructing a task

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