La qualità fa risparmiare

DataVK Corp è una startup che vuole sviluppare un’applicazione basata sull’AI per il riconoscimento di immagini.

Dispone di un insieme di dati di immagini limitato per addestrare il modello. Come responsabile del progetto di AI, stai esplorando tecniche per massimizzare l’utilità del tuo insieme di dati.

Seleziona i due fattori pertinenti alla qualità dei dati in base alle informazioni fornite.

Nota: i modelli di riconoscimento di immagini si basano fortemente su insiemi di dati etichettati. Assicurati che i fattori di qualità dei dati tengano conto di questo aspetto.

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Strategia per l'Intelligenza Artificiale (AI)

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Strategia per l'Intelligenza Artificiale (AI)

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The chapter underpins the intricate relationships between business, data, and AI strategies. It then goes deeper into how an effective AI strategy begins with a clear vision and the role of a focused action plan in driving an organization's strategic objectives. You will also learn the skills that go into making a successful AI strategist, outlining their responsibilities and contributions towards achieving the business goals.

Exercise 1: Different types of strategies Exercise 2: Charting the course Exercise 3: Which strategy?Exercise 4: Pillars of an Effective AI Strategy Exercise 5: Navigating the strategic water Exercise 6: The pillars of AI strategy Exercise 7: Role of an AI Strategist Exercise 8: Onboarding an AI strategist Exercise 9: A successful AI strategist

This chapter sharpens the business acumen by distinguishing AI software from traditional software, ensuring the effective use of resources for pertinent business challenges. It further explains the key business drivers in identifying the most impactful AI initiatives and shares how to set the right AI goals. Alongside explaining the significance of ROI, learners will understand the challenges and drivers of assessing ROI.

Exercise 1: When should one think of AI?Exercise 2: Adapting to the AI revolution Exercise 3: Understanding software paradigms Exercise 4: Uncovering core business drivers Exercise 5: Foundations of a successful AI plan Exercise 6: AI suitability test Exercise 7: Distinguishing AI-friendly scenarios Exercise 8: Setting realistic business goals Exercise 9: AI goals with the SMART lens Exercise 10: A test of time and ambition Exercise 11: Assessing the ROI of an AI initiative Exercise 12: AI's dual-faced ROI Exercise 13: Tracing revenue roots

This chapter explains different components of a successful AI strategy, such as innovation and building the right culture for high-performing teams. It also underscores the importance of AI literacy, covering the pivotal do’s and don’ts of AI usage. While innovation is essential, understanding the potential AI-associated risks and asking the right questions is crucial to building a robust risk assessment framework for AI.

Exercise 1: Costruire una cultura dell'AI Exercise 2: Tentativi di AI diversificati Exercise 3: Esplorazione e sperimentazione Exercise 4: Creare team AI ad alte prestazioni Exercise 5: Individuare il gap Exercise 6: Il trio essenziale Exercise 7: Ottenere i dati giusti Exercise 8: Trovare il giusto mix Exercise 9: La qualità fa risparmiare

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Exercise 10: Valutazione del rischio AI Exercise 11: Privacy ed etica Exercise 12: Conformità alle normative Exercise 13: Conformità Exercise 14: Ciclo di vita di un progetto di AI

In this chapter, we discuss the role of feasibility workshops and emphasize initiating a focused PoC to gauge AI's potential before a full-scale rollout. We will also highlight what it takes to build scalable AI systems and the significance of MLOps in scaling it right. Ultimately, the chapter underscores the influence of executive sponsors and AI champions in fostering AI adoption.

Exercise 1: It all starts with a PoC Exercise 2: Decision points for AI diagnostic tool Exercise 3: Evaluating essentials of an AI initiative Exercise 4: Scaling beyond PoCs Exercise 5: Sorting AI scaling obstacles Exercise 6: Scaling an AI system Exercise 7: MLOps Exercise 8: Navigating MLOps Exercise 9: Sorting architectural practices Exercise 10: Barriers to adoption Exercise 11: Assigning roles in AI transformation Exercise 12: Blueprint for AI transformation Exercise 13: It's time to wrap-up!