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Ashraf Mantur

Membro dal giorno 2026

Campionato Oro

3528 punti
AI responsabile per sviluppatori: interpretabilità e trasparenza Earned ott 26, 2026 EDT
Organize and Govern Data with Knowledge Catalog Earned ott 25, 2026 EDT
Orchestrate Multi-agent Workflows with Gemini Enterprise Earned ott 23, 2026 EDT
AI responsabile per sviluppatori: equità e bias Earned ott 23, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation Earned ott 23, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI Earned ott 23, 2026 EDT

Questo corso introduce i concetti di interpretabilità e la trasparenza dell'AI. Parla dell'importanza della trasparenza dell'AI per sviluppatori ed engineer. Illustra metodi e strumenti pratici per aiutare a raggiungere interpretabilità e trasparenza sia nei dati che nei modelli di AI.

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Complete the introductory Organize and Govern Data with Knowledge Catalog skill badge to demonstrate skills in the following: creating Knowledge Catalog assets, creating aspect types, and applying aspects to entries in Knowledge Catalog.

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Complete the introductory Orchestrate Multi-Agent Workflows with Gemini Enterprise skill badge to demonstrate skills in the following: using agentic assistants powered by Gemini Enterprise to unify data across first- and third-party sources, develop multimedia marketing materials, and automate business actions across systems. Skill badges validate your practical knowledge on specific products through hands-on labs and challenge assessments. Earn a badge by completing a course or jump straight into the challenge lab to get your badge today. Badges prove your proficiency, enhance your professional profile, and ultimately lead to increased career opportunities. Visit your profile to track badges you’ve earned. Explore other content in the Gemini Agent Ready (GEAR) program.

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Questo corso introduce i concetti di AI responsabile e i principi dell'AI. Tratta le tecniche per identificare sostanzialmente l'equità e i bias e mitigare i bias nelle pratiche di AI/ML. Illustra metodi e strumenti pratici per implementare le best practice dell'AI responsabile utilizzando gli strumenti open source e i prodotti Google Cloud.

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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production. Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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