Partecipa Accedi

Abhijeet Nagar

Membro dal giorno 2026

Campionato Oro

2004 punti
Arcade Trail: Containers & Load Balancing Earned ott 20, 2026 EDT
Arcade Voyage: Language, Voice, and Multimodal Processing Earned ott 14, 2026 EDT
AI responsabile per sviluppatori: interpretabilità e trasparenza Earned ott 14, 2026 EDT
AI responsabile per sviluppatori: equità e bias Earned ott 14, 2026 EDT
DevOps Essentials Earned set 8, 2026 EDT
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation Earned lug 21, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI Earned lug 18, 2026 EDT

Learn to build and scale production-ready cloud environments. In this trail, you'll package apps with Docker, deploy workloads on Google Kubernetes Engine, and orchestrate containerized services. Next, configure regional internal proxy and global HTTPS load balancers, then set up Cloud Monitoring to track health and latency. Pass both challenge labs to prove your ability to deploy resilient applications and route global traffic efficiently.

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Transforming unstructured multimedia into searchable, structured data is the core of modern cognitive computing. In this voyage, you'll analyze sentiment and extract entities using the Cloud Natural Language API, pairing language workflows with Apache Spark to demonstrate big-data scalability. From there, you'll branch into multimodal synthesis—generating lifelike audio using Text-to-Speech, bridging global barriers with the Cloud Translation API, and annotating video streams via Video Intelligence. Complete both challenge labs to prove you can deploy pre-trained models that read, speak, translate, and inspect content at scale.

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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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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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Obtain a competitive advantage through DevOps. DevOps is an organizational and cultural movement that aims to increase software delivery velocity, improve service reliability, and build shared ownership among software stakeholders. In this course you will learn how to use Google Cloud to improve the speed, stability, availability, and security of your software delivery capability. DevOps Research and Assessment has joined Google Cloud. How does your team measure up? Take this five question multiple-choice quiz and find out!

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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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