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Applica le tue competenze nella console Google Cloud

Harman Singh

Membro dal giorno 2026

Campionato Oro

1660 punti
Build a Certification Study Guide: PMLE Earned feb 1, 2026 EST
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned feb 1, 2026 EST
Machine Learning Operations (MLOps) for Generative AI Earned feb 1, 2026 EST
Create Embeddings, Vector Search, and RAG with BigQuery Earned gen 29, 2026 EST
Implement CI/CD Pipelines on Google Cloud Earned gen 28, 2026 EST
Work Meets Play: A Cloud That Cares Earned gen 26, 2026 EST
Develop Serverless Apps with Firebase Earned gen 24, 2026 EST

Learn how to use NotebookLM to create a personalized study guide for the Professional Machine Learning Engineer certification exam (PMLE). You'll review NotebookLM features, create a notebook, and use the study guide to practice for a certification exam.

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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 Vertex AI 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 Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.

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Earn the intermediate skill badge by completing the Implement CI/CD Pipelines on Google Cloud skill badge course where you learn how to use Artifact Registry, Cloud Build, and Cloud Deploy. You interact with the Google Cloud console, Google Cloud CLI, Cloud Run, and GKE. This course teaches you how to build continuous integration pipelines, store and secure artifacts, scan for vulnerabilities, attest to the validity of approved releases. Additionally, you get hands-on experience deploying applications to both GKE and Cloud Run.

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Infrastructure is the backbone of modern care. Following the blueprint set by Manipal Hospitals, this challenge dives into the backend architecture that powers healthcare at scale. From automating CI/CD pipelines to securing containerized workloads on GKE, you will explore how cloud-native tools directly impact real-world outcomes—like faster prescriptions and safer transitions of care. Learn to architect for reliability, ensuring that when the demand spikes, your system doesn't just run; it performs.

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Complete the intermediate Develop Serverless Apps with Firebase skill badge course to demonstrate skills in the following: architecting and building serverless web applications with Firebase, utilizing Firestore for database management, automating deployment processes using Cloud Build, and integrating Google Assistant functionality into your applications.

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