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Apply your skills in Google Cloud console

Kamurasi Jordan Arthur

Member since 2024

Silver League

24299 points
Build a Certification Study Guide: PMLE Earned Oca 31, 2026 EST
Geliştiriciler İçin Sorumlu Yapay Zeka: Gizlilik ve Güvenlik Earned Eki 17, 2024 EDT
Geliştiriciler için Sorumlu Yapay Zeka: Yorumlanabilirlik ve Şeffaflık Earned Eki 2, 2024 EDT
Geliştiriciler İçin Sorumlu Yapay Zeka: Adalet ve Önyargı Earned Eki 1, 2024 EDT
Üretken Yapay Zeka İçin Makine Öğrenimi Operasyonları (MLOps) Earned Eyl 18, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned Eyl 18, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned Eyl 18, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned Eyl 17, 2024 EDT
Computer Vision Fundamentals with Google Cloud Earned Eyl 16, 2024 EDT
Production Machine Learning Systems Earned Eyl 5, 2024 EDT
Feature Engineering Earned Ağu 3, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Tem 25, 2024 EDT
Launching into Machine Learning Earned Tem 24, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud Earned Tem 11, 2024 EDT

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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Bu kursta, yapay zekada gizlilik ve güvenlik konuları ele alınmaktadır. Kurs boyunca, Google Cloud ürünleri ve açık kaynak araçları kullanarak yapay zekayla ilgili önerilen gizlilik ve güvenlik uygulamalarını benimsemenize yardımcı olacak pratik yöntemler ile araçları tanıyacaksınız.

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Bu kursta yapay zekanın yorumlanabilirliği ve şeffaflığı kavramlarıyla ilgili temel bilgiler sunulmaktadır. Ayrıca geliştiriciler ve mühendisler için yapay zeka sistemlerinde şeffaflığın önemi ele alınmaktadır. Kurs boyunca, veri ve yapay zeka modellerinde yorumlanabilirliğin ve şeffaflığın sağlanmasına yardımcı olacak pratik yöntemleri ve araçları tanıyacaksınız.

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Bu kursta, sorumlu yapay zeka kavramı ve yapay zeka ilkeleri tanıtılmaktadır. Kurs, adalet ve önyargıyı pratik şekilde tanımlama teknikleri ile yapay zeka/makine öğrenimi uygulamalarında önyargının azaltılması konularını ele almaktadır. Kurs boyunca, Google Cloud ürünleri ve açık kaynaklı araçları kullanarak sorumlu yapay zekayla ilgili en iyi uygulamaları benimsemenize yardımcı olacak pratik yöntemler ve araçları tanıyacaksınız.

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Bu kurs, MLOps ekiplerinin üretken yapay zeka modellerini dağıtırken ve yönetirken karşılaştığı zorlukların üstesinden gelmek için gereken bilgi ve araçları sağlamaktadır. Ayrıca yapay zeka ekiplerinin, MLOps süreçlerini kolaylaştırıp üretken yapay zeka projelerinde başarıya ulaşması için Vertex AI'ın nasıl yardımcı olduğunu öğrenmenizi amaçlamaktadır.

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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 introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.

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