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Miguel Vieira

Member since 2023

Silver League

31125 points
Preparing for your Professional Data Engineer Journey Earned Eyl 14, 2024 EDT
Dikkat Mekanizması Earned May 8, 2024 EDT
Create ML Models with BigQuery ML Earned Şub 16, 2024 EST
Recommendation Systems on Google Cloud Earned Şub 12, 2024 EST
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned Şub 10, 2024 EST
Machine Learning in the Enterprise Earned Şub 10, 2024 EST
Production Machine Learning Systems Earned Şub 10, 2024 EST
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama Earned Şub 10, 2024 EST
Machine Learning Operations (MLOps): Getting Started Earned Şub 3, 2024 EST
Natural Language Processing on Google Cloud Earned Oca 29, 2024 EST
Feature Engineering Earned Oca 15, 2024 EST
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Oca 11, 2024 EST
Launching into Machine Learning Earned Ara 28, 2023 EST
Introduction to AI and Machine Learning on Google Cloud Earned Ara 24, 2023 EST

This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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Bu kursta nöral ağların, giriş sırasının belirli bölümlerine odaklanmasına olanak tanıyan güçlü bir teknik olan dikkat mekanizması tanıtılmaktadır. Kursta, dikkat mekanizmasının çalışma şeklini ve makine öğrenimi, metin özetleme ve soru yanıtlama gibi çeşitli makine öğrenimi görevlerinin performansını artırmak için nasıl kullanılabileceğini öğreneceksiniz.

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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

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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 takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

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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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Giriş düzeyindeki Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Dataprep by Trifacta ile veri temizleme, Dataflow'da veri ardışık düzenleri çalıştırma, Dataproc'ta küme oluşturma ve Apache Spark işleri çalıştırma ve makine öğrenimi API'lerini (Cloud Natural Language API, Google Cloud Speech-to-Text API ve Video Intelligence API dahil olmak üzere) çağırma. Beceri rozeti, Google Cloud ürün ve hizmetlerindeki uzmanlık düzeyiniz karşılığında Google Cloud tarafından verilen özel bir dijital rozettir. Bilgilerinizi, etkileşimli ve uygulamalı bir ortamda kullanma becerinizi test eder. Ağınızla paylaşabileceğiniz bir beceri rozeti kazanmak için bu beceri rozeti kursunu ve son değerlendirme niteliğindeki yarışma laboratuvarını tamamlayın.

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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 introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

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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 the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.

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