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Lex Xai

Membro dal giorno 2022

Campionato Diamante

26215 punti
Badge per Machine Learning Operations (MLOps): Getting Started Machine Learning Operations (MLOps): Getting Started Earned apr 7, 2024 EDT
Badge per Recommendation Systems on Google Cloud Recommendation Systems on Google Cloud Earned apr 3, 2024 EDT
Badge per Natural Language Processing on Google Cloud Natural Language Processing on Google Cloud Earned mar 26, 2024 EDT
Badge per Computer Vision Fundamentals with Google Cloud Computer Vision Fundamentals with Google Cloud Earned mar 25, 2024 EDT
Badge per Production Machine Learning Systems Production Machine Learning Systems Earned mar 23, 2024 EDT
Badge per Machine Learning in the Enterprise Machine Learning in the Enterprise Earned mar 20, 2024 EDT
Badge per Feature Engineering Feature Engineering Earned mar 9, 2024 EST
Badge per TensorFlow on Google Cloud TensorFlow on Google Cloud Earned mar 5, 2024 EST
Badge per Launching into Machine Learning Launching into Machine Learning Earned feb 26, 2024 EST
Badge per Introduction to AI and Machine Learning on Google Cloud - Italiano Introduction to AI and Machine Learning on Google Cloud - Italiano Earned feb 18, 2024 EST
Badge per Implement Load Balancing on Compute Engine Implement Load Balancing on Compute Engine Earned lug 6, 2023 EDT
Badge per Reliable Google Cloud Infrastructure: Design and Process - Italiano Reliable Google Cloud Infrastructure: Design and Process - Italiano Earned lug 6, 2023 EDT
Badge per Developing a Google SRE Culture Developing a Google SRE Culture Earned lug 2, 2023 EDT
Badge per Google Cloud Fundamentals: Core Infrastructure - Italiano Google Cloud Fundamentals: Core Infrastructure - Italiano Earned giu 1, 2023 EDT
Badge per Perform Foundational Infrastructure Tasks in Google Cloud Perform Foundational Infrastructure Tasks in Google Cloud Earned mag 13, 2023 EDT
Badge per Google Cloud Essentials Google Cloud Essentials Earned feb 8, 2023 EST

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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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 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 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 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 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 designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

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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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Questo corso presenta le offerte di intelligenza artificiale (IA) e machine learning (ML) su Google Cloud che supportano il ciclo di vita dai dati all'IA attraverso gli elementi di base dell'IA, lo sviluppo dell'IA e le soluzioni per l'IA. Esplora le tecnologie, i prodotti e gli strumenti disponibili per creare un modello ML, una pipeline ML e un progetto di IA generativa in base ai diversi obiettivi degli utenti, tra cui data scientist, sviluppatori di IA e ML engineer.

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Complete the introductory Implement Load Balancing on Compute Engine skill badge to demonstrate skills in the following: writing gcloud commands and using Cloud Shell, creating and deploying virtual machines in Compute Engine, and configuring network and HTTP load balancers. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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Questo corso spiega agli studenti come creare soluzioni efficienti e ad alta affidabilità su Google Cloud utilizzando pattern di progettazione comprovati. È la continuazione dei corsi Architecting with Google Compute Engine o Architecting with Google Kubernetes Engine e presuppone che si abbia esperienza pratica con le tecnologie esaminate in uno dei due corsi. Attraverso una combinazione di presentazioni, attività di progettazione e lab pratici, i partecipanti impareranno a definire e bilanciare i requisiti aziendali e tecnici per progettare deployment Google Cloud altamente affidabili, altamente disponibili, sicuri ed economicamente convenienti.

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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.

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Google Cloud Fundamentals: Core Infrastructure introduce concetti e terminologia importanti per lavorare con Google Cloud. Attraverso video e lab pratici, questo corso presenta e confronta molti dei servizi di computing e archiviazione di Google Cloud, insieme a importanti strumenti di gestione delle risorse e dei criteri.

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Se sei uno sviluppatore cloud alle prime armi e vuoi mettere in pratica le conoscenze acquisite con "Getting Started - Create and Manage Cloud Resources", questo Laboratorio fa al caso tuo. Acquisirai esperienza pratica con i lab che si concentrano su Cloud Storage e altri servizi applicativi principali, tra cui Stackdriver e Cloud Functions. Completando questo Laboratorio, svilupperai competenze importanti applicabili a qualsiasi iniziativa Google Cloud. Completa questo Laboratorio, compreso il Challenge Lab finale, per ricevere un esclusivo badge digitale Google Cloud. I video di un minuto ti guideranno attraverso i concetti chiave di questi lab..

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In this introductory-level Quest, you will get hands-on practice with the Google Cloud’s fundamental tools and services. Google Cloud Essentials is the recommended first Quest for the Google Cloud learner - you will come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features. 1-minute videos walk you through key concepts for each lab.

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