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Vignesh R

Membro dal giorno 2024

Campionato Diamante

43535 punti
Developing Applications with Cloud Run Functions on Google Cloud Earned ago 29, 2024 EDT
Developing Containerized Applications on Google Cloud Earned ago 29, 2024 EDT
Developing Applications with Google Cloud: Foundations Earned ago 29, 2024 EDT
App Deployment, Debugging, and Performance Earned ago 29, 2024 EDT
Google Cloud Fundamentals: Core Infrastructure - Italiano Earned ago 28, 2024 EDT
Service Orchestration and Choreography on Google Cloud Earned ago 28, 2024 EDT
Developing Applications with Cloud Run on Google Cloud: Fundamentals Earned ago 28, 2024 EDT
Integrating Applications with Gemini 1.0 Pro on Google Cloud Earned ago 28, 2024 EDT
Deploy Kubernetes Applications on Google Cloud Earned ago 27, 2024 EDT
Develop Serverless Applications on Cloud Run Earned ago 27, 2024 EDT
Prepara i dati per le API ML su Google Cloud Earned apr 20, 2024 EDT
Computer Vision Fundamentals with Google Cloud Earned apr 18, 2024 EDT
Build and Deploy Machine Learning Solutions on Vertex AI Earned apr 11, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned apr 4, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned apr 3, 2024 EDT
Recommendation Systems on Google Cloud Earned apr 2, 2024 EDT
Natural Language Processing on Google Cloud Earned apr 1, 2024 EDT
Production Machine Learning Systems Earned mar 30, 2024 EDT
Machine learning in azienda Earned mar 28, 2024 EDT
Feature engineering Earned mar 26, 2024 EDT
Crea, addestra ed esegui il deployment di modelli ML tramite Keras su Google Cloud Earned mar 24, 2024 EDT
Launching into Machine Learning - Italiano Earned mar 20, 2024 EDT
Introduzione all'AI e al machine learning su Google Cloud Earned mar 7, 2024 EST

In this course, you learn about Cloud Run functions, Google's serverless, fully-managed functions as a service (FaaS) product that lets you implement single-purpose function code that reponds to HTTP requests and events from your cloud infrastructure.

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In this course, you learn about containers and how to build, and package container images. The content in this course includes best practices for creating and securing containers, and provides an introduction to Cloud Run and Google Kubernetes Engine for application developers.

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In this course, you learn the fundamentals of application development on Google Cloud. You learn best practices for cloud applications, and how to select compute and data options to match your application use cases. You're introduced to generative AI and how it's used to help build applications. You learn about authentication and authorization, application deployment, continuous integration and delivery, and monitoring and performance tuning for your applications running in Google Cloud. Using lectures and hands-on labs, you learn how to get started building and running applications on Google Cloud.

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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.

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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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This course introduces you to event-based applications and teaches you how to use service orchestration and choreography to coordinate microservices. Using lectures and hands-on labs, you learn how to use Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler to build microservices applications on Google Cloud.

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This course introduces the Cloud Run serverless platform for running applications. In this course, you learn about the fundamentals of Cloud Run, its resource model and the container lifecycle. You learn about service identities, how to control access to services, and how to develop and test your application locally before deploying it to Cloud Run. The course also teaches you how to integrate with other services on Google Cloud so you can build full-featured applications.

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This short course on integrating applications with Gemini 1.0 Pro models on Google Cloud helps you discover the Gemini API and its generative AI models. The course teaches you how to access the Gemini 1.0 Pro and Gemini 1.0 Pro Vision models from code. It lets you test the capabilities of the models with text, image, and video prompts from an app.

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Complete the intermediate Deploy Kubernetes Applications on Google Cloud skill badge course to demonstrate skills in the following: Configuring and building Docker container images.Creating and managing Google Kubernetes Engine (GKE) clusters.Utilizing kubectl for efficient cluster management.Deploying Kubernetes applications with robust continuous delivery (CD) practices.

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Complete the intermediate Develop Serverless Applications on Cloud Run skill badge to demonstrate skills in the following: integrating Cloud Run with Cloud Storage for data management, architecting resilient asynchronous systems using Cloud Run and Pub/Sub, constructing REST API gateways powered by Cloud Run, and building and deploying services on Cloud Run. 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 course and the final assessment challenge lab to receive a skill badge that you can share with your network.

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Ottieni il corso intermedio con badge delle competenze Prepara i dati per le API ML su Google Cloud per dimostrare le tue competenze nei seguenti ambiti: pulizia dei dati con Dataprep di Trifacta, esecuzione delle pipeline di dati in Dataflow, creazione dei cluster ed esecuzione dei job Apache Spark in Dataproc e richiamo delle API ML tra cui l'API Cloud Natural Language, l'API Google Cloud Speech-to-Text e l'API Video Intelligence. Un badge delle competenze è un badge digitale esclusivo rilasciato da Google Cloud come riconoscimento della tua competenza nell'uso di prodotti e servizi Google Cloud dopo aver messo alla prova la tua capacità di applicare le tue conoscenze in un ambiente interattivo pratico. Completa questo corso con badge delle competenze e il Challenge Lab finale di valutazione per ricevere un badge delle competenze da condividere con la tua rete.

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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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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.

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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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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 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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Questo corso adotta un approccio pratico reale al flusso di lavoro ML attraverso un case study. Un team ML è chiamato a rispondere a numerosi requisiti aziendali e ad affrontare vari casi d'uso ML. Deve comprendere gli strumenti necessari per la gestione e la governance dei dati e considerare l'approccio migliore per la pre-elaborazione dei dati. Al team vengono presentate tre opzioni per creare modelli ML per due casi d'uso. Il corso spiega perché il team utilizzerà AutoML, BigQuery ML o l'addestramento personalizzato per raggiungere i propri obiettivi.

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Questo corso illustra i vantaggi dell'utilizzo di Vertex AI Feature Store, come migliorare l'accuratezza dei modelli di ML e come trovare le colonne di dati che forniscono le caratteristiche più utili. Il corso include inoltre contenuti e lab sul feature engineering utilizzando BigQuery ML, Keras e TensorFlow.

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Questo corso tratta la creazione di modelli ML con TensorFlow e Keras, il miglioramento dell'accuratezza dei modelli ML e la scrittura di modelli ML per l'uso su larga scala.

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Il corso inizia con una discussione sui dati: come migliorare la qualità dei dati ed eseguire analisi esplorative dei dati. Descriveremo Vertex AI AutoML e come creare, addestrare ed eseguire il deployment di un modello di ML senza scrivere una sola riga di codice. Comprenderai i vantaggi di Big Query ML. Discuteremo quindi di come ottimizzare un modello di machine learning (ML) e di come la generalizzazione e il campionamento possano aiutare a valutare la qualità dei modelli di ML per l'addestramento personalizzato.

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Questo corso presenta le offerte di intelligenza artificiale (AI) e machine learning (ML) su Google Cloud per la creazione di progetti di AI predittiva e generativa. Esplora le tecnologie, i prodotti e gli strumenti disponibili durante tutto il ciclo di vita data-to-AI, includendo le basi, lo sviluppo e le soluzioni di AI. Ha lo scopo di aiutare data scientist, sviluppatori di AI e ML engineer a migliorare le proprie abilità e conoscenze attraverso attività di apprendimento coinvolgenti ed esercizi pratici.

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