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Vignesh R

Menjadi anggota sejak 2024

Diamond League

43535 poin
Developing Applications with Cloud Run Functions on Google Cloud Earned Agu 29, 2024 EDT
Developing Containerized Applications on Google Cloud Earned Agu 29, 2024 EDT
Developing Applications with Google Cloud: Foundations Earned Agu 29, 2024 EDT
App Deployment, Debugging, and Performance Earned Agu 29, 2024 EDT
Dasar-Dasar Google Cloud: Infrastruktur Inti Earned Agu 28, 2024 EDT
Service Orchestration and Choreography on Google Cloud Earned Agu 28, 2024 EDT
Developing Applications with Cloud Run on Google Cloud: Fundamentals Earned Agu 28, 2024 EDT
Mengintegrasikan Aplikasi dengan Gemini 1.0 Pro di Google Cloud Earned Agu 28, 2024 EDT
Men-deploy Aplikasi Kubernetes di Google Cloud Earned Agu 27, 2024 EDT
Mengembangkan Aplikasi Serverless di Cloud Run Earned Agu 27, 2024 EDT
Menyiapkan Data untuk ML API di Google Cloud Earned Apr 20, 2024 EDT
Computer Vision Fundamentals with Google Cloud Earned Apr 18, 2024 EDT
Membangun dan Men-Deploy Solusi Machine Learning di 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 the Enterprise Earned Mar 28, 2024 EDT
Feature Engineering Earned Mar 26, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Mar 24, 2024 EDT
Launching into Machine Learning Earned Mar 20, 2024 EDT
Pengantar AI dan Machine Learning di 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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Dasar-Dasar Google Cloud: Infrastruktur Inti memperkenalkan konsep dan terminologi penting untuk bekerja dengan Google Cloud. Melalui video dan lab interaktif, kursus ini menyajikan dan membandingkan banyak layanan komputasi dan penyimpanan Google Cloud, bersama dengan resource penting dan alat pengelolaan kebijakan.

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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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Kursus singkat tentang cara mengintegrasikan aplikasi dengan model Gemini 1.0 Pro di Google Cloud ini akan membantu Anda memahami Gemini API dan model AI generatif. Kursus ini menjelaskan cara mengakses model Gemini 1.0 Pro dan Gemini 1.0 Pro Vision dari kode. Anda dapat menguji kemampuan model dengan perintah teks, gambar, dan video dari aplikasi.

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Selesaikan badge keahlian Men-deploy Aplikasi Kubernetes di Google Cloud tingkat menengah untuk menunjukkan keterampilan dalam hal berikut ini: mengonfigurasi dan membangun image container Docker, membuat dan mengelola cluster Google Kubernetes Engine (GKE), memanfaatkan kubectl untuk pengelolaan cluster yang efisien, dan men-deploy aplikasi Kubernetes dengan praktik continuous delivery (CD) yang andal.

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Selesaikan badge keahlian Mengembangkan Aplikasi Serverless di Cloud Run untuk menunjukkan keterampilan Anda dalam hal berikut: mengintegrasikan Cloud Run dengan Cloud Storage untuk pengelolaan data, membangun sistem asinkron yang tangguh menggunakan Cloud Run dan Pub/Sub, membuat gateway REST API yang didukung Cloud Run, dan membangun serta men-deploy layanan di Cloud Run. Badge keahlian merupakan badge digital eksklusif yang diberikan oleh Google Cloud sebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan praktis yang interaktif. Selesaikan kursus badge keahlian ini dan challenge lab penilaian akhir untuk menerima badge keahlian yang dapat Anda bagikan kepada jaringan Anda.

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Selesaikan badge keahlian pengantar Menyiapkan Data untuk ML API di Google Cloud untuk menunjukkan keterampilan Anda dalam hal berikut: menghapus data dengan Dataprep by Trifacta, menjalankan pipeline data di Dataflow, membuat cluster dan menjalankan tugas Apache Spark di Dataproc, dan memanggil beberapa ML API, termasuk Cloud Natural Language API, Google Cloud Speech-to-Text API, dan Video Intelligence API. Badge keahlian adalah badge digital eksklusif yang diberikan oleh Google Cloud s ebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan praktis yang interaktif. Selesaikan kursus badge keahlian ini dan challenge lab penilaian akhir, untuk menerima badge keahlian yang dapat Anda bagikan dengan jaringan Anda.

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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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Dapatkan badge keahlian tingkat menengah dengan menyelesaikan kursus Membangun dan Men-Deploy Solusi Machine Learning di Vertex AI, tempat Anda akan belajar cara menggunakan platform Vertex AI Google Cloud, AutoML, dan layanan pelatihan kustom untuk melatih, mengevaluasi, menyesuaikan, menjelaskan, serta men-deploy model machine learning. Kursus badge keahlian ini diperuntukkan bagi Data Scientist dan Engineer Machine Learning profesional. Badge keahlian adalah badge digital eksklusif yang diberikan oleh Google Cloud sebagai pengakuan atas kemahiran Anda dalam menggunakan produk dan layanan Google Cloud serta menguji kemampuan Anda dalam menerapkan pengetahuan di lingkungan praktis yang interaktif. Selesaikan Badge keahlian ini, dan challenge lab penilaian akhir, untuk menerima badge digital yang dapat Anda bagikan ke jaringan Anda.

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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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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 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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Kursus ini memperkenalkan penawaran AI dan machine learning (ML) di Google Cloud yang membangun project AI prediktif dan generatif. Kursus ini akan membahas teknologi, produk, dan alat yang tersedia di seluruh siklus proses data ke AI, yang mencakup fondasi, pengembangan, dan solusi AI. Kursus ini bertujuan membantu data scientist, developer AI, dan engineer ML meningkatkan keterampilan dan pengetahuan mereka melalui pengalaman belajar yang menarik dan latihan praktik langsung.

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