Reliable Google Cloud Infrastructure: Design and Process - Bahasa Indonesia
Earned Jul 6, 2023 EDT
Developing a Google SRE Culture
Earned Jul 2, 2023 EDT
Google Cloud Fundamentals: Core Infrastructure - Bahasa Indonesia
Earned Jun 1, 2023 EDT
Perform Foundational Infrastructure Tasks in Google Cloud
Earned Mei 13, 2023 EDT
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.
Pelajari lebih lanjut
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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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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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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Kursus ini membekali peserta dengan keterampilan untuk membuat solusi yang sangat andal dan efisien di Google Cloud menggunakan pola desain yang telah terbukti. Kursus ini merupakan kelanjutan dari kursus Architecting with Google Compute Engine atau Architecting with Google Kubernetes Engine dan memberikan pengalaman interaktif dengan teknologi yang dibahas dalam kursus tersebut. Melalui kombinasi presentasi, aktivitas desain, dan lab interaktif, peserta akan mempelajari cara menentukan dan menyeimbangkan kebutuhan bisnis dan teknis untuk merancang deployment Google Cloud yang sangat andal, sangat tersedia, aman, dan hemat biaya.
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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 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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Jika Anda adalah developer cloud pemula yang menginginkan praktik langsung di luar Getting Started - Create and Manage Cloud Resources, artinya quest ini cocok untuk Anda. Anda akan mendapatkan pengalaman praktik melalui lab yang mendalami Cloud Storage dan layanan aplikasi utama lainnya, seperti Stackdriver dan Cloud Functions. Dengan mengambil quest ini, Anda dapat mengembangkan kemampuan berharga yang dapat diterapkan di semua inisiatif Google Cloud. Selesaikan quest ini, termasuk challenge lab di akhir quest, untuk menerima badge digital eksklusif dari Google Cloud. Tersedia video berdurasi 1 menit yang akan membantu Anda memahami konsep utama lab-lab ini.
Pelajari lebih lanjut
Dalam quest level pendahuluan ini, Anda akan mendapatkan praktik langsung dengan aneka fitur dan layanan dasar Google Cloud Platform. Dasar-Dasar GCP adalah Quest pertama yang direkomendasikan bagi peserta kursus Google Cloud—Anda dapat memulai dengan pengetahuan yang minim atau tanpa pengetahuan sama sekali tentang cloud, dan selesai dengan pengalaman praktis yang dapat diterapkan pada project GCP pertama Anda. Mulai dari menulis perintah Cloud Shell dan menerapkan mesin virtual pertama Anda, hingga menjalankan aplikasi di Kubernetes Engine atau dengan load balancing, Dasar-Dasar GCP merupakan pengenalan terbaik pada fitur-fitur dasar platform cloud. Setiap lab disertai video berdurasi 1 menit yang akan memandu Anda memahami berbagai konsep penting.
Machine Learning Operations (MLOps): Getting Started
Earned Apr 7, 2024 EDT
Recommendation Systems on Google Cloud
Earned Apr 3, 2024 EDT
Natural Language Processing on Google Cloud
Earned Mar 26, 2024 EDT
Computer Vision Fundamentals with Google Cloud
Earned Mar 25, 2024 EDT
Production Machine Learning Systems
Earned Mar 23, 2024 EDT
Machine Learning in the Enterprise
Earned Mar 20, 2024 EDT
Feature Engineering
Earned Mar 9, 2024 EST
TensorFlow on Google Cloud
Earned Mar 5, 2024 EST
Launching into Machine Learning
Earned Feb 26, 2024 EST
Introduction to AI and Machine Learning on Google Cloud
Earned Feb 18, 2024 EST
Implement Load Balancing on Compute Engine
Earned Jul 6, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process - Bahasa Indonesia
Earned Jul 6, 2023 EDT
Developing a Google SRE Culture
Earned Jul 2, 2023 EDT
Google Cloud Fundamentals: Core Infrastructure - Bahasa Indonesia
Earned Jun 1, 2023 EDT
Perform Foundational Infrastructure Tasks in Google Cloud
Earned Mei 13, 2023 EDT
Google Cloud Essentials
Earned Feb 8, 2023 EST