Sorumlu Yapay Zeka'ya Giriş
Earned Haz 8, 2023 EDT
Büyük Dil Modellerine Giriş
Earned Haz 8, 2023 EDT
Üretken Yapay Zekaya Giriş
Earned Haz 8, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Haz 1, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 31, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned May 27, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned May 26, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned May 25, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned May 24, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 23, 2023 EDT
Build Google Cloud Infrastructure for AWS Professionals
Earned May 21, 2023 EDT
Deploy and Monitor in Google Cloud for AWS Professionals
Earned May 21, 2023 EDT
Google Cloud Storage and Containers for AWS Professionals
Earned May 21, 2023 EDT
Google Cloud Compute and Scalability for AWS Professionals
Earned May 20, 2023 EDT
Google Cloud IAM and Networking for AWS Professionals
Earned May 19, 2023 EDT
This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge to demonstrate skills in the following: filtering, sorting, and pivoting data, merging results from different Looker Explores, and using functions and operators to build Looker dashboards and reports for data analysis and visualization. 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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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API. 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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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
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Earn a skill badge by completing the Cloud Functions: 3 Ways course, where you learn how to use Cloud Functions (including 2nd gen) through the Google Cloud console and on the command line. 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 digital badge that you can share with your network.
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This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Redshift, you also learn about similarities and differences between Redshift and BigQuery to help you get started with data warehouses in BigQuery. After this course, you can continue your BigQuery journey by completing the skill badge quest titled Build and Optimize Data Warehouses with BigQuery.
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Getting Started - Create and Manage Cloud Resources görevinden daha üst seviyede pratik yapmak isteyen, sınırlı deneyime sahip bir bulut geliştiricisiyseniz bu görev tam size göre. Cloud Storage ve Stackdriver ile Cloud Functions gibi diğer önemli uygulama hizmetlerini konu alan laboratuvarlar sayesinde pratik deneyim sahibi olacaksınız. Bu göreve katılarak herhangi bir Google Cloud girişiminde kullanabileceğiniz değerli beceriler edineceksiniz. Bu görevi, görev sonundaki yarışma laboratuvarı da dahil olmak üzere tamamladığınızda, size özel bir Google Cloud dijital rozetine hak kazanırsınız. Laboratuvarlarda, anahtar kavramlarla ilgili 1 dakikalık yol gösterici videolar bulunur.
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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness. 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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Complete the intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster. 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 the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
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This course teaches participants techniques for monitoring and improving infrastructure and application performance in Google Cloud.
Using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.
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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.
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This course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine courses and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to define and balance business and technical requirements to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective.
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Kurumsal yapay zeka ve makine öğreniminin kullanımı artmaya devam ettikçe, bunu sorumlu bir şekilde oluşturmanın önemi de artıyor. Sorumlu yapay zeka hakkında konuşmanın, onu uygulamaya koymaktan çok daha kolay olabilmesi burada bir zorluk oluşturmaktadır. Kuruluşunuzda sorumlu yapay zekayı nasıl işlevsel hale getireceğinizi öğrenmekle ilgileniyorsanız, bu kurs tam size göre.
Bu kurs, Google Cloud'un sorumlu yapay zeka yaklaşımını nasıl uyguladığını derinlemesine inceleyerek, kendi sorumlu yapay zeka stratejinizi oluşturmanız için size kapsamlı bir çerçeve sunuyor.
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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 explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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Bu kursta, üretken yapay zeka modelleri için prototip oluşturma ve özelleştirme amacıyla kullanılan bir araç olan Vertex AI Studio tanıtılmaktadır. İlgi çekici dersler, sürükleyici demolar ve uygulamalı bir laboratuvar ile üretken yapay zeka iş akışını keşfedecek; çoklu format destekli Gemini uygulamaları, istem tasarımı ve modeldeki ince ayarlar için Vertex AI Studio'yu nasıl kullanacağınızı öğreneceksiniz. Projelerinizdeki bu modellerin potansiyelini Vertex AI Studio kullanarak açığa çıkarabilecek bir seviyeye gelmeniz amaçlanmaktadır.
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Bu kurs, derin öğrenmeyi kullanarak görüntülere altyazı ekleme modeli oluşturmayı öğretmektedir. Kurs sırasında görüntülere altyazı ekleme modelinin farklı bileşenlerini (ör. kodlayıcı ve kod çözücü) ve modelinizi eğitip değerlendirmeyi öğreneceksiniz. Bu kursu tamamlayan öğrenciler, kendi görüntülere altyazı ekleme modellerini oluşturabilecek ve bu modelleri görüntülere altyazı oluşturmak için kullanabilecek.
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Bu kurs, dönüştürücü mimarisini ve dönüştürücülerden çift yönlü kodlayıcı temsilleri (BERT - Encoder Representations from Transformers) modelini tanıtmaktadır. Kursta, öz dikkat mekanizması gibi dönüştürücü mimarisinin ana bileşenlerini ve BERT modelini oluşturmak için dönüştürücünün nasıl kullanıldığını öğreneceksiniz. Ayrıca sınıflandırma, soru yanıtlama ve doğal dil çıkarımı gibi BERT'in kullanılabileceği çeşitli görevler hakkında da bilgi sahibi olacaksınız. Kursun tahmini süresi 45 dakikadır.
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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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Bu kursta, kodlayıcı-kod çözücü mimarisi özet olarak anlatılmaktadır. Bu mimari; makine çevirisi, metin özetleme ve soru yanıtlama gibi "sıradan sıraya" görevlerde yaygın olarak kullanılan, güçlü bir makine öğrenimi mimarisidir. Kursta, kodlayıcı-kod çözücü mimarisinin ana bileşenlerini ve bu modellerin nasıl eğitilip sunulacağını öğreneceksiniz. Laboratuvarın adım adım açıklamalı kılavuz bölümünde ise sıfırdan şiir üretmek için TensorFlow'da kodlayıcı-kod çözücü mimarisinin basit bir uygulamasını yazacaksınız.
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Bu kursta, görüntü üretme alanında gelecek vadeden bir makine öğrenimi modelleri ailesi olan "difüzyon modelleri" tanıtılmaktadır. Difüzyon modelleri fizikten, özellikle de termodinamikten ilham alır. Geçtiğimiz birkaç yıl içinde, gerek araştırma gerekse endüstri alanında difüzyon modelleri popülerlik kazandı. Google Cloud'daki son teknoloji görüntü üretme model ve araçlarının çoğu, difüzyon modelleri ile desteklenmektedir. Bu kursta, difüzyon modellerinin ardındaki teori tanıtılmakta ve bu modellerin Vertex AI'da nasıl eğitilip dağıtılacağı açıklanmaktadır.
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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. By passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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Bu kurs, sorumlu yapay zekanın ne olduğunu, neden önemli olduğunu ve Google'ın sorumlu yapay zekayı ürünlerinde nasıl uyguladığını açıklamayı amaçlayan giriş seviyesinde bir mikro öğrenme kursudur. Ayrıca Google'ın 7 yapay zeka ilkesini de tanıtır.
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Bu giriş seviyesi mikro öğrenme kursunda büyük dil modelleri (BDM) nedir, hangi kullanım durumlarında kullanılabileceği ve büyük dil modelleri performansını artırmak için nasıl istem ayarlaması yapabileceğiniz keşfedilecektir. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirmenize yardımcı olacak Google araçları hakkında bilgi verilecektir.
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Bu, üretken yapay zekanın ne olduğunu, nasıl kullanıldığını ve geleneksel makine öğrenme yöntemlerinden nasıl farklı olduğunu açıklamayı amaçlayan giriş seviyesi bir mikro öğrenme kursudur. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirmenize yardımcı olacak Google Araçlarını da kapsar.
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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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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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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Earn a skill badge by completing the Build Google Cloud Infrastructure for AWS Professionals course, where you learn how to configure IAM permission, orchestrate workloads using Kubernetes, host a web application using compute engine, and configure load balancing. 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 digital badge that you can share with your network.
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This is the fourth course of a four-course series for cloud architects and engineers with existing AWS knowledge. It compares Google Cloud and AWS solutions and guides professionals on their use.
This course focuses on deploying and monitoring applications in Google Cloud. The learners apply the knowledge of monitoring and application deployment processes in AWS to explore the differences with Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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This is the third course of a four-course series for cloud architects and engineers with existing AWS knowledge, and it compares Google Cloud and AWS solutions and guides professionals on their use.
This course focuses on Storage Options and containers in Google Cloud. The learners apply the knowledge of storage and containers in AWS to explore the similarities and differences with storage and containers in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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This is the second course of a four-course series for cloud architects and engineers with existing AWS knowledge. It aims to compare Google Cloud and AWS solutions and guide professionals on their use.
This course focuses on compute resources and load balancing in Google Cloud. The learner will apply the knowledge of using virtual machines and load balancers in AWS to explore the similarities and differences with configuring and managing compute resources and load balancers in Google Cloud. Learners will get hands-on practice building and managing Google Cloud resources.
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This is the first course of a four-course series for cloud architects and engineers with existing AWS knowledge, and it compares Google Cloud and AWS solutions and guides professionals on their use.
This course focuses on Identity and Access Management (IAM) and networking in Google Cloud. The learners apply the knowledge of access management and networking in AWS to explore the similarities and differences with access management and networking in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
Developing Data Models with LookML
Earned Haz 26, 2024 EDT
Understanding LookML in Looker
Earned Haz 23, 2024 EDT
Prepare Data for Looker Dashboards and Reports
Earned Haz 23, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned Kas 25, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned Kas 19, 2023 EST
Cloud Functions: 3 Ways
Earned Kas 19, 2023 EST
BigQuery Fundamentals for Redshift Professionals
Earned Eki 14, 2023 EDT
Perform Foundational Infrastructure Tasks in Google Cloud
Earned Eki 8, 2023 EDT
Optimize Costs for Google Kubernetes Engine
Earned Eki 7, 2023 EDT
Implement Cloud Security Fundamentals on Google Cloud
Earned Eyl 23, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Eyl 17, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Eyl 16, 2023 EDT
Generative AI Explorer - Vertex AI
Earned Eyl 2, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process
Earned Ağu 26, 2023 EDT
Sorumlu Yapay Zeka: Google Cloud ile Yapay Zeka İlkelerinin Uygulanması
Earned Ağu 26, 2023 EDT
Launching into Machine Learning
Earned Haz 27, 2023 EDT
How Google Does Machine Learning
Earned Haz 14, 2023 EDT
Vertex AI Studio'ya Giriş
Earned Haz 9, 2023 EDT
Görüntülere Altyazı Ekleme Modelleri Oluşturma
Earned Haz 9, 2023 EDT
Dönüştürücü Modelleri ve BERT Modeli
Earned Haz 9, 2023 EDT
Dikkat Mekanizması
Earned Haz 9, 2023 EDT
Kodlayıcı-Kod Çözücü Mimarisi
Earned Haz 8, 2023 EDT
Görüntü Üretmeye Giriş
Earned Haz 8, 2023 EDT
Generative AI Fundamentals
Earned Haz 8, 2023 EDT
Sorumlu Yapay Zeka'ya Giriş
Earned Haz 8, 2023 EDT
Büyük Dil Modellerine Giriş
Earned Haz 8, 2023 EDT
Üretken Yapay Zekaya Giriş
Earned Haz 8, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Haz 1, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 31, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned May 27, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned May 26, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned May 25, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned May 24, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 23, 2023 EDT
Build Google Cloud Infrastructure for AWS Professionals
Earned May 21, 2023 EDT
Deploy and Monitor in Google Cloud for AWS Professionals
Earned May 21, 2023 EDT
Google Cloud Storage and Containers for AWS Professionals
Earned May 21, 2023 EDT
Google Cloud Compute and Scalability for AWS Professionals
Earned May 20, 2023 EDT
Google Cloud IAM and Networking for AWS Professionals
Earned May 19, 2023 EDT