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Julian Miranda

Date d'abonnement : 2020

Ligue d'Argent

6100 points
Create ML Models with BigQuery ML Earned mai 14, 2022 EDT
Prepare Data for ML APIs on Google Cloud Earned mai 11, 2022 EDT
Derive Insights from BigQuery Data Earned avr. 23, 2022 EDT
DEPRECATED BigQuery for Data Warehousing Earned avr. 9, 2022 EDT
Deploy Kubernetes Applications on Google Cloud Earned nov. 25, 2021 EST
Develop Your Google Cloud Network Earned nov. 25, 2021 EST
Build a Secure Google Cloud Network Earned nov. 25, 2021 EST
Set Up an App Dev Environment on Google Cloud Earned nov. 24, 2021 EST
Implementing Cloud Load Balancing for Compute Engine Earned nov. 24, 2021 EST
[Deprecated] Data Science on Google Cloud: Machine Learning Earned juin 20, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud Earned juin 18, 2020 EDT
Machine Learning APIs Earned juin 12, 2020 EDT
Intro to ML: Image Processing Earned juin 11, 2020 EDT
Intro to ML: Language Processing Earned juin 10, 2020 EDT
DEPRECATED BigQuery for Data Analysis Earned juin 9, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts Earned juin 6, 2020 EDT

Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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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 Managed Service for Apache Spark, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.

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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Data Studio by connecting to BigQuery data.

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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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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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Earn a skill badge by completing the Develop your Google Cloud Network skill badge course, where you learn multiple ways to deploy and monitor applications including how to: explore IAM roles and add/remove project access, create VPC networks, deploy and monitor Compute Engine VMs, write SQL queries, deploy and monitor VMs in Compute Engine, and deploy applications using Kubernetes with multiple deployment approaches.

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Earn a skill badge by completing the Build a Secure Google Cloud Network skill badge course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud.

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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud skill badge course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub.

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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.

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This is the second of two courses of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.

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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.

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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.

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It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this introductory course, you will get hands-on practice with machine learning as it applies to language processing by taking labs that will enable you to extract entities from text, and perform sentiment and syntactic analysis as well as use the Speech to Text API for transcription.

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Want to learn the core SQL and visualization skills of a Data Analyst? Interested in how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.

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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

En savoir plus