Engineer Data for Predictive Modeling with BigQuery ML
Earned juil. 4, 2022 EDT
Prepare Data for ML APIs on Google Cloud
Earned juin 30, 2022 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned juin 27, 2022 EDT
Google Cloud Essentials
Earned juin 27, 2022 EDT
Build Streaming Data Pipelines on Google Cloud
Earned juin 16, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned juin 9, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned mai 20, 2022 EDT
Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.
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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.
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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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In this introductory-level course, you get hands-on practice with the Google Cloud’s fundamental tools and services. Optional videos are provided to provide more context and review for the concepts covered in the labs. Google Cloud Essentials is a recommendeded first course for the Google Cloud learner - you can come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.
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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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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.
Engineer Data for Predictive Modeling with BigQuery ML
Earned juil. 4, 2022 EDT
Prepare Data for ML APIs on Google Cloud
Earned juin 30, 2022 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned juin 27, 2022 EDT
Google Cloud Essentials
Earned juin 27, 2022 EDT
Build Streaming Data Pipelines on Google Cloud
Earned juin 16, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned juin 9, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned mai 20, 2022 EDT