Engineer Data for Predictive Modeling with BigQuery ML
Earned avr. 18, 2024 EDT
Build a Data Warehouse with BigQuery
Earned mars 28, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned mars 27, 2024 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned mars 24, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned mars 12, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned fév. 14, 2024 EST
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 intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.
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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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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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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 avr. 18, 2024 EDT
Build a Data Warehouse with BigQuery
Earned mars 28, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned mars 27, 2024 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned mars 24, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned mars 12, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned fév. 14, 2024 EST