Introduction to Large Language Models
Earned août 6, 2025 EDT
Introduction to Generative AI
Earned mars 28, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned mars 3, 2023 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned fév. 26, 2023 EST
Implementing Cloud Load Balancing for Compute Engine
Earned fév. 26, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned fév. 5, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned jan. 27, 2023 EST
This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.
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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
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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 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 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.
Introduction to Large Language Models
Earned août 6, 2025 EDT
Introduction to Generative AI
Earned mars 28, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned mars 3, 2023 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned fév. 26, 2023 EST
Implementing Cloud Load Balancing for Compute Engine
Earned fév. 26, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned fév. 5, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned jan. 27, 2023 EST