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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.
7 Modules · 4 Labs · 45 Videos
Basic understanding of one or more of the following:
BigQuery, BigQuery ML, Dataflow, Pub/Sub, Apache Beam, Looker, Data Studio, Vertex AI, AutoML, Vertex Workbench, Document AI, Contact Center AI (CCAI), TPU (Tensor Processing Unit), Kubernetes Engine, Compute Engine
This section welcomes learners to the Big Data and Machine Learning Fundamentals course and provides an overview of the course structure and goals.
This section explores the key components of Google Cloud’s infrastructure. We introduce many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.
This section introduces Google Cloud’s solution to managing streaming data. It examines an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Data Studio.
This section introduces learners to BigQuery, Google’s fully managed, serverless data warehouse. It also explores BigQuery ML and the processes and key commands that are used to build custom machine learning models.
This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google’s unified platform for building and managing the lifecycle of ML projects.
This section focuses on the three key phases—data preparation, model training, and model preparation—of the machine learning workflow in Vertex AI. Learners can practice building a machine learning model with AutoML.
This section reviews the topics covered in the course and provides additional resources for further learning.
Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.
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