GOOGLE CLOUD
Google Cloud Big Data and Machine Learning Fundamentals
Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
Design streaming pipelines with Dataflow and Pub/Sub.
Analyze big data at scale with BigQuery.
Identify different options to build machine learning solutions on Google Cloud.
Describe a machine learning workflow and the key steps with Vertex AI.
Build a machine learning pipeline using AutoML.
Instructor LIVE training
An instructor will answer your questions
OFFICIAL
Google Cloud Content
Course reflects the latest authorised Google Cloud class contact
Hands on LABS
Real world hands on experience throughout the class. Supported by instructor.
Beginner
Delivered in English
Familiarity with application development, systems operations, Linux operating systems is helpful in understanding the technologies covered.
Data analysts, data scientists, and business analysts who are getting started with Google Cloud • Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results, and creating reports
Executives and IT decision makers evaluating Google Cloud for use by data scientists
🔸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 ection 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.
Ref: T-GCPBDML-B-03
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