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GCPBD - Google Cloud Platform Big Data and Machine Learning Fundamentals
This certification & training course will introduce you to Google Cloud's big data and machine learning functions. You'll begin with a quick overview of Google Cloud and then dive deeper into its data processing capabilities.
What You Will Learn
Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
Employ BigQuery and Cloud SQL to carry out interactive data analysis.
Choose between different data processing products in Google Cloud.
Create ML models with BigQuery ML, ML APIs, and AutoML.
Module 1: Introducing Google Cloud Platform
Google Platform Fundamentals Overview.
Google Cloud Platform Big Data Products.
Lab: Sign up for Google Cloud Platform.
Module 2: Compute and Storage Fundamentals
CPUs on demand (Compute Engine).
A global file system (Cloud Storage).
Lab: Set up an Ingest-Transform-Publish data processing pipeline.
Module 3: Data Analytics on the Cloud
Stepping stones to the cloud.
Cloud SQL: your SQL database on the cloud.
Lab: Importing data into CloudSQL and running queries.
Spark on Dataproc.
Lab: Machine Learning Recommendations with Spark on Dataproc.
Module 4: Scaling Data Analysis
Fast random access.
Lab: Build a Machine Learning Dataset.
Module 5: Machine Learning
Machine Learning with TensorFlow.
Lab: Carry out ML with TensorFlow.
Pre-built models for common needs.
Lab: Employ ML APIs.
Module 6: Data Processing Architectures
Message-oriented architectures with Pub/Sub.
Creating pipelines with Dataflow.
Reference architecture for real-time and batch data processing.
Module 7: Summary
Where to go from here.
This Course Is For
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.
Roughly one year of experience with one or more of the following:
A common query language such as SQL.
Extract, transform, and load activities.
Machine learning and/or statistics.
Programming in Python.
Associated with Machine Learning Engineer & Professional Data Engineer Certification.
11 Jul 2022 - 11 Jul 2022
12 Sep 2022 - 12 Sep 2022
21 Nov 2022 - 21 Nov 2022
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