GCP: Complete Google Data Engineer and Cloud Architect Guide

Even though Google Cloud Platform is not the most famous cloud service in the present day, it is for sure the best platform that offers support for high-end machine learning applications.

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Google

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Intermediate

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23 Hours

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Even though Google Cloud Platform is not the most famous cloud service in the present day, it is for sure the best platform that offers support for high-end machine learning applications.

Course Information

About the Course:

Even though Google Cloud Platform is not the most famous cloud service in the present day, it is for sure the best platform that offers support for high-end machine learning applications. All this is due to TensorFlow, which is a deep learning technology by Google.

A Cloud and Data Engineer Professional earn $88,000 on average annually.

Course Objective:

At the end of this course, students shall will familiar with:

  • Computation and Storage – AppEngine, Compute Engine, and Kubernetes
  • Big Data and Managed Hadoop - Dataproc, Dataflow, BigTable, BigQuery, and Pub/Sub 
  • TensorFlow – complete information about the neural networks, including what they are, how they work, what are the components, how should we train them, and how is it connected to deep learning 
  • DevOps – Cloud Deployment, Cloud Monitoring, and StackDriver logging
  • Cloud Security - Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, and service accounts
  • Networking – Shared and Traditional Virtual Private Clouds, the HTTP Layer, Cloud, and CDN Interconnect, Network Load Balancing, and transport layer
  • Hadoop Foundations – Open source platforms including Hadoop, Pig, Hive, HBase, and Spark

Audience:

This course targets the following audience:

  • Startup owners or IT companies who want to use the Google Cloud Platform
  • Individuals interested in learning about cloud architecture, networking, load balancing, and other features of Google Cloud Platform
  • Candidates looking for big data solutions and serverless analytics with the Google Cloud Platform
  • People who want to build and deploy TensorFlow

Prerequisite:

Candidates must have a basic understanding of IT and some experience with Hadoop.

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BrandGoogle
SubjectsApp Development
Lab AccessNo
TechnologyGoogle
Learning StyleSelf-Paced Learning
Learning TypeCourse
DifficultyIntermediate
Course Duration23 Hours
LanguageEnglish
VPA DiscountVPA Discount

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Course Expert:

Author

Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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