Development [udemy.com] GCP: Complete Google Data Engineer and Cloud Architect Guide

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What you'll learn

• Deploy Managed Hadoop apps on the Google Cloud
• Build deep learning models on the cloud using TensorFlow
• Make informed decisions about Containers, VMs and AppEngine
• Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub

Requirements

• Basic understanding of technology - superficial exposure to Hadoop is enough

Desсription

This course is a really comprehensive guide to the Google Cloud Platform - it has ~25 hours of content and ~60 demos.

The Google Cloud Platform is not currently the most popular cloud offering out there - that's AWS of course - but it is possibly the best cloud offering for high-end machine learning applications. That's because TensorFlow, the super-popular deep learning technology is also from Google.

What's Included:

• Compute and Storage - AppEngine, Container Enginer (aka Kubernetes) and Compute Engine
• Big Data and Managed Hadoop - Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub
• TensorFlow on the Cloud - what neural networks and deep learning really are, how neurons work and how neural networks are trained.
• DevOps stuff - StackDriver logging, monitoring, cloud deployment manager
• Security - Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
• Networking - Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
• Hadoop Foundations: A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)

Who this course is for

• Yep! Anyone looking to use the Google Cloud Platform in their organizations
• Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP
• Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud
• Yep! Anyone looking to build TensorFlow models and deploy them on the cloud

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