Google Data Engineer 2018 - Complete Google Data Engineer and Cloud Architect Guide | Simpliv

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 is the target audience?
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
Basic knowledge
Basic understanding of technology - superficial exposure to Hadoop is enough.
What you will 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
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Phone no: 510-849-6155
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