Udemy – gcp-data-engineer-and-cloud-architect
English | Size: 2.45 GB
The Google Cloud for ML with TensorFlow, Big Data with Managed Hadoop
Created by Loony Corn
Last updated 12/2017
What Will I 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
Basic understanding of technology – superficial exposure to Hadoop is enough
• 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.
• 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)
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