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Description
Udemy – 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
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Build deep learning models on the cloud using TensorFlow
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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
Description
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! 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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