This course is a really comprehensive guide to the Google Cloud Platform - it has ~20 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 of TensorFlow, the super-popular deep learning technology is also from Google.
- Certification stuff - Covers pretty much all of the material you ought to need to get past the Google Data Engineer and Cloud Architect certification tests
- Compute and Storage - AppEngine, Container Engine (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)
What are the requirements?
- Basic understanding of technology - superficial exposure to Hadoop is enough
What am I going to get from this course?
- 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
What is the target audience?
- Yep! Anyone looking to use the Google Cloud Platform in their organizations
- Yep! Anyone looking to clear the Google Data Engineer or Cloud Architect certification tests
- Yep! Anyone looking to build TensorFlow models and deploy them on the cloud