WP_Cloud Analytics - Mapping Requirements to Technology

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W H I T E P A P E R
www.persistent.com
© 2017 Persistent Systems Ltd. All rights reserved.
References
10.1.2.9 Cloud Machine Learning
Google Cloud Machine Learning (ML) Platform provides modern machine learning services, with pre-trained
models and a service to generate the developer’s own tailored models. Developers can create their models
on TensorFlow, Google’s second generation machine learning system, released as open source software in
late 2015. Cloud ML has better training performance and increased accuracy compared to other large scale
deep learning systems. It can support thousands of users and TBs of data, and can be deployed to run on
multiple CPUs and GPUs. The services are fast, scalable and easy to use. Major Google applications use Cloud
ML, including Photos (image search), the Google app (voice search), Translate, and Inbox (Smart Reply). The
platform is now available as a cloud service to bring unmatched scale and speed to the customer’s business
applications. It is portable, fully managed, and integrated with other Google Cloud Data platform products such as
Google Cloud Storage, Google Cloud Dataflow, and Google Cloud Datalab so you can easily train your models.
References
10.1.2.10 Cloud Dataproc
Google Cloud Dataproc is a managed Spark and Hadoop services (Apache Hadoop, Apache Spark, Apache Pig,
and Apache Hive). It is fast, easy to use, and easily process big datasets at low cost. Users can control their costs
by quickly creating managed clusters of any size and turning them off when done. Cloud Dataproc integrates
across GCP products, giving customers a powerful and complete data processing platform. It automates cluster
management, resizes the clusters and integrates with Cloud Storage, BigQuery, Bigtable, Stackdriver Logging,
and Stackdriver Monitoring, giving developers a complete and robust data platform.
References
10.1.2.11 Compute Engine
Google Compute Engine is a virtual machine running in Google’s innovative data centers and worldwide fiber
network. Compute Engine’s tooling and workflow support enable scaling from single instances to global, load-
balanced cloud computing. Its VMs boot quickly, come with persistent disk storage, and deliver consistent
performance. Google’s virtual servers are available in many configurations including predefined sizes or the
option to create Custom Machine Types optimized for your specific needs. Flexible pricing and automatic
sustained use discounts make Compute Engine the leader in price/performance. Google bills in minute-level
increments (with a 10-minute minimum charge), so customers only pay for the compute time they use. You can
run OS like Debian, CentOS, CoreOS, SUSE, Ubuntu, Red Hat, FreeBSD, or Windows 2008 R2 and 2012 R2.]
References
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