Google cloud platform (note5)
Target : the enterprise cloud market
- Position- To be a developer-friendly platform
1) Not strong and no impression on cloud service and the enterprise segment
2) No contribution to the open source community before.
- Stength : ASIC, GPU and TPU hardware in its cloud
1) begin to work with open source projects (note3)
Other partners in the new foundation include AT&T, Box, Cisco, Cloud Foundry Foundation, CoreOS, Cycle Computing, Docker, eBay, Goldman Sachs, Huawei, IBM, Intel, Joyent, Kismatic, Mesosphere, Red Hat, Switch SUPERNAP, Twitter, Univa, VMware and Weaveworks.
Absent : Microsoft, Amazon, Pivotal, and Taiwanese tech companies)
–the popular open source container orchestration system
- TensorFlow for machine learning
-Spanner for launching massive distributed databases
-Draco for 3D graphics compression
2) To be a developer-friendly platform
1)letting customers run whatever open source stack they choose on Google’s infrastructure,
2)releasing and supporting open source projects and making the ecosystem
3)the partners who build tools and technologies on top of GCP, a first class citizen on the platform.
4) treating them as part of the whole and the net is bringing the tech you want and using Google technology or using any of the [partner] services
The KSF :
1)Being open to win the mind shares of developers
2) much more supportive of the open source community makes people feel better about Google and makes developers feel better about working with their tools because they can avoid lock-in
- Threat : peers: AWS (2006. 1st public cloud, market leader, 1st mover), Microsoft, IBM
UsingKubernetes, the popular open source container orchestration system offer robust open source tools, something that surprised some people in this market.
4 ways Google will enable enterprises to adopt machine learning and AI (note2)
1). Machine learning computing in Google Cloud
a deep learning algorithm can have tens of millions of parameters, training these machine learning models requires enormous computational resource
the Cloud Machine Learning Engine. This capability is designed for companies with data scientists and machine learning experts who are able to build their own unique machine learning models with libraries such as Tensorflow.
Google’s infrastructure as the solution to speed training times and improve the return on investment. Google has specialized ASIC, GPU and TPUhardware in its cloud to accelerate training and improve the ROI with on-demand cloud resource utilization. After the model is trained, it is deployed in range of platforms—from on-premise to mobile devices.
2. Algorithms and pretrained machine learning models
建ML model 需用 the machine learning engine, 用 Google’s pre-trained models (full list) using APIs to add machine learning capability to their applications, such as understanding natural language, images and natural language.
An API beta for understanding videos
demo: This 3-minute video of the demonstration of the Cloud Video Intelligence beta
3. Google acquires Kaggle for data
Google acquired Kaggle for data sets and talent. Kaggle, founded in 2010, is a community of 850,000 data scientists from around the world that hosts competitions to create the most accurate predictive models and market models, as well as to acquire new public data sets in a variety of fields.
the Advanced Solutions Lab for customers with ambitious goals to develop machine learning to solve complex problems.
(note4: The mission of Linux foundation :The mission of this new foundation is to “help facilitate collaboration among developers and operators on common technologies for deploying cloud native applications and services,” )
(Reference : Google Cloud Platform 入門)