TensorFLow : google’s machine learning api can be super powerful.
Just make json REST calls to the end-point and get results based on google’s machine learning lib.
1. identify an image (image classification)
2. parse speech into text
3. translate languages
4. use for predictive analytics to run wide and deep learning on datasets
Google Cloud Platform : It is Google’s AWS cloud.
Key takeaways: They only charge by the minute and not by the hour like amazon.
Extremely cheap pay as you use model on an extremely fast network; Access to Google’s pipes and servers.
A ton of of useful Cloud tools: SaaS, PaaS, BaaS etc..
Compute Engine : AWS EC2 boxes except more scaleable.
App Engine : AWS Beanstalk
Cloud Storage : AWS S3
data tools : the ones I’m interested in.
Google’s Big Table : Similar to amazon DynoDB, a nosql data store. The basic store engine of gmail,googlemaps and etc. Google’s original Hbase hapdoop data store.
Google BigQuery : Web interface to scan and query millions, billions, trillions of rows.
Google DataProc : Managed Hadoop, Spark Pig, Hive combined into one interface. Spin up a multiple cluster nodes in seconds and run a spark, pig, hive job using google’s compute power.
(better than amazon emr)
Summary: pay-as-you-go for cloud computing service. Watch out Amazon Web Services and Azure.
google data studio : neat visualization app for data.