Google Cloud Machine Learning (CML) is an effort to simplify and reduce time and cost of building a data science tech infrastructure for data science projects. It can plug into Google's other storage, querying, and data-handling products to generate machine learning models. Among the data sources is Google Cloud Dataproc - a managed Hadoop and Spark platform that is now in general availability.
Before the onset of Big Data, businesses had to wait on data warehouses and data analytics to organize and interpret data so as to provide batch intelligence to users. Today, organizations use Data Science and Big Data Analytics to analyze data in real time.
Why should you adopt Big Data Analytics in your organization?
Are you considering a career change? Maybe the current job does not pay well or is not fulfilling enough. In that case, which are the other jobs you could explore? There are many, but you really need something that best aligns with your interests.
For a newbie, changing jobs is an easy decision to take. But what if you have dependents to support? Then you need to take calculated risks that give results.
How do you do that?
What better way to start getting into TensorFlow than with a notebook technology like Jupyter, the successor to IPython Notebook? There are two little hurdles to achieve this:
I am often asked how to select the right database. The answer is it depends - no one size fits all situations. How do you choose?
Spark Summit (West) 2016 took place this past week in San Francisco, with the big news of course being Spark 2.0 which among other things ushers in yet another 10x performance improvement through whole-stage code generation.