For years now, companies have been hiding behind the moniker "machine learning" as a way to avoid the stigma associated (or associated in the past from the AI winters) with "artificial intelligence". Well, no more. This week Google announced Google Neural Machine Translation (GNMT) that gets close to human performance.
Cloud giants like Amazon, Google, Azure and IBM have rushed into the big data analytics cloud market. They claim their tools will make developer tasks simple. For machine learning, they say their cloud products will free data scientists and developers from implementation details so they can focus on business logic.
The free 2016 O'Reilly Data Science Salary Survey has been released. This year, they applied a handy linear regression and spelled out the entire model on page 42. A couple of things jumped out at me. In terms of $1,000s of dollars:
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: