Machine learning as a whole has developed a lot in the recent past, moving from a purely theoretic matter to a very sought after piece of technology which is dominating (almost) every company's development plan. Machine learning doesn't solely apply to data science and other forms of data-driven applications but, moves along a series of tools and software which are now using important architectures' raw computing power. Let's analyze how today's technology industry is using a combination of R-coded algorithms and Python in order to optimize and implement specific features.
Data Warehousing: Re-balancing Computing Brute Force for Better
In the recent past, data warehousing has been a consistent trend within the data science world, given the number of resources which have been invested in this very matter by enterprises like Apple, Amazon and many others. It is vital, at this pivotal stage, to state the fact that data warehousing has developed significant value within cloud-based architectures, especially on powerful computing ones like Amazon Redshift. A lot of Python developers have noticed how data warehousing IDEs were not using the entire processing power of the Redshift architecture and, therefore, they decided to combine variable reading features (the ones, to reference, which are used to print data in what's known as "third level printing") to simply coded R algorithms.
So What's The Purpose Of Machine Learning?
The purpose of machine learning, especially for what concerns these architectures, is related to the printing and storing side of the data which is being gathered by the R algorithm. For these data warehousing tools, you can imagine ML features as a translation, in Python, of what normally would have been done within a React application. This is important to state as it better clarifies the fact that Python is being tackled in this very industry as a rendering language, instead of an object manipulation one.
Data warehousing, when cloud-based, has been heavily contaminated by powerful computing languages which are made to properly use the computing power of such architecture to its fullest. From a business perspective, this could be a great chance for the Python developer who wants to embrace data science from a new perspective.
This is a companion discussion topic for the original entry at https://blog.datasciencedojo.com/p/35390c39-633c-463e-83e9-00a4f6b6f390/