Do you know the machine learning global market is estimated to reach $30.6 billion by 2024? This marvellous growth is the outcome of Omni-presence of artificial intelligence and its trending subset; machine learning.
From face recognition to self-driving cars, machine learning has successfully evolved our lifestyle drastically. People could have hardly predicted this breakthrough of machine learning technology a few years ago.
Amidst the increasing popularity of this technology, the computer visionaries are constantly working on the programming tools to empower the machine learning development services. Until now, python has been the most popular programming language to create machine learning applications.
This has tremendously increased the usage of python. According to the Builtwith Stats, 146,344 live websites are using python. Apart from the popularity of machine learning, python’s easy to learn and write codes have also contributed to its present-day flourishing state.
Many techies assume that machine learning tools end on Python and R. But little do they know about the upcoming trend of machine learning development with SQL server. Yes, machine learning services is a feature of SQL server which has superb plans to enlighten the future of SQL developers just like that of Python developers.
In fact, without database language like SQL, learning python and R for machine learning is a complete waste! Like machine learning, SQL is all about data which every developer will have to use to retrieve and read the data.
Data science is incomplete without Structured Query Language (SQL). And this is surely not an overstatement! Read on to find the ways in which SQL developers can contribute to machine learning development services:
Manage Relational Database:
Machine learning is fuelled by data science which need the management, modification and structuring of data. This is done through structured tables which are compiled into relational database. SQL developers aim to manage this database to facilitate the development process.
Extract data from large chunks:
SQL can be used to find the relevant data from the large chunks of unorganized and unstructured data. It offers easy tools, commands and data types to make this task seamless for SQL developers. In fact, MySQL is one of the basic languages to communicate with data.
SQL is open source and cross-platform:
These are the benefits which are searched in every programming tool. Being open-source, SQL application is available for free from the official website. Composed of various libraries and APIs, SQL is supported by almost every operating system.
Conclusion:
Undoubtedly, researchers have developed a plethora of tools to support the machine learning development. We need to focus on the right tools to attain a quick and efficient development.
While python is shining bright in machine world, SQL developers need to realise their importance. If machine learning is all about data, you cannot leave the database languages behind!