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Can Data Science Survive the Rise of AutoML?
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swethakrishn017
Can Data Science Survive the Rise of AutoML?
Data scientists do not endanger the field of autoML. Because a data scientist brings a lot of things to the table that autoML cannot, at least not yet. When the subject of AutoML comes up, the most common and incorrect prediction is that "AutoML will replace data science." Here are some reasons why nothing could be more wrong:
5 Reasons Why Today's AutoML Will Never Kill Data Scientists:
Machine Learning Is Only One Part Of Data Science.
Data Science Is Much More Than Machine Learning: It is not incorrect to refer to data science as "the enormous technological puzzle." whereas AutoML is only a tiny piece of the puzzle. Data science is its own world. Aside from using machine learning models, a data scientist is responsible for a variety of other tasks. Analyzing confidential information, preparing the correct data to make the ML system work properly, extracting functional correlations, properly studying insights, and many other things are what the data scientist aspires to.
AutoML Can't Carry Out the Whole Data Science Process:
Only machine learning tasks can be performed by AutoML software. They are not yet qualified to complete the entire data science process. A data scientist's list of responsibilities is lengthy, and machine learning is only one of them.
AutoML Can Not Work Without Data Scientist:
It makes no difference how much the technological system is upgraded; it will always require supervision. Supervision can be semi-supervised or fully supervised. The data scientist's trained eye must validate the results generated by the AutoML software. The data scientist ensures that the results are correct and will make sense in the context of the task.
Data Scientists are the only ones who can create AutoM:
Data scientists cast a spell by conducting in-depth business context analysis, understanding, and data cleansing on large amounts of information and data. Only then does the AutoML software transform into a magic wand. AutoML will never achieve its goal unless a data scientist performs these tasks accurately. The Data Scientist deserves a standing ovation for contributing to AutoML's success!
AutoML Doesn't "Auto Select":
A data scientist is required to assist AutoML in determining where the complex data is located. There are numerous functions that AutoML cannot perform without the assistance of a data scientist. The list of problems that AutoML cannot solve includes auto integration, automatically aligning stakeholders, detecting business problems, and automatically solving them. These are not tasks that AutoML can perform on its own.
Data Scientists' Benefits from AutoML:
Data scientists used to code a lot, but now they don't have to! One of the most important advantages provided by AutoML is the automation of the coding process. Only if you are one Are you one of those data scientists tired of writing those long codes of immeasurable longitudes? Can you appreciate what a blessing AutoML is? If a data scientist prefers to manually carry out the entire coding process, he will have to create everything from scratch for his ML pipeline. Using the magical tool of AutoML, data scientists can create ML models flawlessly without focusing on code and worrying about it. Automated Machine Learning has benefited Data Science by allowing businesses to select only the algorithms that produce the best results. Data scientists save a significant amount of time with AutoML. According to a general survey, data scientists must devote 60% of their working time to organizing and cleaning data sets, with the remaining 19% spent on data collection. This valuable set of hours can be easily saved by handing control over to the pre-built AutoML system. Data scientists can apply the time saved to solve more complex problems.
Summary
"Will machines surpass humans?" is a popular Hollywood question. A thorough examination of the subject makes it abundantly clear that "AutoML will never spell the end of data scientists." Instead of worrying about machines taking their jobs, data scientists should appreciate this technological advancement. As a result, data scientists can achieve much larger goals, resulting in great success in data science. Combining AutoML and Data Science features will result in useful results for revolutionary change.
Thus, data science will be more intelligently designed because AutoML will do most of the work. However, a solid foundation of knowledge will still be built to use it effectively. Because the future of data scientists is entirely secure, the demand for data scientists will increase. To become a data scientist, you can enroll in a data science course in Pune, crafted for working professionals.
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