If you are a Machine Learning Practitioner, you must be having this question these days that will AutoML replace Data Science Jobs or not? Now, this is a good question if you are dedicated to your practice and you will feel demotivated if a concept like AutoML will become an enemy for your future. In this article, I will answer this question for you that will AutoMl Replace Data Science Jobs?
What is AutoML?
As the name suggests, AutoML is an area of ​​machine learning concerned with automating the repetitive tasks of the machine learning process. AutoML aims to automate the number of steps to be taken in the workflow of a machine learning task without compromising in the performance of the trained model.
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Through intelligent automation, AutoML fulfils an important mission: to enable more people to enjoy the benefits of using machine learning to solve real-world problems by democratizing machine learning and making it accessible to non-users. experts, while increasing the productivity of experts.
Will AutoML Replace Data Science Jobs?
A common question that arises whenever the topic of AutoML is brought up is: Will AutoML replace data science jobs? My answer is no. I’ll use the example of a food processor as an analogy to clarify the arguments.
AutoML, just like food processors, helps humans be more productive and efficient by delegating to machines the part of their work that is repetitive and resource-intensive, which is exactly the type of work where machines tend to outperform. humans.
For example, constantly stirring at a certain pace, at a constant temperature, and for a specific amount of time (e.g. 20 minutes non-stop), is something food processors do better than humans, mainly due to the lesser variability involved. The lower variability while stirring will tend to generate more promising results with fewer errors.
However, creating the recipe, choosing the right ingredients, and putting those ingredients in the food processor are tasks humans are currently better at, as they involve creativity, judgment, and manual dexterity. By using a food processor to cook, a human will likely spend less time in the kitchen, allowing them to concentrate on other tasks.
The same way if we automate the process of training multiple machine learning algorithms for choosing the best machine learning model, then a Data Scientist can focus much on the most rewarding elements of the human roles in the task of automation, such as those involving creativity and critical thinking.
The Reasons Why AutoMl Will Never Replace Data Science Jobs
If I give you a few more reasons why AutoMl will not replace data science jobs based on the analytics world, many activities lie at the heart of data science where the influence, human intervention and oversight are vital.Â
Here is a list of some of the most crucial tasks but often overlooked tasks that a data scientist needs to perform in their role and which is very less prone to the world of automation:
- Identify real-world issues that can be analyzed through the lens of data science.
- Frame the problem as a data science problem (for example, should the problem be treated as a supervised, unsupervised, or reinforcement learning task? Or are traditional statistics sufficient?).
- Anticipate risks and design strategies to manage them.
- Design data collection methodology, perform data annotation, assess data quality if no labelled data is available.
- Identify and control human biases, especially if relying on external data.
- Integrate domain knowledge into the process, for example, through feature engineering.
- Critically analyze and evaluate the results of a model.
- Explain model decisions in a human interpretable way.
- Analyze ethical issues and assess the impact of the project result on society.
- Communicate results effectively to stakeholders.
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So these are the reasons why automation will not replace data science jobs. I hope you liked this article on Will AutoML replace Data Science Jobs or not. I hope you got the answer. Feel free to ask your valuable questions in the comments section below.
