Will AutoML Soon Throw Data Scientists Out of their Jobs?

AutoML cannot replace a data scientist's job; instead, it may help speed up a data scientist's work.

AutoML (Automated Machine Learning) automates certain key components of the machine learning pipeline. Data interpretation, data engineering, extraction of features, model training, hyperparameter tweaking, model monitoring, and so on are all part of this machine learning process.

What is AutoML, and will it affect a Data Scientist Job?

Some aspects of the machine learning pipeline are automated using AutoML. TPOT, AutoKeras, AutoViML, and more AutoML utilities are available. Some aspects of the machine learning process are time-consuming and repetitive. An AutoML library can be used to automate these tasks. In the future, AutoML will not be able to replace a data scientist; rather, it will aid a data scientist in optimising their job.

There are four components to an end-to-end machine learning project:

• Data Collection
• Data Preparation
• Modeling
• Deployment

The following are some of the most often used AutoML tools:

• TPOT
• Auto-Sklearn
• AutoViML
• AutoKeras and many more.

Advantages of AutoML

1. Accessibility: Professionals and researchers from other fields who are unfamiliar with machine learning may utilise AutoML for their projects without having to worry about the time-consuming and redundant data preparation and other processing phases, such as model selection.

2. Efficiency: For typical data scientists and analysts, AutoML may save a lot of time in redundant processes that might have been spent modifying hyperparameters to make the models more optimal. As a result, the task becomes more efficient.

3. Fewer Errors: Codes are prone to mistakes. AutoML aids in the reduction of human mistakes in routine tasks. You wouldn't have to be concerned about making mistakes in the early phases that might jeopardise your future forecasts. AutoML is similar to using a calculator for multiplication in that, instead of performing operations and steps manually, we can just use a calculator for multiplication to achieve the same result.

4. Cost savings: This will come in handy for small businesses and startups who can't afford to engage a machine learning expert to construct their recommendation or sales forecasting systems. However, you'll still need staff to simulate massive projects.

5. Meet Industry Demands: AutoML will make the process of learning ML, as well as many other experts from other disciplines, easier, attracting individuals to transition to Machine Learning and Analyst professions, which will help to meet the sector's ever-increasing need for human resources.

If we move farther into the future, companies will eventually become data-driven or model-driven. They will then face a new challenge: providing good responses in a short amount of time. There will be no competitive advantage because the competitors will have access to AutoML tools as well. Assuming that numerous organisations have access to the same data (which is unusual but would provide a huge competitive advantage), the data scientist's duty may be to increase the performance of AutoML systems to gain a competitive edge. This will very certainly only make sense for applications greater than a certain scale, i.e., those whose returns justify the investment.

Does AutoML eliminate data science jobs?

"NO" is the correct response to the question. The reasons behind this are as follows:

1. While AutoMLs are capable of selecting models in the majority of cases, they are still unable of doing the majority of a Data Scientist's labour. We still require data scientists/analysts to apply their domain expertise to develop more meaningful features and information that affect the intended outcome (Feature Engineering).

2. AutoML will not replace most data science professions; rather, it will assist experts in completing their assignments more quickly.

3. Machines aren't smart enough, and algorithms frequently fail to generalise and comprehend the context of an issue.

4. While AutoML can assist us in finding a good model for a specific problem, it cannot create a novel approach, which is frequently necessary for emergent real-world challenges.

AutoML will continue to grow in popularity. Certain duties will almost certainly be mechanised, and certain responsibilities will almost certainly be eliminated. AutoML, on the other hand, will serve as a supplement to data science projects for decades to come, allowing them to be more efficient.

To acquire the best performing model, AutoML helps to automate modelling training and hyperparameter adjustment. It uses the data to train several machine learning methods such as SVM, Logistic Regression, Ensemble models, and so on, and then delivers the best model. Hyperparameters are unique to each model. AutoML fine-tunes each model's parameters and returns the best model with the best collection of hyperparameters.

Advanced deep learning is also trained with AutoML tools using the optimal collection of hyperparameters, such as the number of layers, neurons, and so on. Because a deep learning model has many parameters, the AutoML framework may speed up a data scientist's work and deliver the optimal deep learning model in a shorter amount of time.