Data science projects typically involve spending a significant amount of time experimenting with various algorithms, processing data, and comparing results to determine optimal models for deployment. With the increasing number of data science, machine learning, and AI projects under development, the desirability of model development and deployment automation has become increasingly attractive. AutoML automates the preprocessing stage and picks the best model for predictive analysis.
Recent advances in AutoML have created an environment where developers can build superior machine learning and AI applications and streamline data science cycles with AI-powered feature engineering. With such sophisticated automation, should data scientists be worried about the impact of automated processes on their livelihood? Keep reading.
What is AutoML?
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning models to real-world problems. AutoML includes every stage of model development and deployment, from beginning with a raw dataset to building a machine learning model and deploying the model within a pipeline. It selects, constructs, and parameterizes machine learning models by choosing hyperparameters that produce the best accuracy within a data set.
Traditionally a data science process involves a lengthy and challenging manual process of feature engineering. The process consists of creating a flat “table" that includes various features to be evaluated against various machine learning algorithms. The goal of feature engineering is to create new features suitable for use in a given system. Feature engineering enables you to build more complex models than you could with only raw data. It also helps increase the accuracy of the model and thereby enhances the results of the predictions. The process is iterative and requires advanced knowledge and expertise.
First-generation AutoML platforms targeted the automation of basic data science processes such as data acquisition, exploration, and modeling. Recently emerging platforms are capable of automating the time-consuming task of feature engineering and are known as AutoML 2.0 systems. These platforms enable data scientists to focus on more meaningful and exciting analytics tasks, transforming their work into more creative and business-focused solutions.
Could AutoML be the end of data scientists?
Given the strides in recent years to create and deploy powerful AutoML tools, one might ponder the need for data scientists in the near future. However, this would be an improbable outcome for numerous reasons.
- AutoML is not intended to replace the responsibilities of the data scientist fully. It is designed to improve the efficiencies of the process and speed up implementation and rollout.
- AutoML is not a direct substitute for human intelligence. A data scientist must review the results for the correctness and ensure they make sense in the context of the business environment for which they were produced.
- AutoML can perform an excellent job with many datasets, but solutions are optimized for accuracy and precision by data scientists.
Now, let’s examine a few benefits that AutoML can bring to data scientists, developers, and the business
The next generation AutoML platforms, AutoML 2.0, are designed to automate the development and deployment of machine learning models. They enable developers to create AI-powered models, accelerate the development of enterprise AI initiatives, and expedite the data scientists’ work. They reduce the time it takes to build and deploy models from months to days.
AutoML helps empower non-experts, data scientists, and developers alike by eliminating the need for manual and exhaustive data preparation and model selection processes, and custom development. It allows data scientists and analysts to save significant time by automating selective tasks and tuning the hyperparameters of their models. Businesses benefit significantly through increased efficiency and rapid model deployment, particularly small-to-medium-sized companies that cannot afford to support large teams of experts to build and deploy sophisticated prediction, classification, and recommendation systems.
AutoML will not replace the data scientist profession, but rather the two will work in concert to create the highest value for an organization, expediting model creation and delivery, ultimately increasing business ROI. The demand for a data scientist to perform complex data analysis and interpretability tasks will continue for the foreseeable future. In fact, according to experts, the market for AutoML practitioners will significantly grow over the next few years, as suggested in this Mckinsey analysis.
If you are interested in automating data science processes with AutoML, email us at firstname.lastname@example.org. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.