Companies’ moving operations to digital results in the generation of a plethora of data. Where there are big quantities of data,i.e., Big Data, there is an opportunity for model training for Artificial Intelligence. Why would companies spend money creating machine learning models? Because the adoption of data science has been proven to boost business growth.
Analyzing companies’ data is called Analytics. It is the classical spreadsheet-based approach, combined with a touch of statistics. But building a Machine Learning Model out of the data is “Augmented Analytics.” Augmented Analytics enriches decision-making in businesses. It is correlated with improved productivity and costs optimization in the long run.
The skills required for data science are highly specialized. Furthermore, the number of data scientists is limited. The scarcity of data science resources is a bottleneck in the adoption of Data Analytics. Consequently, the data science race is pushing aside the companies that cannot integrate data scientists. They lose out to competitors. Such businesses cannot capitalize on data and create machine learning models to analyze them. It is where AutoML comes in.
Automated Machine Learning is a branch of artificial intelligence. Its purpose is to automate the end-to-end machine learning process in Data Analytics for business. AutoML facilitates the deployment of machine learning models without any talent constraints.
The standard approach used by data scientists involves:
Each one of these steps is time-consuming, tedious.
Blending this method into the workflow of businesses can be complex and time-consuming.
Automated Machine Learning eliminates all these steps by automating the workflow and running numerous machine learning models simultaneously.
Another worthy advantage of applying AutoML is the democratization of data science in companies. Businesses customarily consider it challenging to address the need for better machine learning models. Because they have limited access to people from a data science background. AutoML reduces this chasm by empowering ‘citizen data scientists’ to execute the tasks without previous expertise.
AutoML lets employees, who are not data scientists, add their contribution to the data science effort within their company with little assistance (if none at all) from the data science teams. For example, TADA by MyDataModels allows business experts to produce customized machine learning models with limited abilities in the field. AutoML extends the accessibility of Machine Learning to a more general audience.
No-AutoML will not make data scientists vanish. It will alleviate the weight on their shoulders by taking over monotonous tasks that do not require much concentration. AutoML will automate some of their work. But they will take care of those that demand extremely technological talents. Businesses will want data scientists to determine problems, utilize field knowledge, and create sound and inventive models. AutoML can assist data scientists.
Predictive analytics includes a variety of techniques, including machine learning. It consists in analyzing current and past facts to make predictions about future or otherwise unknown events. Predictive Analytics is Data Analytics pushed one step further into the future.
Predictive Analytics based solely on Machine Learning is an exclusive area available only to a happy few.
On the contrary, Predictive Analytics based on AutoML allows for the automatic generation of Machine Learning Models without data scientist intervention. Hence, AutoML makes Predictive Analytics available to everyone.
Augmented Analytics is a field of data analytics. It employs machine learning and natural language processing. What for? To automate investigation methods usually performed by specialists or data scientists. It encompasses the use of Predictive Analytics.
Just as Data Analytics and Predictive Analytics require mastering Machine Learning (through autoML or with the assistance of dedicated data scientists), so does Augmented Analytics.
AutoML is available for use by anyone. No need for prior data science or Machine Learning knowledge. As a consequence, the variety of use cases is tremendous. Here is a couple of them :
The characteristics to remember about AutoML are: