Machine learning is the emerging future technology. Artificial intelligence entailed with machine learning increases the demand for machine learning engineers and data scientists. But handling machine learning is a tough job.

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning educates itself with its sense to observe. It doesn't need a human hand for help. Whether it is an action or a task, machine learning observes it closely and tries to imitate the function by putting the actions in its system. 

Machine learning is a popular technology that is being used in various sectors. It is in use at almost all the fields. Data scientists and machine learning engineers are used to functioning with the technology. But what about people who don't know machine learning? There is an emerging solution for them. Automated machine learning or AutoML comes for their aid.

What is AutoML?

AutoML or automated machine learning involves automating the end-to-end process of applying machine learning to real-world problems that are actually relevant in the industry. The aim is to reduce or eliminate the need for skilled data scientists to build machine learning and deep learning models. An AutoML system allows anyone to provide the labelled training data as input and receive an optimized model as output. 

In recent years, machine learning has been noticed as the key to the future. However, handling and programming machine learning involves various directions of research, analysis and implementation. By the technical process, machine learning is confined to data scientists and machine learning enthusiasts and researched. To break the chain, AutoML bridges the gap that provides a theory or concept of automated machine learning. 

A data scientist has to apply the appropriate data pre-processing, parameter engineering, parameter extraction and parameter selection methods that make the datasets ready to configure. And later it involves algorithms to get a final machine learning model. AutoML remedy was provided to challenge the lengthy methods. It can apply machine learning without such expertise or an ML expert.

Characteristics of AutoML

AutoML utilizes transfer learning to get users model trained without loading a large amount of data. Transfer learning is sometimes called custom machine learning. It allows models to obtain knowledge from other models as opposed to gaining it from raw data. The process will shorten the usual time it takes for the cycle. 

AutoML provides a platform for automated object detection and image tagging. The technology differentiates itself from the rest by empowering users to provide the platform with a sample set of images with specific tags called out. The system learns the images and the tags. Once the model is fully trained, an unseen sample will accurately detect and tag the items based on the learned model. 

Google's AutoML

Google launched a cloud AutoML in 2018. It was expected that the AutoML platform will take machine learning to a next level. Google AutoML is a service that allows users to train machine learning models without requiring in-depth knowledge and proficiency. 

Major features of Google's AutoML

• Google's AutoML will eliminate the financial barrier that small organizations face while attempting to involve machine learning in their workflow.
• The technology unravels machine learning features to laymen breaking the years-long routine of technological accessibility only to people with relevant academic background. 
• It will help business align human intellectual capital to innovate and differentiate. 

AI developers are the bridge between technology and the world

The unleashing of AutoML has stirred controversy among tech scientists. An AutoML can let a 3rd grader build deep learning in 20 minutes. It can even make potato chips recognizer in just 3 hours. The AutoMl solutions based on transfer learning lets the creator use little labelled data to make impressive results. The rapid growth in technology has brought a question among techies, 'Is AutoML good or bad for AI developers?'

The arguments side both aspects. Technology has not gone to the high-level it is today without the help of humans. If we have a look at the development, there are a lot of sectors where scientist did research and enriched the system to minimize human hand in it. Remarkable, it didn't lower the demand for tech scientists. Most of the functions were designed to lower the cost. This made both the tech experts and entry-level developers wanted in the market.

Looking back at the timeline of technology growth and developers part in it makes one thing clear. The need for data scientists who can tell the actual story behind data and not just data into mindless predictions will go up. The understanding of new models is often explained by data scientists and the demand is on hype. Henceforth, it doesn't matter how far the technology goes and how tall it grows out of human hands, the root for all these are scientists who have the responsibility to explain the functioning of a technology model. So it is pretty clear that data scientists, machine learning engineers and researchers will always be on stipulation.