The digital transformation is driven primarily by the data. As a result, various companies are looking for opportunities to gain maximum value from their data. Machine learning is a crucial field of data science. Algorithms are trained using statistical methods to imitate the way humans learn and make a decision. The utility of Machine Learning continues to flourish in companies of all sizes. The most common examples are – fraud prevention, automatically targeting the consumer segments in marketing agencies, customer service chatbots at banks, retailer personalization and suggestions for e-commerce goods, etc. Undoubtedly, machine learning is a trending topic, but there’s another trend that’s gaining speed – Automated Machine Learning.  

Automated Machine Learning or AutoML is a new approach. It is a process in which raw data and models are matched together to reveal the most relevant information. In present times, there has been a sudden increase in the AutoML trends across different industries. 

What is Automated Machine Learning?

Due to the constant development in the AutoML field, there’s no such universal definition that everyone follows. In simple words, Automated Machine Learning is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML provides machine learning tools to automate repetitive tasks by applying ML to ML. In addition, AutoML aims to create techniques for computers to automatically solve the new ML issues so that human ML experts won’t be needed to feed the data. 

Machine Learning technologies require data scientists, professional researchers, and engineers. Still, the supply of such professionals falls short of meeting the demand. These positions are so poorly filled that this led to the emergence of AutoML to automate many of the tasks data scientists used to perform. In simple words, AutoML has the potential to counter the scarcity of AI and ML experts. Moreover, with automated machine learning, data scientists can also build more ML models in less time with improved quality. 

Growth Of AutoML

The AutoML market size is growing rapidly as the technology is getting more popular. According to the report published by Research and Markets in 2020, the AutoML market has generated a revenue of $0.3 billion in 2019, which is estimated to go up to $14.5 billion by 2030. The report also stated the drivers of the growth, which are :

• Increasing demand for more efficient fraud detection solutions
• The rising importance of predictive lead scoring
• The growing need for personalized product recommendation

Moreover, the interest in AutoML is also increasing, which is expected to continue for a few more years. 

Funding

The total funding amount is also a good indicator of success because the investors always invest their money in successful companies. According to CrunchBase, the best-funded AutoML solution among all the AutoML products and services is DataRobot, with a total funding amount of $431 million. After the second, H20.ai comes second with funding of $ 151.1 million, and Dataiku comes third with $ 147 million. 

Machine Learning processes that can be automated

• • Data Pre-Processing – Data Pre-Processing includes enhancing the data quality and converting raw and unstructured data to a set format through methods like Data Cleaning, Data transformation, Data integration, and data reduction. 

• Feature Extraction – This AutoML process includes combining many features or datasets to generate new features. These new features will create more accurate results while reducing the size of the data being processed. 

• Feature engineering – Automated machine learning can automate the method by analyzing the input data to create features that can be more compatible with machine learning algorithms. 

• Feature selection – With AutoML, the task of selecting only useful features for processing becomes automated. 

Why is AutoML important?

Short supply of data scientists

Data science has become an indispensable part of our lives. As a result, businesses need more solutions in the data science field and need more data scientists to build these solutions. If not for AutoML, it would be very difficult for companies to understand simple processes, study performance levels, and take immediate actions to prevent huge losses. 

Demand for Data Scientists will be increased in the future. However, in reality, it takes 43 to 51 days to fill a data scientist position. The scarcity of data scientists has led to the slow development of a solution for data science. AutoML process can help businesses with different solutions to satisfy the demand for data scientists. 

Error in applying ML algorithms

The responsibility of implementing machine learning algorithms and choosing the best method for business lies with the data scientists. However, sometimes the implementation process doesn’t work accurately due to human-made errors. These errors can be completely eliminated by AutoML as it can run even more machine learning algorithms which might be neglected by the data scientists. 

Big companies like Facebook have also invested in AutoML. Presently, Facebook trains approximately 300,000 ML Models to improve its machine learning processes and has even created its own AutoML engineer named Asimo. 

Benefits of Automated Machine Learning

• It saves time 

Data scientists are not born with the ability to predict the best alternative and hyperparameters for solving a problem. They manually test models, tune hyperparameters, and evaluate models to arrive at the best model for a particular problem. With AutoML, the process can be done automatically in lesser time by transferring the data into the training algorithm to automatically search for the best suitable neural network architecture for any concerned problem. This can save a lot of time. 

• Skill gaps are bridged

Today, every business is well aware of the latest AI and Digital Trends to compete on a large scale. But companies usually struggle to find the right talent. There is an increasing demand for ML engineers or data scientists that businesses are unable to find. In that case, AutoML can work to bridge the shortage of skilled people in an organization. 

• Increase in productivity

It is clear by now that Automated Machine Learning can simplify the process of applying ML to real-world problems. It focuses on running all the steps to solve any particular business problem while reducing the complexity of testing, developing, and deploying machine learning frameworks. This results in increased productivity. 

• Enhanced Scalability

Certain ML models can mimic a few human learning processes, and AutoML helps to apply this at scale. 

1. Reduced errors in application of ML algorithms

As any business starts growing, the amount of data also expands. AutoML eliminates the possibility of inaccuracies in the algorithms due to human neglect or error. As a result, a business can introduce new innovation, generate new business benefits, and gain higher ROI on ML projects with accurate algorithms and processes. 

All the mentioned benefits lead to the next topic, which is where AutoML should be applied? 

AutoML use Cases

The most common AutoML use cases are as follows :

• Time Series Forecasting – Data scientists and machine learning engineers use time-series forecasting to predict the events happening in the future. This is done by analyzing the data and a series of values observed through time. But time-series forecasting is a complicated and time-consuming process. 

AutoML automates the entire process, including hyperparameter tuning, feature engineering for discovering predictive signals, model selection, and more.

• Classification Problem – A classification problem is learning that assigns a label or class to a sample. Common classification examples include handwriting recognition, object detection, and fraud detection. AutoML can help deploy an advanced classification ML model automatically to derive insights. 
• Feature Selection – Predictors, also known as features, are essential to an ML model. Predictors often depend on ML algorithm choice and, if not accurately selected, could affect model building time and scoring. AutoML makes the feature selection process easy by using an automated evaluating process to access the combination of stable and strong features. 
• Algorithm Selection – The most daunting task in an ML task is finding an optimal algorithm. However, data scientists can infer the right algorithm by referring to a data set. AutoML uses an automation process to identify the most suitable models and algorithms for a problem. 
• Model Evaluation – It is a technique used to validate an ML model’s performance. In simple words, model evaluation is the process of determining whether the model is overfitting or underfitting. AutoML automatically evaluates an ML model’s efficiency among the given set of evaluation metrics.

Conclusion

The main function of AutoML is to automate repetitive tasks like hyperparameter tuning so data scientists can handle other business problems at hand. In addition, AutoML aims to make the technology available to everyone rather than a few. 

The success of AutoML depends on its adoption and the advancements made in the concerned sector. However, it wouldn’t be wrong to say that AutoML is a big part of future machine learning.