The advancement of science and technology has made possible enumerable innovations, and automation is one such development that is growing pervasively. Machines and data-driven insights today are foundational to the decision-making of almost every industry. Machine Learning has emerged as one of the most powerful technologies applicable across practically every field. AutoML or Automated Machine Learning is the technique of automating the process of developing machine learning models, which is typically time-intensive and repetitive.

While tech enthusiasts and experts have a nuanced knowledge of what autoML is all about, it is still a concept of marvel for many. This blog aims to weigh up the concept of autoML and the process of its operations and functions. 

What is AutoML (Automated Machine Learning)?

AutoML or Automated Machine learning is the process of simplifying the repetitive process of developing complex machine learning models to augment the accessibility of machine learning without so much need for data science or machine learning expertise. The aim of automating machine learning is to democratize the power of ML to make it accessible for industries, organizations, and individuals without the constraints of limited expertise in developing and deploying complex ML models. 

Machine Learning in general is capable of performing an expansive range of problems, including natural language processing, image recognition, etc. These potentials however are limited only to those who essentially have the technical skills and specialized expertise like programming, statistics, and algorithms. This limits the bounds of ML, impeding its applicability and potential. AutoML promises a simpler and easy-to-use interface for training and deploying ML models, where non-experts can also build ML models. 

The mechanism behind AutoML: How does it work?

AutoML starts its process by training data at the first primary level. This training model is performed by using a large dataset consisting of a combination of attributes parallel to a target variable. The dataset is properly cleaned, preprocessed and made ready for training ML model. Algorithms take their role by exploring the data and curating a set of models that is most suitable for the relationship between the target and the attributes.  AutoML typically works by going through several steps:

Data Ingestion: This is the first AutoML step that involves reading data into an actionable format and analysing it to check its applicability for the following procedure in the AutoML process. Here, data exploration also takes place to guarantee the usability of data for ML by checking the availability of sufficient data and to ensure there aren’t too many missing values. 

Data Preparation: The next step is the preparation of data where raw data are converted into a clean format making it more suitable for model training. This process employs several techniques, such as: filling in missing values, deduplication, scaling and normalization. This step is crucial for AutoML as ML algorithms are particular about the data fed to them as inputs. Data preparation guarantees the quality of data is fit to be used for modelling and also enhances the accuracy of the model training result. 

Data Engineering: 

Then comes the third step-data engineering, where the extracting and processing of features are selected alongside data sampling and shuffling. This process can be carried out manually or automatically by employing ML techniques, like deep learning. Deep learning performs feature selection by automatically extracting features from the data. Through data sampling, a subset of the original data to be used in model training is selected. Data shuffling then rearranges and organizes the subsets of original data into separate sequences prior to training. 

Model Selection: 

Model selection is the fourth step in AutoML that entails the selection of a variety of models for building model and training. There are variances in model where some offer higher accuracy on a given dataset or for different purposes like time series prediction or binary classification. Knowing what information to extract and the suitability of the model type is critical, given model selection from many choices is a challenging task. AutoML tools automatically identify the most suitable and appropriate tools. 

Model Training 

Model training is the fifth step in the process of AutoML. AutoML models are of multiple variants, like deep neural network networks, neural networks, linear regression models, random forest models, decision trees, etc. each having their own set of hyperparameters. Typically, models are trained by feeding subsets of data and by selecting the most accurate one, it is further fine-tuned and deployed. The final trained model goes through a series of verification steps with held-out data. Today, understanding the performance of the model is easy with the visualization provided by advanced platforms that require no-coding. 

Hyperparameter Tuning: 

The efficiency of AutoML demands hyperparameter optimization, which entails AutoML to be able to adjust meta parameters, or hyperparameters. AutoML must have the ability to generate a series of predictions for multiple combinations of hyperparameters and choose the most appropriate based on the performance.  Common instances of hyperparameters are initial weights, maximum depth of tree, learning rate, momentum, etc. 

Model Deployment

After building and tuning a model, deployment of it is the next big challenging step, given the system’s large scale, which often requires intensive efforts of data engineering. In AutoML, this process is made easy through the use of in-built knowledge on the model deployment to various environments and systems. 

Model Updates

AutoML models comes with the capacity of continually updating models as and when new data is fed to them. Through this method, models are rendered updated with new information vital in today’s dynamic business ecosystem. 

AutoML today has become crucial for organizations and enterprises to automate their ML processes. There are instances of use cases and implementation of machine learning to enhance their performance. Today, companies mostly seek to acquire automated insights to make informed, data-driven predictions and decisions. Some of the most common use cases include:

• Marketing Management
• Sales Management
• Pricing
• AML Detection
• Fraud Detection
• Healthcare

With AutoML several processes of machine learning can be automated, including, Data pre-processing, Feature engineering, reducing error in the use of ML Algorithms, feature creation, transformation, feature extraction, algorithm selection.  

In today’s technologically evolved era, transforming big data into valuable insights has become indispensable. AutoML serves as a great mechanism to staying competitive and most importantly to tap into the full potential of ML.