In recent years, Machine Learning has propelled in many aspects, counting model structuring and various methods of leaning. It is plausible today to explore entire machine learning algorithms via fundamental operations as fabricating blocks.
Also, the essence of neural networks have touched the remarkable performance on various tasks and observed a rapid growth in their popularity, it is only achievable because of diverse machine learning research into a vast realm ranging from learning strategies to new neural architectures.
However, the length and difficulty of ML research have prompted a new domain, Automated Machine Learning(AutoML), that automate the ML process by employing machine compute time in place of human research time. It has made significant research in recent times as AutoML has hugely converged on neural network architectures.
On the same remarkable deeds of ML research, let’s drive into the blog, and understand the conceptual information regarding automated machine learning aka AutoML, its benefits, advantages, limitations and where to deploy it in the context of classification, regression, and time-series forecasting.
Put simply, the spacious and powerful technology is restricted to the number of data scientists, machine learning experts, and researchers, that are increasing slowly, therefore to overpass this gap, the concept of Automated machine learning comes into the picture.
What is AutoML?
AutoML concerns an automated end-to-end process of implementing machine learning to real-world issues that are pertinent to the industry, in actuality.
The ultimate aim of AutoML is to make ML more approachable through generating a data analysis pipeline automatically that can comprise data-preprocessing, feature selection, and feature engineering methods, and also ML schemas and optimized parameter frameworks, for data, included. (In order to get the detailed discussion of these steps, visit the section)
Each of the, above mentioned, steps could be time consuming and demand for an ML expertise that can be incapacitating for neophytes. Therefore, AutoML enables to hone the use of big data more widely. For example, Google has released the Cloud AutoML for fabricating customized machine learning models on the basis of business to business(B2B) applications.
Key Features of AutoML
AutoML facilitates an advancing method to make machine learning available for non-tech experts as well for gaining the efficiency of machine learning and hastening up accelerating research on it. Besides that, other features are highlighted below;
It automates the entire model building process,
It employs the intelligent model selection which is applicable to versatile industries.
As diminishing 70-75% efforts as claimed while the statistical modelling phase, it becomes notorious and state-of-the-art ML models.
It helps in making ML applications more harmonious and scalable.
AutoML frameworks and services exclude the necessity of data professionalists for building ML and deep learning models.
What are the Benefits of AutoML?
Counting various benefits of AutoML as;
Connectedness to distinctive data sources,
Least time for hyperparameters optimization,
Easier for consolidating and organizing ensembles models,
All-inclusive algorithms to pick up from,
AutoML guide board in order to select an exclusive performance model,
Reduction in 75% of the time from the entire Model Building Phase through automation. (From)
When to Use AutoML: Classification, Regression and Time-series Forecasting
AutoML enhances machine learning model development process, authorises it users to explore an end-to-end machine learning pipeline for any issue; let’s understand where to use AutoML in context with machine learning techniques;
One of the important tasks in machine learning is Classification, it is a type of supervised learning where ML models learn through training data and deploy those learnings to new data. AutoML proposes featurization for various tasks like deep neural network text featuring for classification. (Recommend blog: How Does K-nearest Neighbor Works In Machine Learning Classification Problem?)
Classification models focus on predicting under which category new data will fall on the basis of learnings from its training data that is applicable to various tasks such as fraud detection, handwriting recognition, and object detection.
Alike classification, regression is also considered as important as other supervised learning techniques. Since regression models concentrate on predicting numerical output values relied on independent predictors. Also, it helps in building a relationship amid independent predictors via computing impact of one variable on other variables. (Most related blog: 7 Types of regression techniques)
The automated process, under regression, accepts training data and configuration settings and iterates automatically by sequences of several features normalization methods, models and hyperparameters settings in order to reach the best model.
Anticipating forecasts becomes a significant element of businesses, either it is revenue, inventory, sales and marketing or customer services. For such noticeable events, an individual can deploy AutoML for consolidating suitable techniques and approaches and can obtain a recommend and top-notch time-series forecasts. (In order to understand the time series analysis and forecasting, click here)
In more simple words, an automated time-series event is considered to be a multivariate regression problem. Former time-series values are turned out to be additional dimensions, for the regressor, along with other predictors which support in including added contextual variables and their relevance to each another during training.
Limitations of AutoML
Below are some limitations faced by experts in terms of successful implementation of AutoML on industry level;
Privacy and Security: While implementing AutoML, security and privacy are two major topics. In the context of security of AutoML, companies are looking for various technical solutions for various possibilities like automatic machine learning for privacy protection, automatic multi-party machine learning and automatic federation, etc.
But existing conditions can be ignored including the AutoML’s implementation wants the support of laws, regulation and industry norms. However, instead of ample advancement, companies are required to settle down and enhance the norms for federated learning and secured multi-party computing.
Explainability: Undoubtedly, AutoML can discover solutions, but what if the user demands an explainable model. Since an individual has different perspectives, therefore it is more difficult to make generalized explainable model.
There is a need to work on promoting the development of standards in association with an intelligible ML. AutoML could provide outcomes, and how much these outcomes are interpretable and explicable with norms and compatibility is evaluated by some experts.
Business Challenges For AutoML: With popularity and application, AutoML also heads with some problems as well including data and model applications, data inconsistencies during offline data analysis, AutoML processing of unstructured and semistructured data, etc.
Other issues are complexity in delivering AutoML in a dynamic environment than in a static environment. For dynamic feature learning, organizations have to acclimate changes in data faster, spotting configuration changes and readjusting to various models automatically. (Reference)
With the noteworthy machine learning system that incorporates complex phenomena with choices of several parameters, models and contours in order to obtain optimal performance, the resulting optimization could be too compact for researchers to probe. For that, automated systems, i.e., AutoML are beneficiary, their approaches are speculative and its applications are applause-worthy.
Some specific problems that can be addressed through AutoML are neural architecture search, model selection, feature engineering, model compressions, etc. However, in near times, AutoML will be noticed as key to the future and coming up technology that empowers multiple directions of research, analysis and implementation.