How Automated Machine Learning Addresses the Challenges of Traditional ML Models?
For a while, the industry is disrupted with the machine learning models. Every organization equipped with technologically innovative resources employ machine learning models. Machine learning is a subsidiary of artificial intelligence which aids the systems to learn through consequent experience. Machine learning models are trained with humongous amount structured and structured data accumulated from diverse sources. Machine learning Pioneer Tom M Mitchell defines machine learning as “The study of computer algorithms that allow computer programs to automatically improve through experience”. As machine learning models learn through experience, they do not require human intervention. The global machine learning market is expected to grow from US$1.03 billion in 2016 to US$8.81 billion by 2022, at a CAGR of 44.1%.
Challenges of Traditional Machine Learning Models
Data scientists play a key role in training a machine learning model. They clean, organize and collect the relevant data sets from the data pool ensuring that correct data is fed to the model. It is observed that data scientists invest 60% of their time in cleaning and organizing data sets and 19% of the time in collecting relevant datasets. Moreover, in a traditional machine learning model, data scientists follow a sequential approach of data mining, analyzing, filtering the raw data, selecting the algorithm that can be trained, tuning and testing the algorithm and repeating the entire process to find the best algorithm. As there is no defined best algorithm, data scientists have to invest their time in training and testing individual algorithms until they find a suitable one. Henceforth, this is considered to be a major drawback of the traditional machine learning model. The entire process becomes time-expensive not only for the employees but organizations as well, who can employ skills of data scientists in performing valuable and critical tasks. Additionally, as these tasks are performed by humans, they are prone to having errors thus germinating the concept of bias in AI algorithms.
What is Automated Machine Learning Model?
Automated Machine learning is considered as a suitable and comprehensive approach to address and eradicate the challenges associated with machine learning algorithms and models. Automated machine learning ensures end-to-end automation of the ML algorithm and model. It is designed to conduct automated data analysis, so that accurate and précise results can be achieved. Automated Machine learning algorithm unburdens the data scientists, as it not only cleans and collects the data but also automatically trains the models as well. Through its automated feature engineering attribute, AutoML automatically collects the data, extracts meaningful information, and detects any distorted data in the entire process. Furthermore, it optimizes the learning and function of a suitable algorithm, automates the data storage and identifies leaky spots and misconfigurations. This ensures accuracy and precision in the result, thus eliminating the risk of permeating biases. Additionally, as data scientists are not required to either clean or collect the data, organizations can utilize their skills in solving more critical and urgent problems.
Sectors Employing Automated Machine Learning
A report by Mckinsey Insight indicates that the deployment of AutoML has already initiated across the industry. Many deep learning models are deploying end to end automation, so that data scientists can perform very few tasks. Additionally, many companies in telecom, retail and energy sector are deploying AutoML to achieve accurate and precise results. Amazon’s Alexa has automated its deep learning algorithm to produce end-to-end user interface. Google Cloud AutoML is a machine learning model which ensures developers with little knowledge can reap the benefit of training high-quality models.
Impact on Data scientists Hiring
The report by Mckinsey insight predicts that though the data scientists will be required to perform technical tasks, the demand for AutoML practitioners is likely to be twice as high as the demand for data scientists as companies build out their strategies with both levels of expertise.
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