This article was published as a part of the Data Science Blogathon.
Introduction
Often we have heard the term Automated Machine Learning or AutoML being used when it comes to machine learning, but what is AutoML, and is the hype around it real? The article is a deep-dive on AutoML, how it can make the power of machine learning available to everyone, and how certain AutoML tools can be tailored to your business needs.
Today, companies are incorporating AI into their internal processes to improve the quality of their product pipeline, sales forecasting, predicting customer needs, designing new products/ inventory, and even optimizing their workplaces. As AI/ ML continues to become important for businesses, its challenges have also become more recognizable, particularly when it comes to accessibility of AI and data science.
The need for an intelligence revolution in a challenging environment
Currently, designing machine learning models includes tasks such as data pre-processing, data preparation, feature engineering, feature selection, etc. We then select the best algorithm and tune our parameters in order to get the best results. Designing these ML models can be extremely time-consuming. To add to it, AI/ ML is a field with high barriers to entry that requires domain expertise that few companies can afford. Companies often struggle with having the right number of data scientists on board and are also faced with the challenge of training employees.
Having said that, how can companies lower the barrier of entry and ensure that AI is accessible to everyone? How can we democratize AI and implement citizen data platforms to solve the current challenges? Can machine learning itself be automated?
Enter: AutoML.
An evolving technology area, AutoML involves a series of techniques used to automate time-consuming tasks of machine learning model development. AutoML has the ability to make it possible for business users within an organization to understand, prepare, build, develop, deploy, and monitor ML applications for challenging workflows.
The next wave of machine learning
AutoML can identify discrepancies, errors, and other issues within the data, and present the user with choices, suggestions as well as suggest outliers. Once the expert is presented with all this information, they can seamlessly curate multiple models, saving them time and effort. How AutoML applications work is this way; first, set the goal and upload the data. AutoML then generates and tests the ML models, recommends a working ML model, and monitors model performance.
For businesses, AutoML helps save time and money, enabling them to build efficient ML models, maximize their output more efficiently, and increasing their accuracy and turnaround time to production-ready models. It reduces the need to hire many domain experts, reduces errors from occurring, and reduces the time spent in developing and testing ML models.
Take, for example, a wildlife conservation organization – in order to track wildlife populations in a particular area, it has to track wildlife movement to better understand human impact/ interactions and its short and long-term effects on the ecology. To track and monitor wildlife, it will need to set up camera traps and then manually analyze and appropriately tag lakhs of images which is a labor and time-intensive process. With AutoML, it can automate the process of analyzing and tagging images, in turn, saving time, cutting costs, and, essentially giving them quicker and more accurate results.
Once AutoML tools step into the picture, work that would ordinarily take weeks to code up, it can do within hours. Essentially, it is a zero or less-coding platform making companies more productive and giving data scientists the ability to focus on solving more innovative and complex problems. In the retail industry, for example, companies can successfully predict which product a customer is likely to buy with a high level of accuracy as well as the churn rate – that is the kind of predictability and transparency that AutoML brings to enterprises and data scientists.
AutoML frameworks and solutions
It is important to note that currently, AutoML open-source and commercial tools such as TPOT, H2O.ai, Google AutoML, and DataRobot are some of the ones best suited for streamlining the development of tasks wherein the goal is to predict an outcome/ result. These popular solutions tend to automate some or all of the ML pipeline.
For instance, DataRobot, the enterprise AI platform democratizes data science and automates the end-to-end solution of building, deploying, and maintaining AI at scale. It eliminates the reliance on manual workflows, automates repetitive and time-intensive steps, enables new users to build highly-accurate models, and provides a fast-path for getting AI into production.
Democratizing data science
The key to standing out as an AI-driven enterprise is not just hiring skilled and talented data scientists but also empowering those within your organization who knows your business best with powerful tools and software that fills the skills gap. After all, not all companies have the budgets and bandwidth to hire data scientists like some of the biggest technology giants.
With AutoML one doesn’t need extensive training or knowledge of machine learning – essentially, you feed in the data into the AutoML tool/ software and the system basically goes through the entire model-building and deployment cycle; right from understanding the data, checking the quality of the data, feature engineering, developing models, fine-tuning the models and then recommending the results. It enables everyone within the organization to run complex data science models, in turn creating a new class of citizen data scientists. In doing so, AutoML opens up doors of opportunities for businesses to create machine learning models that were until then inaccessible to them.
AutoML comes of age
Instead of eliminating data science jobs, in years to come, AutoML is going to become a more popular tool for businesses and data scientists to adopt in order to stay ahead in an extremely competitive environment. By automating repetitive tasks it allows data scientists to spend more time on the business problem at hand. In the meantime, it also makes the technology available to everyone in an organization as opposed to a select few.
AutoML has indeed shifted the focus of many businesses worldwide. Over the next few years, the use of AutoML will continue to reduce the necessity to write extensive codes; the mathematical and statistical aspect will still be of use, and logical skills will still be required, however, all the pre-processing work will become more in-built. There will also be an increase in demand for AutoML practitioners as well as data scientists who can understand which tasks can be safely automated without bias or inaccuracy.
As enterprises start moving towards becoming AI-driven enterprises, they will need to start rethinking their talent strategies when it comes to upskilling domain experts, reassess their requirements, and understand their limits and opportunities.
Author details:
Chetan Alsisaria – CEO & Co-Founder, Polestar Solutions & Services Pvt Ltd.
An excellent business leader and technologist, Chetan have, over the past 17 years, led many technology-driven business transformation engagements for clients across the globe for Fortune 500 companies, large/mid-size organizations, new-age companies as well as in the government sector.
Chetan’s area of expertise lies in identifying strategic growth areas, forming alliances, building high potential motivated teams, and delivering excellence in the areas of data analytics and enterprise performance management.
As Co-Founder & CEO of Polestar Solutions, he has defined the business processes for sales, marketing, human resource, delivery, and finance. As a leader, his focus has been on sustainable growth with a win-win situation for all stakeholders (employees, clients, suppliers, society, at-large).
Prior to founding Polestar, Chetan worked with leading consulting firms such as PricewaterhouseCoopers, Deloitte, and Ernst & Young.
