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The Emergence of Automated Machine Learning in Industry

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By Parul Pandey | minute read | June 30, 2021

Category: AutoML , Company
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This post was originally published by K-Tech, Centre of Excellence for Data Science and AI, powered by NASSCOM. The link of the post can be found here.

The concept of Automated Machine Learning has gained much traction recently. Automated Machine Learning, also known as AutoML, is the process of automating the end-to-end process of applying machine learning to real-world problems. A typical machine learning process consists of several steps: ingesting and preprocessing data, feature engineering, model training, and deployment. In conventional machine learning, every step in this pipeline is monitored and executed by humans. Tools for automatic machine learning (AutoML) aim to automate one or more stages of these machine learning pipelines making it easier for non-experts to build machine learning models while removing repetitive tasks and enabling seasoned machine learning engineers to build better models faster.

Source: Automated Machine Learning (AutoML) Market

Artificial Intelligence (AI) has the potential to make a big difference in every facet of our lives, especially in areas like healthcare, education, and environmental conservation. However, many enterprises still struggle to deploy machine learning solutions effectively. It is primarily due to the issues of talent, time, and trust, which affect these businesses.

  • The traditional machine learning (ML) process heavily relies on human expertise. As a result, before starting the ML journey, a company needs to invest in expert data scientists, researchers, and mathematicians. Unfortunately, there is a considerable talent gap with an acute shortage of experienced and seasoned data scientists in the industry today.
  • Secondly, time is of the essence here. When machine learning solutions drive business decisions, it is crucial to get the results quickly. Some of the current ML solutions take months to deploy, which affects their outcomes. Also, due to the heavy manual dependence, there are chances of errors creeping into the workflow.
  • Finally, it is imperative to tackle the issue of trust. Many companies fail to translate model predictions into understandable terms for stakeholders. Although there are systems in place for interpretability and explainability in conventional ML systems, a lack of knowledge and experience makes the implementation hard.
The traditional machine learning workflow | Source:H2O.ai

AutoML is an effort towards democratizing machine learning by making its power available to everybody, rather than a select few. AutoML enables people with diverse skill sets to work on ML problems. Automating repetitive tasks allows data scientists to focus on essential aspects of the ML pipeline, like data gathering and model deployment. Not only does AutoML remove the manual dependency, but it also reduces the time to put models into production from months to weeks and days. Another critical aspect of AutoML solutions today is providing explanations and reason codes for their predictions.

With AutoML taking care of most of the repetitive tasks, humans can give more time to framing and understanding the business problems and addressing issues like model deployment and data preparation. AutoML and data scientists can work together in conjunction to accelerate the machine learning process so that more accurate results can be obtained in less time.

I work at H2O.ai, an AutoML company itself, and our products enable organizations to build world-class AI models and applications rapidly. H2O AI Cloud, our flagship product, makes it possible for enterprises to harness the true potential of AI by enabling every employee, customer, and citizen with sophisticated AI technology and easy-to-use AI applications.

Gartner 2021 Magic Quadrant for Data Science and Machine Learning Platforms.

Leveraging Automatic Machine Learning can help companies accelerate and scale their AI efforts and journey to becoming an AI company. With the availability of extensible AutoML solutions in the market, data scientists can leverage the benefits of automation without losing the ability to influence optimization. Automated Machine learning is a big part of the future of machine learning, and its widespread adoption is a testimony to this fact. Many advancements are being made to continuously upgrade and enhance the capabilities of these solutions to make machine learning more accessible and more valuable. It is essential to make AI accessible to everyone for the sake of social and economic stability, and AutoML, in a way, is a step in that direction.

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Parul Pandey

Parul focuses on the intersection of H2O.ai, data science and community. She works as a Principal Data Scientist and is also a Kaggle Grandmaster in the Notebooks category.