AutoML – bridging the skills gap with machine learning
- Early adopters of data science automation tools across industries are reporting significant time and cost savings as well as revenue gains
- The number of firms investing in big data and AI has ballooned to 33.9% from 27% in 2018
- More than 40% of data science tasks are expected to be automated in 2020
Is there anything that can stop AI? As the novel Covid-19 pandemic forces the world to put on its brakes, AI technologies like machine learning – AutoML in particular – have been continuing to develop at break-neck speeds at the beginning of the new decade.
Following a recent breakthrough by Google scientists at the start of a period of enforced lockdown, AutoML is seeing a wave of new progress in correlation with the explosion of big data, advanced analytics and predictive models. The increasing amount of viable data has meant that AI, machine learning (ML) and data science is undergoing reams of data and training that has served to boost the technology exponentially.
AutoML in 2020, can perform data pre-processing, as well as Extraction, Transformation and Loading tasks (ETL). This allows today’s in-demand yet rare-to-find highly skilled data scientists to bridge the skills gap by building models that use the best diagnostic and predictive analytics tools.
Several AutoML packages automatically do model selection, scoring and hyperparameter optimization, while services like Google’s AutoML-Zero and Amazon Forecast help determine the algorithm that best fits with the data.
Bridging the gap
The full impact of ML across a wide range of industries has already been vast up to 2020. From retail to banking, 5G to healthcare, not to mention its growing importance in software development and addressing environmental concerns, ML is continuing to take center stage in the growth of these industries, while becoming more vital to them.
The challenge for most companies with the explosion of big data is unlocking the value of that data. Those that fail to effectively apply data science will predictably put themselves at a competitive disadvantage. More than 40% of data science tasks are expected to be automated in 2020 and there has already been a report of significant time and cost savings, as well as revenue gains by early adopters of data science automation tools. However, almost every major company is looking for data science talent and the demand has evidently rapidly outpaced the supply of people with required skills. This lingering skills gap produces longer hiring times that inevitably causes project delays and higher costs.
AutoML’s greatest strength possibly lies in its potential to help non-tech companies and organizations with less data science expertise to build their own Machine Learning (ML) applications. It’s estimated that data scientists can spend up to 80% of their time on tedious and repetitive tasks that could instead be fully or partially automated. AutoML is well-positioned to alleviate organizations from a crippling talent shortage and their solutions make AI more accessible to everyone by automating complex manual data science processes. This helps to empower citizen data scientists with advanced analytical tools, while companies reap the benefits of emerging technology.
The recently notable launch of Cloud AutoML by Google, using Neural Architecture Search (NAS) and transfer learning claims to bring about the potential to make existing AI/ML experts not only more productive, but can also help less-skilled engineers to build a powerful AI system.
Time and efficiency
While first-generation AutoML platforms focused on automating machine learning as part of the data science process, AutoML 2.0 platforms are evolving to provide AI-focused data preparation, feature engineering automation, ML automation and automated production. The full cycle of data science automation involves automating the entire data science process to render a faster delivery of insights from months to just days.
An ML model built by humans often takes too much time and outcomes are usually inaccurate, while ML processes typically take less time to implement with AutoML than one under human supervision. According to a survey conducted in 2019, the number of firms investing in big data and AI has ballooned to 33.9% from 27% in 2018. This rise indicates that big data-based technologies and analytics are set to continue to increase, making AutoML an essential focus for organisations to process vast data in 2020 and beyond.
AutoML technologies build production-ready models quickly, without expensive data science, while providing businesses around the world the capabilities to make use of data-driven applications that are powered by statistical models. It’s hoped that a combination of ML, AI and deep learning can continue to fulfill the existing talent gap in the data science industry.
The need for more insights from big data is a beast that increases constantly and organizations need to transition their emphasis towards predictive power by utilizing the ability of complex automated machine learning.
Democratization of AI
Full-cycle data science automation allows enterprises to avoid investing in as many skilled data scientists or teams of engineers, while empowering citizen data scientists to bring AI to masses of people. Organizations can also stay on top of their projects more efficiently and remain accountable for their data-driven decisions, while meeting regulatory compliance requirements.
Hiring the right talented data scientists is an unforgiving task for any organization. Due to how expensive it is to hire one, AutoML provides a viable solution for companies to bridge the skills gap.
As economic uncertainty continues to face the global community, enabling a new class of AI/ML developers with smaller investments could become a game-changing proposition to increase competitive advantages.
30 November 2021
30 November 2021
26 November 2021