4 Reasons Why Companies are Using AutoML
The meager supply and high salaries of data scientists have led to a decision among many companies totally in keeping with artificial intelligence ― to automate whatever is possible. Case in point is machine learning. A Forrester study found that automated machine learning (AutoML) has been adopted by 61% of data and analytics decision makers in companies using AI, with another 25% of companies saying they’ll do so in the next year.
Automated machine learning (AutoML) automates repetitive and manual machine learning tasks. That’s no small thing, especially when data scientists and data analysts now spend a majority of their time cleaning, sourcing, and preparing data. AutoML allows them to outsource these tasks to machines to more quickly develop and deploy AI models.
If your company is still hesitating in adoption of AutoML, here are some very good reasons to deploy it sooner than later.
1. AutoML Super Empowers Data Scientists
AutoML transfers data to a training algorithm. It then searches for the best neural network for each desired use case. Results can be generated within 15 minutes instead of hours. Deep neural networks in particular are notoriously difficult for a non-expert to tune properly. AutoML automates the process of training a large selection of deep learning and other types of candidate models.
With AutoML, data scientists can say goodbye to repetitive, tedious, time-consuming tasks. They can iterate faster and explore new approaches to what they’re modeling. The ease of use of AutoML allows more non-programmers and senior executives to get involved in conceiving and executing projects and experiments.
2. AutoML Can Have Big Financial Benefits
With automation comes acceleration. Acceleration can be monetized.
Companies using AutoML have experienced increased revenue and savings from their use of the technology. A healthcare organization saved $2 million per year from reducing nursing hours and $10 million from reduced patient stays. A financial services firm saw revenue climb 1.5-4% by using AutoML to handle pricing optimization.
3. AutoML Improves AI Development Efforts
AutoML simplifies the process of choosing and optimizing the best algorithm for each machine learning model. The technology selects from a wide array of choices (e.g., decision trees, logistic regression, gradient boosted trees) and automatically optimizes the model. It then transfers data to each training algorithm to help determine the optimal architecture. Automating ML modeling also reduces the risk of human error.
One company reduced time-to-deployment of ML models by a factor of 10 over past projects. Others boosted lead scoring and prediction accuracy and reduced engineering time. Using ML models created with AutoML, customers have reduced customer churn, reduced inventory carryovers, improved email opening rates, and generated more revenue.
4. AutoML is Great at Many Use Cases
Use cases where AutoML excels include risk assessment in banking, financial services, and insurance; cybersecurity monitoring and testing; chatbot sentiment analysis; predictive analytics in marketing; content suggestions by entertainment firms; and inventory optimization in retail. AutoML is also being put to work in healthcare and research environments to analyze and develop actionable insights from large data sets.
AutoML is being used effectively to improve the accuracy and precision of fraud detection models. One large payments company improved the accuracy of their fraud detection model from 89% to 94.7% and created and deployed fraud models 6 times faster than before. Another company that connects retailers with manufacturers reduced false positive rates by 55% and sped up deployment of models from 3-4 weeks to 8 hours.
A Booming Market for AutoML
The global AutoML market is booming, with revenue of $270 million in 2019 and predictions that the market will approach $15 billion by 2030, a CAGR of 44%. A report by P&S Intelligence summed up the primary areas of growth for the automation technology: “The major factors driving the market are the burgeoning requirement for efficient fraud detection solutions, soaring demand for personalized product recommendations, and increasing need for predictive lead scoring.”
Experts caution that AutoML is not going to replace data scientists any time soon. It is merely a powerful tool that accelerates their work and allows them to develop, test, and finetune their strategies. With AutoML, more people can participate in AI and ML projects, utilizing their understanding of their data and business and letting automation do much of the drudgery.
The Easy Button
Whether you’re just getting started or you’ve been doing AI, ML and DL for some time, Dell Technologies can help you capitalize on the latest technological advances, making AI simpler, speeding time to insights with proven Validated Designs for AI.
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Validated Designs for AI speed time to insight with automatic machine learning, MLOps and a comprehensive set of AI tools. Dell PowerScale storage improves AI model training accuracy with fast access to larger data sets, enabling AI at scale to drive real‑time, actionable responses. VxRail enables 44% faster deployment of new VMs, while Validated Designs enable 18x faster AI models.
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AI for AI is here, making it easier and faster than ever to scale AI success. For more information, visit Dell Artificial Intelligence Solutions.
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