Automated machine learning (AutoML) is an emerging technology that is rapidly transforming the field of data science. AutoML tools automate key parts of the machine learning model development process, including data preprocessing, feature engineering, model selection, hyperparameter tuning and more. This enables data scientists, analysts and other users to build highly accurate machine learning models with greater speed and efficiency.
In this comprehensive blog post, we‘ll explore 27 of the most insightful statistics that demonstrate the explosive growth, widespread adoption and far-reaching benefits of AutoML solutions across industries.
AutoML Market Size & Growth Projections
The AutoML market has experienced massive growth over the past few years. Here are some key stats that underscore the sheer scale and momentum of this market:
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According to Research and Markets, the global AutoML market generated $270 million in revenue in 2019. By 2030, revenues are forecast to reach $15 billion – representing a staggering 44% CAGR over the decade.
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North America and Europe are expected to drive a significant majority of this growth, commanding over 65% market share by 2030 according to Research and Markets projections. This highlights the rapid mainstream adoption of AutoML among technologically advanced and data-driven organizations in these regions.
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In terms of absolute market value, some reports estimate the North American AutoML market alone could be valued at $4.78 billion by 2026, advancing at an above-average CAGR of 29.2% (Verified Market Research 2021).
AutoML Global Market Revenue Forecasts (Research and Markets 2022)
These optimistic growth trajectories demonstrate the massive upside potential of AutoML technology in the years ahead as more organizations seek to streamline and scale their AI and machine learning initiatives. The demand for automating complex, time-intensive ML workflows will only accelerate – especially among AI trailblazers in North America and Europe.
Current & Future AutoML Adoption Rates
In addition to hockey stick growth projections, current adoption rates also signal the expanding role of AutoML in enterprise environments.
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According to Forrester‘s 2020 survey of data and analytics decision makers, 61% of respondents from firms adopting AI said they have already implemented AutoML or are in the process of implementing it.
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The same Forrester survey found that 25% of respondents plan to roll out AutoML software at their organization within the next year.
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An older Spiceworks survey from 2019 revealed that 14% of responding organizations were already using AutoML tools at that time – a figure that has certainly increased substantially since then.
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McKinsey estimates that AutoML adoption among data scientists increased from just 9% in 2017 to 39% by 2020 – highlighting remarkably rapid uptake in only a few years.
This data highlights that while AutoML adoption is already mainstreaming, we‘re still in the relatively early stages with plenty of room for continued expansion. In particular, increased adoption of cloud, AI and big data is creating the optimal environment for AutoML to deliver value across organizations of all sizes and industries.
Quantifiable Benefits of Using AutoML
Now let‘s examine some real-world examples of the tangible benefits users across different verticals have experienced after implementing AutoML tools:
Financial Performance Improvements
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Singaporean real estate firm Ascendas Singbridge saw a 20% revenue increase amounting to tens of millions in additional income after deploying an AutoML-powered AI platform across its properties to optimize rental rates (DataRobot).
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UK appliance insurer D&G achieved between 1.5-4% revenue uplift by using AutoML to optimize pricing for 300,000 customers based on predictive analytics. This yielded an estimated £3.7 million in added revenue. (DataRobot).
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AutoML-based dynamic pricing models created for a European electronics retailer generated a 2.2% increase in gross margins through optimized price point determination and markdowns. These incremental gains are worth approximately €28.6 million annually in extra profitability. (McKinsey)
Operational Efficiency Gains
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Medical AI company Imagia cut image processing time from 16 hours to just 1 hour using Google Cloud AutoML Vision to expedite analysis of microscopy images for cancer detection (Google).
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Furniture company California Design Den reduced inventory carryovers by 50% with help from Google‘s AutoML Video Intelligence which automated analysis of recorded store interactions to improve demand forecasting (Google).
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Health IT provider Evariant achieved 10x faster model deployment – from months to weeks – using DataRobot‘s AutoML platform to create patient Health Risk Assessments (DataRobot).
Enhanced Model Accuracy
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Lead scoring accuracy improved from 80% to 95% for marketing agency G5 after implementing H2O Driverless AI. The AutoML tool also reduced model development time by 80% from weeks to days (H2O.ai).
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Using AutoML, Lenovo increased predictive accuracy from 80% to 87.5% for targeted marketing campaigns while slashing model training time from 4 weeks to 3 days – leading to $2 million in incremental sales (Amazon).
Higher Conversion Rates
- A Danish marketing firm helped the Copenhagen Concert Hall boost ticket sales by 83% while improving email click-through rates by 24% using AI propensity models built with DataRobot‘s automated feature engineering and machine learning (DataRobot).
As these examples demonstrate, AutoML can drive significant business value – from increased revenues to cost savings to operational improvements and beyond. The benefits span virtually every function from marketing to supply chain to healthcare.
AutoML for Fraud Prevention
In addition to commercial benefits, AutoML also proves invaluable for public sector and financial use cases such as fraud detection and prevention:
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PayPal employed H2O Driverless AI to improve their fraud detection from 89% to 94.7% while accelerating model development by 6x. AutoML allowed more models to be developed to handle new fraud patterns. (H2O.ai)
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A major North American insurance company achieved 92% accuracy in identifying fraudulent claims using an AutoML-driven AI solution – vastly exceeding the 75% accuracy rate of its previous manual process (McKinsey).
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A leading Brazilian telecom created 50-70% fewer false positives in identifying fraudulent account behavior using DataRobot‘s automated feature engineering and machine learning platform (DataRobot).
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A global financial services company reduced false positive rates by 55% in fraud detection while slashing model deployment time from 3-4 weeks to just 8 hours by implementing an integrated AutoML pipeline with Trifacta, AWS and DataRobot (Trifacta).
The quantifiable improvements in detection accuracy and efficiency underscore the immense value of AutoML in stopping fraud before it impacts revenues.
Leading AutoML Vendors by Funding and Employees
Several technology vendors have emerged as leaders in the AutoML space. Here‘s an overview of funding, valuations and employee counts for top vendors:
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DataRobot – Over $750 million in total funding raised across 9 rounds. The company was valued at $6.3 billion during its July 2021 Series G round (Pitchbook). DataRobot has over 1000 employees globally (LinkedIn).
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H2O.ai – $251 million in total funding raised across 9 rounds, giving it a valuation of $1.7 billion. H20.ai has between 51-100 employees (Crunchbase).
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Google Cloud -Has made 7 AutoML-focused investments including acquiring Kaggle. Overall parent Alphabet has over 135,000 employees worldwide (Statista).
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Dataiku– Over $400 million raised across 5 rounds with a valuation of $4.6 billion (TechCrunch). The company has 700-1000 employees (LinkedIn).
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dotData – $73 million in funding over 4 rounds. DotData claims between 51-100 employees (Crunchbase).
The extensive venture funding and growing workforces underline the strategic priority of AutoML for these leading technology vendors vying for market leadership.
Top AutoML Vendor Funding Totals (Crunchbase 2022)
Drivers of Adoption for AutoML
What factors are fueling the mass adoption of AutoML across industries? Some of the key drivers include:
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Data proliferation – The explosion of structured and unstructured data from IoT, websites, mobile etc. is making ML model development more complex. AutoML provides a scalable way to leverage large datasets.
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Need for faster model velocity – Shortened product cycles require rapidly iterating ML models. Manual approaches can‘t keep pace with demand. AutoML accelerates iteration.
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MLOps – AutoML complements MLOps practices by enabling continuous model integration and deployment with DevOps pipelines.
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Skill gaps – The shortage of qualified data scientists is problematic – AutoML democratizes ML to enable development by non-experts.
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Cloud adoption – Cloud computing provides the infrastructure to harness AutoML‘s computational intensity in scalable fashion.
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Expanding use cases – AutoML is moving beyond initial applications in areas like image recognition and NLP to new frontiers like fraud detection.
These factors will continue propelling AutoML adoption across verticals to new heights.
Best Practices for AutoML Success
For organizations exploring AutoML, here are 5 best practices to ensure successful outcomes:
1. Start with a focused business problem – Deploy AutoML to solve real-world problems like personalized marketing or predictive maintenance instead of open-ended technology experiments.
2. Ensure high-quality, cleaned data inputs – AutoML produces garbage models without properly pre-processed data
3. Leverage hybrid approaches – Blend AutoML with human oversight and domain expertise rather than going fully automated.
4. Build MLOps pipelines – Connect AutoML workflows into CI/CD infrastructure to continually retrain models on new data.
5. Choose vendor carefully – Requirements like data types supported, model interpretability and transparency will guide provider selection.
The Future of AutoML
Looking ahead, here are 3 key trends that will shape the next phase of AutoML innovation:
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Reinforcement learning – AutoML will increasingly leverage reinforcement learning for dynamic model optimization beyond static training data.
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Embedded and edge computing – AutoML will need to support model deployment across IoT devices, self-driving cars, smartphones and more.
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Enterprise integration – Seamless integration into enterprise stacks for data, model management and analytics will be a priority.
These developments will expand the possibilities for AutoML-driven transformation across virtually every industry.
Conclusion
The 27 statistics presented in this article underscore how AutoML adoption is hitting an inflection point. Driven by the pressing need for accelerated model velocity, AutoML brings immense value – from boosted revenues to improved operational metrics to enhanced ML model accuracy. As AutoML capabilities continue advancing, it will become an indispensable part of the data science toolkit – empowering a new generation of citizen data scientists while allowing expert practitioners to focus on higher-value work. The future of machine learning is undoubtedly automated.