22 AutoML Case Studies: Applications and Results
Though there is a lot of buzz around autoML, we haven’t found a good compilation of case studies. So we built our comprehensive list of automated machine learning case studies so you can see how autoML could be used in your function/industry.
This AutoML case study list will help us to understand what AutoML is and how you can use it in your business function. The most common application areas of autoML are decision-making and forecasting. Read on to discover how AutoML can support your business function.
What are the typical results of AutoML projects?
In these case studies, we discovered companies gain various benefits from automation processes. These benefits support companies to improve their businesses and provide more efficient services. Below, you can find the top 3 typical results of those case studies.
Time savings
AutoML provides faster deployment time by automating data extraction, and algorithms. In the end, manual parts of the analyses are eliminated and the deployment time reduces significantly. As an example, Consensus Corporation reduced its deployment time from 3-4weeks to 8 hours.
Improved accuracy
As businesses continue, the data grows and, the industry trends change. AutoML automates these facts and removes any manual actions. As a result, any possible errors are eliminated and, continuously-evolving algorithms improve accuracy. With this benefit, companies can reach high levels of accuracy rate in their predictions. Trupanion can identify two-thirds of its customers will churn before they churn.
Democratization
Machine learning applications require high-level skills which make companies dependent on data scientists. By AutoML, these processes can be done without high-level knowledge.
What are typical cases for AutoML?
Companies can automate their machine learning processes for a variety of purposes. In most of these use cases, companies have already implemented machine learning and want to improve their performance. Mostly, companies want to have automated insights for better data-driven decisions and predictions. The typical processes we have observed from the case studies are:
The full list of case studies that we have collected from different AutoML vendors can be found below. You can filter the list by the vendor, industry, or use case and investigate the achieved results.
Company | Country | AutoML Tool | Industry | Use Case | Results |
|---|---|---|---|---|---|
Ascendas-Singbridge Group (ASG) | Singapore | DataRobot | Real Estate | Parking Lot Efficiency | ▪ 20% increase in revenue ▪ Reduced deployment time ▪ More accurate predictions on parking lot usage |
Avant | US | DataRobot | Finance | Loan Decisions | ▪ Time savings ▪ More accurate identification of risk |
California Design Den | US | Google Cloud AutoML | Retail & Consumer Goods | E-Commerce | ▪ 50% reduction in inventory carryovers ▪ Improved profit margins |
Consensus Corporation | US | DataRobot | Technology | Fraud Detection | ▪ 24% improvement in fraud detection ▪ 55% reduction in false-positives ▪ Reduced deployment time from 3-4 weeks to 8 hours |
DemystData | US | DataRobot | Technology | Product Quality | ▪ Democratization of the process ▪ Reduced cost by one tenth |
Domestic & General (D&G) | UK | DataRobot | Insurance | Customer Experience | ▪ Increased number of customers who gets optimal price from 40,000 to 300,000 ▪ Improved pricing optimization from 1.5% to 4% of the revenue |
Evariant | US | DataRobot | Healthcare | Service Delivery and Marketing Management | ▪ 10 times shorter deployment time ▪ More one-to-one involvements with clients ▪ Increased ROI ▪ Improved service quality |
G5 | US | H2O.ai | Real Estate | Marketing and Call Center Management | ▪ 5 times faster model building ▪ Improved accuracy to 95% |
Harmoney | Australia | DataRobot | Fintech | Credit Application Process | ▪ More accurate risk assessment ▪ Increased profitability ▪ Shortened credit application process |
Hortifrut | Chile | H2O.ai | Agriculture | Product Quality | ▪ Reduced deployment time from weeks to hours |
AutoML implementation challenges and solutions
Data quality issues
Challenge: Inconsistent data formats, missing values, and isolated data silos across departments often hinder AutoML adoption.
Solution: Establishing robust data governance frameworks and implementing unified data quality standards during the early stages ensures cleaner, more reliable datasets for modeling.
Technical integration problems
Challenge: Legacy systems frequently face compatibility issues with modern AutoML platforms and experience difficulties integrating via APIs.
Solution: Conducting thorough technical assessments before platform selection and planning for necessary system upgrades or middleware solutions helps ensure smoother integration.
Skills gap and resistance
Challenge: Limited understanding of AutoML tools among staff and skepticism from traditional analysts can slow down adoption.
Solution: Investing in comprehensive training programs that emphasize AutoML as a complementary tool fosters acceptance and empowers teams.
Resource constraints
Challenge: Budget limitations and the absence of dedicated teams for AutoML implementation can delay projects.
Solution: Initiating small-scale pilot projects with clear ROI helps demonstrate value early on, making it easier to secure further investment and resources.
Change management
Challenge: Shifting organizational culture towards data-driven automated decision-making may meet resistance.
Solution: Encouraging cross-functional collaboration and adopting a gradual implementation approach facilitates smoother cultural transitions.
You can also check out our sortable and data-driven list of AutoML Software. To learn more about AutoML, you can read our in-depth AutoML guide.
You can also review our list of AutoML solution providers to find the right vendor for your business.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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