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Abacus.ai, a platform creating dev tools to develop and deploy enterprise AI technologies, today announced that it raised $50 million in a series C round led by Tiger Global with participation from Coatue, Index Partners, and Alkeon Ventures. The raise brings the company’s total funding to $90.3 million to date, and CEO Bindu Reddy says it’ll be used to further develop Abacus’ AI technologies while growing the company’s workforce.
While the percentage of firms investing greater than $50 million in big data and AI initiatives reached 64.8% in 2020 (up from 39.7% in 2018), organizations of all sizes still struggle to implement AI expeditiously — and successfully. About 80% of AI projects never reach deployment, according to Gartner, and those that do are only profitable about 60% of the time.
Founded in 2019 by Arvind Sundararajan, Siddartha Naidu, and Reddy, Abacus provides a service for organizations to develop AI models via modules that can stream, monitor, debias, merge, store, and transform data. According to Reddy, users without advanced data science knowledge and limited budgets can use it to iterate end-to-end systems comparable to Twitter’s and TikTok’s content feeds and Gmail’s autocomplete feature.
“We have seen rapid adoption of our platform as customers generate orders of magnitude more data, move all their operations to the digital realm, and are looking to AI models to make decisions,” Reddy told VentureBeat via email. “We will soon see an inflection point in AI adoption, as it becomes easier and easier to develop models and operationalize them.”
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Abacus embraces elements of “AutoML,” or the process of automating the application of machine learning to real-world problems. AutoML covers the complete pipeline, from raw datasets to deployable machine learning models, and data science teams are increasingly adopting it to overcome blockers in their organizations. Forrester reports that 25% of data and analytics decision makers whose firms are adopting AI said that they’re planning to implement AutoML software within the next year. Sixty-one percent said that they’d already implemented AutoML software or are were in the process of implementing it, according to the study.
Abacus conducts research and offers cloud AI services to help companies embed machine learning models into their processes. Customers pick a use case and point to their data, after which Abacus’ engine creates an AI system that can be used to make and share predictions.
Abacus says its system applies the startup’s research on generative models and neural architecture search to deal with noisy or incomplete data. It ostensibly identifies the best neural network that models a customer’s proprietary dataset and use cases spanning IT operations, marketing and sales, fraud and security, and forecasting and planning.
In addition, the system is good at configuring pipelines, scheduling model retraining on new data, provisioning model serving from raw data, and providing explanations for models’ predictions, Reddy says. “Common enterprise AI use cases like churn modeling, lead scoring, and anomaly detection have seen exponential growth [on our platform],” she added. “The pandemic has been great for AI companies — and specifically for us.”
Pulling from multiple data sources
Beyond the new funding, Abacus today announced what it’s calling “vision AI-as-a-service,” along with support for hybrid AI models that can generate predictions from language, vision, and tabular data. According to Reddy, customers can now use a combination of datasets to create models that extract intelligence from all of the available data on hand.
“For example, you can predict the closing price of homes based on unstructured data like listing description and house photos along with structured tabular data including number of bedrooms, bathrooms, and more by combining all this data and using the Abacus predictive workflow to generate a hybrid predictive model that combines all the data types,” Reddy explained. “This is a powerful way to extract intelligence from data.”
Despite competition from platforms like Amazon SageMaker, Google’s Cloud AutoML, and startups such as DataRobot and H2O.ai, Abacus says that over 10,000 developers across more than 6,000 customers including 1-800-Flowers have used its products to train roughly 20,000 real-time personalization, time-series forecasting, and anomaly detection models to date. The San Francisco, California-based company currently has 45 employees and plans to expand to 80 by the end of the year.
“Abacus has several vertically integrated workflows for common enterprise use cases, including natural language processing,” Reddy continued. “The new money is going to be used to continue to build out more vertical use cases like computer vision and to create more horizontal platform capabilities such as machine learning and deep learning operations modules.”
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