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Power your products with predictions in days regardless of your ML experience
We automate all major steps in the ML lifecycle from raw data ingestion to sustained production deployment
Query the future in our ‘Predictive Querying’ language that is as easy to use as SQL
Leverage automated machine learning with state of the art Graph Neural Network technology to drive higher accuracy even with less data
Deliver more predictions more quickly across every team, enabling your entire enterprise to more proactively choose the future you want
One platform
Customer Acquisition
Customer Retention and Next Best Action
Personalization
Forecasting and Anomaly Detection
Entity Resolution and Knowledge Graph Enrichment
Fraud and Abuse Detection
Anti Money Laundering
Embeddings for Data Scientists
Read about our platform capabilities in more detail
At Whatnot, AI plays a critical role in personalizing the shopper experience, driving cross-sell across categories and predicting future aggregate shopper behavior so we can shape our broader marketplace.
To this end, we are working with Kumo to deliver a service that is truly ground-breaking, allowing us to not only quickly launch these needed predictions with their very simple predictive querying language and accompanying APIs, but also drive dramatic model quality gains, including a doubling of both precision and recall over existing baselines in initial experiments. We've been thrilled by the progress so far, and the ability of the Kumo product to allow even non-technical teams to harness the power of AI from our data in the future.
VP of Engineering at Whatnot
At Yieldmo, we are hyper focused on cutting edge AI approaches to maximize the value of advertising for buyers, sellers, and consumers in a privacy-first way.
Our recent collaboration with the Kumo team offers us the opportunity to leverage their innovative graph neural network technology within our next-generation machine learning (ML) models for ad inventory curation.
So far, the early results have been very promising, showing a significant improvement in predictive power compared to leading solutions in the market today.
Head of Analytics and Data Science
Predictive analytics traditionally refers to the process of identifying meaningful patterns in historical data in order to predict future trends and events. Having the ability to forecast potential scenarios can help drive strategic decisions.
As every data scientist knows, it takes a significant number of manual steps to go from a business problem with raw data to a fully operational production model.
Most recommendations, promotions, and advertisements people encounter on a daily basis are the result of many complex data pipelines that transform consumer behaviors into targeted predictions.
Having a fully automated detection system at scale is critical for organizations to ensure trust with their customers, however this is incredibly difficult to do effectively in practice. In this blog post, we’ll dive into the mechanics of these systems and talk about some of the traditional approaches.
Today, when enterprises say they are “data-driven,” they primarily rely on a backward-facing approach for making decisions.
Independent audit verifies Kumo’s internal controls and processes. Kumo is proud to announce that we are now SOC 2 Type 1 certified and compliant and SOC 2 Type 2 is in progress.
I often get asked by aspiring founders to share lessons I’ve learned in the one year since the starting of Kumo. At Kumo I wear two hats - one as a co-founder and the second as a head of engineering
Graph neural networks (GNNs) have emerged as one of the leading solutions for ML applications. Most real-world data can be represented as graphs - see this blog for a comprehensive overview of what use cases are best solved with GNNs and their key advantages.
In this post, we’ll paint a picture of how effective graphs are in representing many real world problems, most likely including many of the business problems you face.
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