Deploying GNNs poses significant challenges. See how Kumo’s architecture is designed to handle at scale deployments
How to build an efficient and scalable end-to-end system for graph learning in data warehouses.
Last month, Jure Leskovec, Co-founder and Chief Scientist at Kumo, unveiled the revolutionary Kumo.AI platform, marking a significant milestone in reshaping the landscape of the machine learning lifecycle.
We’re excited to announce the general availability of the Kumo.AI platform, enabling the rapid creation and deployment of state-of-the-art AI models on private enterprise data. AI practitioners can now use our intuitive SQL-like Predictive Querying Language to build multiple task-specific AI models in a single day. The Kumo.AI platform empowers enterprises to unlock customer-focused use cases, such as personalization, churn and LTV prediction, fraud detection, and forecasting
Using AI and predictive machine learning (ML) to get actionable forward-looking insights from data are no longer a competitive edge, rather a necessity for ecommerce businesses. With so many options available, consumers expect high quality, personalized experiences that give them exactly what they are likely interested in.
Online food delivery is a massive market worth over $150B annually and growing at an exponential pace. With so much opportunity, competition is fierce - the market is incredibly fragmented, with new entrants coming in every year.
The world of ecommerce today is predominantly powered by machine learning to optimize the user experience and drastically improve how people interact with and consume goods and services.
The online gaming industry is one of the largest and most lucrative businesses in the world, projected to reach $321 billion in revenue by 2026.
Ecommerce has revolutionized the way we shop and interact with brands today. Within a few clicks, consumers can access a vast array of highly personalized products and services.
Every online and mobile game developer is familiar with the basic equation for successfully monetizing a game: CPI < LTV (i.e. you need to be able to generate more revenue from your players than it costs to acquire them).
Artificial intelligence is transforming industries across the globe, and personal finance is no exception. The sheer scale of financial data – from one billion daily credit card transactions to the 130 million Americans with personal loans – requires powerful predictive capabilities for financial service providers to find true signals in such noisy data.
We're excited to announce our partnership with Snowflake, the Data Cloud, with the common goal to democratize machine learning (ML) in the enterprise.
Kumo.ai presents an entirely new approach to performing machine learning at scale, one that drastically simplifies the end-to process and accelerates time-to-value.
In our digital world, privacy is always top of mind for consumers, vendors, and regulators. The digital landscape is constantly changing with respect to consumer data as policies like GDPR enforce limited tracking, and even more so with the Apple iOS 14 update implementing ATT, which enforces new data sharing policies.
In today’s fast-paced world of ecommerce and online marketplaces, AI and predictive ML have become essential tools in allowing businesses to stay competitive and drive sustainable growth.
The explosive emergence of OpenAI’s ChatGPT has generated a wave of intense interest among enterprises of all sizes and industries in leveraging Large Language Models (LLMs) to create chat-based interfaces for their end users.
Using AI to enable efficient cross-selling and upselling has become increasingly important in helping businesses increase revenue and profitability while simultaneously improving customer engagement and loyalty.
Graph neural networks (GNNs) have emerged as a leading solution for machine learning (ML) applications, as many real-world problems and data can be effectively modeled as graphs.
Kumo.ai enables users across the enterprise to rapidly develop, evaluate and deploy state-of-the-art predictions in production in hours instead of months.
When marketing resources are constrained, it is critical for businesses to identify and focus on the future high value customers that will have the biggest impact as these users represent the biggest opportunity for the business.
If you’ve ever unsubscribed from a notification, you’re aware of what a poor notification experience is. Similarly, if you’ve ever clicked on a notification, you’ve likely found it to be useful and relevant. In this article, we’ll share what a good notifications strategy looks like, and how to use AI to improve your approach for every user.
Personalization is all around us. If you’ve ever received a relevant recommendation on a website, a notification from an app, or a promotion in your inbox, you’ve been delivered a personalized experience.
Customers are watching their pennies now, just like the rest of us. This means at a time that companies are doing everything they can to grow revenue, they need to be on high alert to anything that could push their customers away.
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
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.
Our vision is simple: to make it as easy for you to ‘Query the Future’ as it is for you to query the past using SQL on historic data today. All enabled by the cutting edge of deep learning innovation.
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