25 Machine Learning Startups To Watch In 2019
From powering personalized career sites that recommend open positions that are ideal for a given candidate based on their capabilities as eightfold.ai does today, to scaling the complexity and volume of machine learning algorithms, so they’re more accessible as DataRobot does, machine learning startups are taking on many of business’ most significant challenges. AI and machine learning have the potential to create an additional $2.6T in value by 2020 in Marketing and Sales, and up to $2T in manufacturing and supply chain planning according to the McKinsey Global Institute. Please see the latest roundup of machine learning forecasts and market estimates, 2019 for more market data on machine learnings’ exponential growth.
Alation – Alation offers a machine learning data catalog to help people find, understand, and trust data across their organizations. They’ve defined their solution to align with the needs of four dominant personas, including Chief Data Officers, Analysts, Stewards, and IT and Engineering. Their Data Catalog is known for its usability and intuitive design. More than 100 organizations, including the City of San Diego, eBay, Munich Re, and Pfizer, have adopted the Alation Data Catalog. Alation is funded by Costanoa Ventures, DCVC (Data Collective), Harmony Partners, Icon Ventures, Salesforce Ventures, and Sapphire Ventures. Alation has raised a total of $82M in funding over four rounds. Their latest funding was raised on Jan 17, 2019, from a Series C round.
Anodot – Capitalizes on the innate strengths of machine learning by continually looking for patterns using constraint-based modeling across the diverse data sets, businesses are relying on to operate daily. Similar to many machine learning startups that capitalize on the technology’s ability to learn continually, Anodot’s AI platform looks to eliminate blind spots in data and quantify root-causes in diverse data sets. Anodot’s Autonomous Analytics platform leverages advanced machine learning techniques to constantly analyze and correlate every business parameter, providing real-time alerts and forecasts, in their context, lowering time to detection and resolution. Anodot raised a total of $27.5M in funding over four rounds. The latest funding came from a Series B round on Dec 19, 2017, from Redline Capital. The following screen from their app is an example of how Anodot provides real-time anomaly detection.
Ablacon – Ablacon is a fascinating startup that has built a premier machine intelligence system to quantitatively and qualitatively understand and treat atrial fibrillation (AF).Their technology visualizes in real-time what is going on in the heart. They compute the electrographic flow, which allows treatment of atrial fibrillation faster, more precisely, and more reliably. Ablacon has raised a total of $21.5M in funding over 1 round. This was a Series A round raised on Apr 30, 2019, with Ajax Health.
Biofourmis – Biofourmis is a fast-growing global digital health tech start-up that is reinventing remote patient monitoring by combining AI, machine learning, and real-time monitoring. Their platform is capable of detecting personalized patterns predictive of a patient’s health condition and can find leading indicators of potential health deterioration. Their Biovitals platform is one of the most sophisticated personalized physiological data analytics engine based on human physiology that formulates personalized health models, resulting in highly optimized post-acute patient monitoring solutions and accurate prediction of patient health deterioration before it happens. They use connected devices and bio-sensors to capture physiological signals and detect anomalies. This AI-empowered continuous monitoring platform alerts medical professionals to intervene days before a critical event. Their RhythmAnalytics™ Platform recently received FDA clearance for AI-based automated interpretation of cardiac arrhythmias. The startup has raised a total of $41.
