The 12 Coolest Machine-Learning Startups Of 2020

The 12 coolest machine-learning startups include companies that are developing leading-edge technology for building machine-learning models and collecting and managing the vast amounts of data needed to train those models.


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Read the latest entry: The 10 Hottest Data Science and Machine Learning Startups in 2022

Artificial intelligence has been a hot technology area in recent years and machine learning, a subset of AI, is one of the most important segments of the whole AI arena.

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Machine learning is the development of intelligent algorithms and statistical models that improve software through experience without the need to explicitly code those improvements. A predictive analysis application, for example, can become more accurate over time through the use of machine learning.

But machine learning has its challenges. Developing machine-learning models and systems requires a confluence of data science, data engineering and development skills. Obtaining and managing the data needed to develop and train machine-learning models is a significant task. And implementing machine-learning technology within real-world production systems can be a major hurdle.

Here’s a look at a dozen startup companies, some that have been around for a few years and some just getting off the ground, that are addressing the challenges associated with machine learning.


Top Executive: Daeil Kim, Co-Founder, CEO

Headquarters: New York

AI.Reverie develops AI and machine -earning technology for data generation, data labeling and data enhancement tasks for the advancement of computer vision. The company’s simulation platform is used to help acquire, curate and annotate the large amounts of data needed to train computer vision algorithms and improve AI applications.

In October AI.Reverie was named a Gartner Cool Vendor in AI core technologies.


Top Executive: David Drai, Co-Founder, CEO

Headquarters: Redwood City, Calif.

Anodot’s Deep 360 autonomous business monitoring platform uses machine learning to continuously monitor business metrics, detect significant anomalies and help forecast business performance.

Anodot‘s algorithms have a contextual understanding of business metrics, providing real-time alerts that help users cut incident costs by as much as 80 percent.

Anodot has been granted patents for technology and algorithms in such areas as anomaly score, seasonality and correlation. Earlier this year the company raised $35 million in Series C funding, bringing its total funding to $62.5 million.


Top Executive: Francisco Martin, Co-Founder, CEO

Headquarters: Corvallis, Ore.

BigML offers a comprehensive, managed machine-learning platform for easily building and sharing datasets and data models, and making highly automated, data-driven decisions. The company’s programmable, scalable machine -earning platform automates classification, regression, time series forecasting, cluster analysis, anomaly detection, association discovery and topic modeling tasks.

The BigML Preferred Partner Program supports referral partners and partners that sell BigML and oversee implementation projects. Partner A1 Digital, for example, has developed a retail application on the BigML platform that helps retailers predict sales cannibalization—when promotions or other marketing activity for one product can lead to reduced demand for other products.


Top Executive: Matt Provo, Founder, CEO

Headquarters: Cambridge, Mass.

StormForge provides machine learning-based, cloud-native application testing and performance optimization software that helps organizations optimize application performance in Kubernetes.

StormForge was founded under the name Carbon Relay and developed its Red Sky Ops tools that DevOps teams use to manage a large variety of application configurations in Kubernetes, automatically tuning them for optimized performance no matter what IT environment they‘re operating in.

This week the company acquired German company Stormforger and its performance testing-as-a-platform technology. The company has rebranded as StormForge and renamed its integrated product the StormForge Platform, a comprehensive system for DevOps and IT professionals that can proactively and automatically test, analyze, configure, optimize and release containerized applications.

In February the company said that it had raised $63 million in a funding round from Insight Partners.


Top Executive: Gideon Mendels, Co-Founder, CEO

Headquarters: New York

Comet.ML provides a cloud-hosted machine-learning platform for building reliable machine-learning models that help data scientists and AI teams track datasets, code changes, experimentation history and production models.

Launched in 2017, Comet.ML has raised $6.8 million in venture financing, including $4.5 million in April 2020.


Top Executive: Florian Douetteau, Co-Founder, CEO

Headquarters: New York

Dataiku’s goal with its Dataiku DSS (Data Science Studio) platform is to move AI and machine-learning use beyond lab experiments into widespread use within data-driven businesses. Dataiku DSS is used by data analysts and data scientists for a range of machine-learning, data science and data analysis tasks.

In August Dataiku raised an impressive $100 million in a Series D round of funding, bringing its total financing to $247 million.

Dataiku’s partner ecosystem includes analytics consultants, service partners, technology partners and VARs.


Top Executive: Ryohei Fujimaki, Founder, CEO

Headquarters: San Mateo, Calif.

DotData says its DotData Enterprise machine-learning and data science platform is capable of reducing AI and business intelligence development projects from months to days. The company’s goal is to make data science processes simple enough that almost anyone, not just data scientists, can benefit from them.

The DotData platform is based on the company’s AutoML 2.0 engine that performs full-cycle automation of machine-learning and data science tasks. In July the company debuted DotData Stream, a containerized AI/ML model that enables real-time predictive capabilities.


Top Executive: Ashutosh Garg, Co-Founder, CEO

Headquarters: Mountain View, Calif.

Eightfold.AI develops the Talent Intelligence Platform, a human resource management system that utilizes AI deep learning and machine-learning technology for talent acquisition, management, development, experience and diversity. The Eightfold system, for example, uses AI and ML to better match candidate skills with job requirements and improves employee diversity by reducing unconscious bias.

In late October Eightfold.AI announced a $125 million Series round of financing, putting the startup’s value at more than $1 billion.

Top Executive: Sri Ambati, Co-Founder, CEO

Headquarters: Mountain View, Calif. wants to “democratize” the use of artificial intelligence for a wide range of users.

The company’s H2O open-source AI and machine-learning platform, H2O AI Driverless automatic machine-learning software, H20 MLOps and other tools are used to deploy AI-based applications in financial services, insurance, health care, telecommunications, retail, pharmaceutical and digital marketing. recently teamed up with data science platform developer KNIME to integrate Driverless AI for AutoMl with KNIME Server for workflow management across the entire data science life cycle—from data access to optimization and deployment.


Top Executive: Asaf Somekh, Co-Founder, CEO

Headquarters: New York

The Iguazio Data Science Platform for real-time machine learning applications automates and accelerates machine-learning workflow pipelines, helping businesses develop, deploy and manage AI applications at scale that improve business outcomes—what the company calls “MLOps.”

In early 2020 Iguazio raised $24 million in new financing, bringing its total funding to $72 million.


Top Executive: Luis Ceze, Co-Founder, CEO

Headquarters: Seattle

OctoML’s Software-as-a-Service Octomizer makes it easier for businesses and organizations to put deep learning models into production more quickly on different CPU and GPU hardware, including at the edge and in the cloud.

OctoML was founded by the team that developed the Apache TVM machine-learning compiler stack project at the University of Washington’s Paul G. Allen School of Computer Science & Engineering. OctoML’s Octomizer is based on the TVM stack.


Top Executive: Mike Del Balso, Co-Founder, CEO

Headquarters: San Francisco

Tecton just emerged from stealth in April 2020 with its data platform for machine learning that enables data scientists to turn raw data into production-ready machine-learning features. The startup’s technology is designed to help businesses and organizations harness and refine vast amounts of data into the predictive signals that feed machine-learning models.

The company’s three founders: CEO Mike Del Balso, CTO Kevin Stumpf and Engineering Vice President Jeremy Hermann previously worked together at Uber where they developed the company’s Michaelangelo machine-learning platform the ride-sharing company used to scale its operations to thousands of production models serving millions of transactions per second, according to Tecton.

The company started with $25 million in seed and Series A funding co-led by Andreessen Horowitz and Sequoia.