The 10 Hottest Data Science And Machine Learning Startups In 2022

Data science and machine learning technologies continue to rapidly evolve, providing innovative ways for businesses to leverage their data assets and automate data-focused processes. Here are 10 startups with leading-edge data science and machine learning technology that have caught our attention this year.

Steep Learning Curve

As businesses wrestle with ever-greater volumes of data, both generated within their organizations and collected from external sources, finding efficient ways to analyze and “operationalize” all that data for competitive advantage is increasingly challenging.

That’s driving demand for new tools and technologies in the realms of data science and machine learning. The global machine learning market alone reached $15.44 billion in 2021, will reach $21.17 billion this year and is expected to grow to $209.91 billion by 2029 for a CAGR of 38.8 percent, according to a Fortune Business Insights report.

The global market for data science platforms, meanwhile, was valued at $4.7 billion in 2020 and is projected to reach $79.7 billion by 2030, a CAGR of 33.6 percent, according to an Allied Market Research report.

“Data science” and “machine learning” are sometimes confused and even used interchangeably. They are two different things, but they are related in that data science practices are key to machine learning projects.

Data science is a field of study that uses a scientific approach to extract meaning and insights from data, according to the Master’s in Data Science website. It includes developing data analysis strategies, preparing data for analysis, developing data visualizations and building data models.

Machine learning, a subsegment of the broader AI universe, uses data analytics to teach computers how to learn – imitating the way that people learn – using models based on algorithms and data, according to the Fortune Business Insights report.

The demand for data science and machine learning tools has spawned a wave of startup companies developing leading-edge technology in the data science/machine learning arena. Here’s a look at 10 of them:








*Snorkel AI




Top Executive: Liran Hason, Founder and CEO

Headquarters: Tel Aviv, Israel

Aporia develops a full-stack, highly customizable machine learning observability platform that data science and ML teams use to monitor, debug, explain and improve machine learning models and data.

Aporia raised $25 million in Series A funding in March 2022, 10 months after raising $5 million in seed funding.

The startup, founded in 2020, is using the financing to triple its headcount through early 2023, expand its presence in the U.S., and increase the range of use cases addressed by its technology.


Top Executive: Tuhin Srivastava, Co-Founder and CEO

Headquarters: San Francisco

Baseten, which officially launched in April of this year, offers a product that the company says speeds the process of moving from machine learning model development to production-grade application.

The technology, in private beta since the summer of 2021, automates many skills needed to move machine learning models into production, according to the company, helping data science and machine learning teams incorporate machine learning into businesses processes without back-end, front-end or MLOps knowledge.

Baseten was founded in 2019 by CEO Tuhin Srivastava, CTO Amir Haghighat and chief scientist Philip Howes who all previously worked at ecommerce platform developer Gumroad. In April Baseten raised $12 million in Series A funding and disclosed an earlier $8 million seed funding round.


Top Executive: Yonatan Geifman, Co-Founder and CEO

Headquarters: Tel Aviv, Israel

Deci develops a deep learning development platform for building next-generation AI and deep learning applications. The startup’s technology is designed to help resolve the “AI efficiency gap” – situations where computer hardware is unable to meet the demands of machine learning models that are growing in size and complexity.

The Deci platform helps data scientists eliminate this gap by accounting for production considerations early in the development lifecycle, reducing the time and costs of fixing problems when deploying models in production. The platform, incorporating Deci’s proprietary AutoNAC (automated neural architecture construction) technology, provides “a more productive development paradigm,” according to the company, helping AI developers leverage hardware-aware “neural architecture search” to build deep learning models to meet specific production goals.

In July Deci, founded in 2019, raised $25 million in a Series B funding round led by Insight Partners, just seven months after raising $21 million in a Series A round.


Top Executive: Vikram Chatterji, Co-Founder and CEO

Headquarters: San Francisco

Galileo develops a machine learning data intelligence platform for unstructured data that allows data scientists to inspect, discover and fix critical machine learning errors throughout the entire ML lifecycle.

In early November the company unveiled Galileo Community Edition, a free version of its platform that enables data scientists working on natural language processing to build models faster with higher quality training data.

Galileo emerged from stealth in May of this year with $5.1 million in seed funding. That was followed on Nov. 1 by a $18 million Series A funding round led by Battery Ventures. The company’s co-founders include CEO Vikram Chatterji, who was a cloud AI project management leader at Google; Atindriyo Sanyal, previously a software engineer at Apple and Uber, and Yash Sheth, a software engineer who worked on the Google Speech Recognizer system.


Top Executive: Andrey Korobitsyn, CEO

Headquarters: San Jose, Calif.

Neuton, founded in 2021, develops an automated, no-code “tinyML” platform and other tools for developing tiny machine learning models that can be embedded within microcontrollers that can make edge devices intelligent.

The company’s technology is finding its way into a wide range of applications including predictive maintenance for compressor water pumps, preventing electrical grid overloads, room occupancy detection, handwriting recognition on handheld devices, gearbox fault prediction and water pollution monitoring devices.


Top Executive: Edo Liberty, Founder and CEO

Headquarters: San Francisco

Pinecone develops a vector database and search technology for powering AI and machine learning applications. In October 2021 the company launched Pinecone 2.0, which the company said takes the software from the research lab to production applications.

Founded in 2019 and launched last year, Pinecone raised $28 million in Series A funding in March, adding to the $10 million in seed funding it raised in January 2021.

In October, the company expanded its machine learning search infrastructure portfolio with the debut of a new “vector search” solution that combines semantic and keyword search capabilities.

Gartner recognized Pinecone in 2021 as a “Cool Vendor” in the category of data for artificial intelligence and machine learning.


Top Executive: Piero Molino, Co-Founder and CEO

Headquarters: San Francisco

Predibase emerged from stealth in May of this year with its low-code machine learning platform that the company says lets both data scientists and non-experts quickly develop machine learning models with “best-of-breed” ML infrastructure. The software is currently in beta use at a number of Fortune 500 companies.

Predibase offers its technology as an alternative to traditional AutoML approaches to developing machine learning models for real-world problems. The platform uses declarative machine learning, which the company describes as allowing users to specify ML models as “configurations” or simple files that tell the system what a user wants and let the system figure out the best way to fill that need.

CEO Piero Molino and CTO Travis Addair, who both worked at Uber, founded the company with Chief Product Officer Devvret Rishi and Stanford University associate professor Chris Re. At Uber Molino and Addair created the Ludwig open-source framework for deep-learning models and the Horovod open-source framework for scaling and distributing deep-learning model training to massive amounts of data. (Predibase is built on Ludwig and Horovod.)

In May of this year Predibase raised $16.5 million in Seed and Series A funding rounds led by Greylock.

Snorkel AI

Top Executive: Alex Ratner, Co-Founder and CEO

Headquarters: Redwood City, Calif.

Snorkel, founded in 2019, has its roots in the Stanford University AI Lab where the company’s five founders researched ways to address the problem of the lack of labeled training data for machine learning development.

The Snorkel Flow data-centric system, which Snorkel just made generally available in March, is used to accelerate AI and machine learning development through the use of programmatic labeling, a key step in data preparation and machine learning model development and training.

Snorkel’s company valuation hit $1 billion in August 2021 when the startup raised $85 million in Series C funding, financing the company is using to grow its engineering and sales teams and accelerate development of its platform.


Top Executive: Cyril Brignone, Co-Founder and CEO

Headquarters: San Francisco

Vectice develops an automated data science knowledge capture and sharing solution. The startup’s technology auto-captures the assets that data science teams create for projects, including datasets, code, models, notebooks, runs and illustrations, and generates documentation throughout the project lifecycle, from business requirements to production deployment.

The Vectice software is designed to help businesses manage transparency, governance and alignment with their AI and machine learning projects and deliver consistent project results, according to the company.

Founded in 2020 by CEO Cyril Brignone and CTO Gregory Haardt, Vectice raised $12.6 million in a Series A funding round in January of this year, bringing its total funding to $15.6 million.


Top Executive: Manasi Vartak, Founder and CEO

Headquarters: Palo Alto, Calif.

Verta develops AI/ML model management and operations software that data science and machine learning teams use for deploying, operating, managing and monitoring inherently complex models throughout the AI and ML model lifecycle.

In August the company enhanced the enterprise capabilities of its MLOps platform including additions to its native integration ecosystem and additional capabilities around enterprise security, privacy and access controls and model risk management.

Founded in 2018 and launched in 2020, Verta was recognized this year by market researcher Gartner as a “Cool Vendor” in core AI technologies.