Startups are dealing with an increasing amount of data, whether generated within the organization or collected from external sources, and it is becoming increasingly difficult to find effective ways to analyze and “manipulate” all of this data to gain a competitive advantage. increasingly challenging.
This is also driving the demand for new tools and information technologies in the fields of data science and machine learning. According to the Fortune Business Insights report, the global machine learning market size will reach $15.44 billion in 2021 alone, and it is expected to reach $21.17 billion this year and grow to $209.91 billion in 2029, with a compound annual growth rate of 38.8%.
At the same time, according to the United Market Research report, the global data science platform market size is 4.7 billion US dollars in 2020, and is expected to reach 79.7 billion US dollars by 2030, with a compound annual growth rate of 33.6%.
The concepts of “data science” and “machine learning” are sometimes easily confused or even used interchangeably, but they are actually two different concepts that are related because the practice of data science is part of a machine learning project.
According to the Master’s in Data Science website, data science is a field of study that uses scientific methods to extract meaning and insights from data, including developing data analysis strategies, preparing data for analysis, developing data visualizations, and building data models.
According to the Fortune Business Insights report, machine learning is the next subsection of the broader field of artificial intelligence. It uses data analysis to teach computers how to use algorithms and data-based models to learn, that is, to imitate the way humans learn.
The market demand for data science and machine learning tools has spawned a large number of startups developing cutting-edge technologies in the field of data science or machine learning.
- Founder & CEO: Liran Hason
- Headquarters: Tel Aviv, Israel
- Website: https://www.aporia.com/
Founded in 2020, Aporia has developed a full-stack, highly customizable machine learning observability platform that data science and machine learning teams can use to monitor, debug, interpret and improve machine learning models and data.
Aporia raised $5 million in a seed round, followed by a $25 million Series A in March 2022.
Aporia will use the funding to triple the size of its workforce by 2023, as well as expand its U.S. presence and the range of use cases its technology covers.
2. Black Crow AI
- Founder & CEO: Richard Harris
- Headquarters: New York, USA
- Website: https://www.blackcrow.ai/
Black Crow AI has developed a machine learning platform for e-commerce applications that enables online direct-to-consumer merchants to use their models to predict visitor behavior and future value when shopping. The software analyzes billions of data points in real-time to enhance customer experience, manage churn, and optimize marketing spend.
Founded in 2020, Black Crow AI raised $25 million in a Series A round in March, bringing its total funding to $30 million. Black Crow AI will use the funding to accelerate the discovery of new, usable machine learning scenarios in digital commerce and adjacent verticals.
- Co-founder and CEO: Gideon Mendels
- Headquarters: New York, USA
- URL: https://www.comet.ml/site/
Comet’s platform provides data scientists and data science teams with the ability to manage and optimize the entire machine learning lifecycle, including building and training models, experiment tracking, and model production monitoring to improve visibility, collaboration, and productivity.
Founded in 2017, Comet raised $50 million in a Series B round last November, but the company says it has seen a 5x increase in annual recurring revenue and a 2x increase in the size of its global workforce, with clients including Ancestry, Etsy, Uber and Zappos.
- Founder, CEO: Ryohei Fujimaki
- Headquarters: San Mateo, California, USA
- URL: https://dotdata.com/
dotData’s software provides automated feature engineering and enterprise AI automation capabilities for building artificial intelligence or machine learning models. During machine learning development, feature engineering is a critical step in finding important hidden patterns in the data used to develop and train machine learning models).
dotData’s flagship product is dotData Enterprise predictive analytics automation software, in addition to related products including the dotData Cloud AI automation platform, dotData Py and dotData Py Lite tools, and dotData Stream for real-time AI models.
Founded in 2018, dotData is a company spun off from NEC. It received $31.6 million in its Series B financing in April this year, bringing the total financing amount to $74.6 million. dotData has been using these external funds to accelerate its own product development.
- CEO: Andrey Korobitsyn
- Headquarters: San Jose, California, USA
- Website: https://neuton.ai/
Founded in 2021, Neuton has developed an automated, no-code “tinyML” platform and other tools for developing tiny machine learning models that can be embedded in microcontrollers to make edge devices smart.
Neuton’s technology is finding its way into a wide range of applications, including predictive maintenance of compressor pumps, protection against grid overload, room occupancy detection, handwriting recognition on handheld devices, gearbox failure prediction and water pollution monitoring devices.
- Founder, CEO: Edo Liberty
- Headquarters: San Francisco, USA
- Website: https://www.pinecone.io/
Pinecone develops a vector database and search technology that powers artificial intelligence and machine learning applications. Last October, Pinecone launched Pinecone 2.0, bringing the software from research labs to production applications.
Founded in 2019, Pinecone came out of stealth mode last year, raising $10 million in a seed round in January 2021 and $28 million in a series A round in March.
Gartner named Pinecone a “Cool Vendor” for AI and Machine Learning data in 2021.
7. Snorkel AI
- Co-founder and CEO: Alex Ratner
- Headquarters: Redwood City, California, USA
- Website: https://snorkel.ai/
Snorkel was founded in 2019 and originated from the artificial intelligence laboratory of Stanford University. The five founders of the company were all in this laboratory at the time to solve the problem of lack of labeled training data for machine learning development.
Snorkel launched Snorkel Flow in March this year, a data-centric system that accelerates the development of artificial intelligence and machine learning by using programmatic labels, which are also a key in the data preparation and machine learning model development and training process. step.
Snorkel’s valuation topped $1 billion in August 2021, when the startup raised $85 million in a Series C round to expand its engineering and sales teams and accelerate platform development.
- CEO: James Rebesco
- Headquarters: Austin, Texas, USA
- Website: https://striveworks.us/
Launched in 2018, Striveworks develops MLOps technology for those highly regulated industries.
Striveworks’ flagship product, the Chariot Platform, is primarily used for operational data science, easing the burden of creating artificial intelligence or machine learning solutions. The system oversees the process of data acquisition and preparation, as well as the training, validation, deployment, and monitoring of machine learning models, all in the cloud, on-premises or at the edge of the network.
- Co-founder and CEO: Mike Del Balso
- Headquarters: San Francisco, USA
- Website: https://www.tecton.ai/
Tecton has developed a machine learning function library platform that can reduce the deployment speed of machine learning applications from months to minutes. Tecton’s technology can automatically transform raw data, generate training data sets, and provide large-scale online inference capabilities.
Tecton was founded in 2019. The founder developed the Uber Michelangelo machine learning platform, and the company went out of stealth mode in April 2020.
- CEO: Manasi Vartak
- Headquarters: Menlo Park, California, USA
- Website: https://www.verta.ai/
Verta’s platform can be used by data science and machine learning teams to deploy, operate, manage and monitor models throughout the entire AI and machine learning model lifecycle.
Verta was named a “Cool Vendor” for core AI technologies by Gartner this month.
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