Enterprises are dealing with an increasing amount of data, whether generated within the organization or collected from external sources. How to find effective ways to analyze and "operate" all this data to gain a competitive advantage is changing. Becoming more and more challenging.
This has also driven the demand for new tools and information technologies in the field of data science and machine learning. According to the Fortune Business Insights Report, the global machine learning market size will reach US$15.44 billion in 2021 alone, and is expected to reach US$21.17 billion this year and grow to US$209.91 billion by 2029, with a compound annual growth rate of 38.8%.
At the same time, according to a joint market research report, the global data science platform market size was US$4.7 billion in 2020 and is expected to reach US$79.7 billion 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 in fact they are two different concepts and there is a correlation between them because data science Practice is key to machine learning projects.
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 constructing data model, etc.
According to the Fortune Business Insights Report, machine learning is the next sub-section of the broader field of artificial intelligence. It uses data analysis to teach computers how to learn using models based on algorithms and data. That is Mimic the way humans learn.
The market demand for data science and machine learning tools has spawned a large number of start-up companies developing cutting-edge technologies in the field of data science or machine learning, including the following 10 companies:
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 and debug , interpret and improve machine learning models and data.
Aporia raised $5 million in seed funding, followed by $25 million in Series A funding in March 2022.
Aporia will use the funds to triple its employee size by 2023, as well as expand its business scale in the United States and the range of use scenarios covered by its technology.
Black Crow AI has developed a machine learning platform for e-commerce applications for online direct-to-consumer merchants. Their model predicts 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.
Black Crow AI was founded in 2020 and received US$25 million in Series A financing in March this year, bringing the total financing amount to US$30 million. Black Crow AI will use the funds to accelerate the discovery of new, usable machine learning scenarios in digital commerce and adjacent verticals.
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 increase visibility, collaboration, and productivity.
Comet, founded in 2017, raised $50 million in Series B funding in November last year, but the company said it has grown its annual recurring revenue fivefold and its global workforce by threefold. Customers include Ancestry, Etsy, Uber and Zappos.
dotData’s software provides automated feature engineering and enterprise AI automation functions for building artificial intelligence or machine learning models. In the machine learning development process, 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, and it also offers related products including dotData Cloud AI automation platform, dotData Py and dotData Py Lite tools, and dotData Stream for real-time AI models.
DotData was founded in 2018 as a company spun off from NEC. It received US$31.6 million in Series B financing in April this year, bringing the total financing amount to US$74.6 million. dotData has been using these external funds to accelerate its own product development.
Neuton, founded in 2021, develops an automated, no-code “tinyML” platform and other tools for developing microcontroller-embeddable Tiny machine learning models to make edge devices smart.
Neuton’s technology is entering a wide range of applications, including predictive maintenance of compressor water pumps, preventing grid overload, room occupancy detection, handwriting recognition on handheld devices, gearbox failure prediction and water pollution monitoring equipment, etc. .
Pinecone develops a vector database and search technology to power artificial intelligence and machine learning applications. Last October, Pinecone launched Pinecone 2.0, taking the software from research labs to production applications.
Pinecone was founded in 2019. It came out of stealth mode last year and received US$10 million in seed round financing in January 2021 and US$28 million in Series A financing in March this year.
Gartner named Pinecone a "Cool Vendor" in artificial intelligence and machine learning data in 2021.
Snorkel was founded in 2019 and originated from the Artificial Intelligence Laboratory of Stanford University. The five founders of the company were working in this laboratory to solve the problem of lack of labeled training data in 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 through the use of programmatic tags, which are also used for data preparation and machine learning model development and A critical step in the training process.
Snorkel’s valuation topped $1 billion in August 2021, when the startup secured $85 million in Series C funding to expand its engineering and sales teams and accelerate platform development.
Striveworks was founded in 2018 and mainly develops MLOps technology for those highly regulated industries.
Striveworks’ flagship product, Chariot Platform, is primarily designed 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 network edge.
Tecton has developed a machine learning function library platform that can shorten the deployment speed of machine learning applications from months to a few minute. Tecton's technology can automatically convert 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. Later, the company came out of stealth mode in April 2020.
Verta’s platform can be used by data science and machine learning teams to deploy and operate throughout the entire artificial intelligence and machine learning model lifecycle , management and monitoring models.
Verta was named a “Cool Vendor” for core AI technology by Gartner this month.
The above is the detailed content of Mid-year review: Ten hot data science and machine learning startups in 2022. For more information, please follow other related articles on the PHP Chinese website!
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