Emerging AI And Machine Learning Tool Vendors To Know In 2022
As part of CRN’s Emerging Vendors for 2022, here are 11 AI and machine learning technology startups, founded in 2016 or later, that solution providers should be aware of.
Automating business processes and decisions is key to operational efficiency today. And that is driving the increased use of artificial intelligence and machine learning technology to enable that automation.
That, in turn, is fueling a wave of startup companies that provide the tools and platforms developers and data scientists need to build AI and ML capabilities and deploy and manage them within production environments.
Just about every IT vendor, from semiconductor and system manufacturers to leading application developers, are building AI and ML capabilities into their products. But as is often the case in new areas of IT, startups are setting the pace in AI and ML with leading-edge technologies—some barely out of the research labs of places like Stanford University, Carnegie Mellon University and the Massachusetts Institute of Technology.
The global machine learning market was valued at $15.44 billion in 2021, according to a Fortune Business Insights report, and is expected to grow at a 38.8 percent compound annual growth rate to $209.91 billion by 2029.
All this means that AI and machine learning technologies present a major opportunity for solution providers and strategic service providers. As part of CRN’s Emerging Vendors for 2022, here are 11 AI and machine learning startups, founded in 2016 or later, that solution providers should be aware of.
Top Executive: Bindu Reddy, CEO
Abacus.AI launched its MLOps platform in 2021, offering an autonomous, end-to-end system for training and deploying custom, deep learning models. The platform supports streaming pipelines, data wrangling, model monitoring and drift tracking, online and batch predictions, and other capabilities.
In October, the San Francisco-based company raised $50 million in a Series C round of funding.
Top Executive: Liran Hason, Founder, CEO
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 Tel Aviv, Israel-based startup is using the financing to triple its head count through early 2023, expand its presence in the U.S., and increase the range of use cases addressed by its technology.
Top Executive: Rohit Gupta, Co-Founder, CEO
Auditoria provides AI-driven SaaS automation applications for corporate finance back-office operations including vendor management, accounts payable/receivable, and record-to-report to accelerate cash performance.
By leveraging natural language processing, AI and machine learning, Auditoria SmartBots remove friction and repetition from mundane tasks while automating complex functions, providing real-time cash performance visibility.
In March Auditoria, based in San Jose, Calif., said more than 130 global companies were using its software. The company also recently raised $20 million in Series A financing.
Top Executive: Gideon Mendels, Co-Founder, CEO
The Comet platform provides data scientists and data science teams with the ability to manage and optimize the entire machine learning life cycle from building and training models, experiment tracking, and model production monitoring—all resulting in improved visibility, collaboration and productivity.
Comet raised $50 million in Series B funding in November 2021. At the time the New York-based company said that its annual recurring revenue had grown by a factor of five, it had tripled its global workforce and its customer base included Ancestry, Etsy, Uber and Zappos.
Top Executive: Ryohei Fujimaki, Co-Founder, CEO
DotData’s software provides automated feature engineering and enterprise AI automation for building AI and machine learning models. Feature engineering is the critical step in machine learning development of finding important patterns hidden within data used to develop and train ML models.
In addition to the company’s flagship dotData Enterprise predictive analytics automation software, the company offers related products including the dotData Cloud AI automation platform, dotData Py and dotData Py Lite tools, and dotData Stream for real-time AI models.
DotData, founded in 2018 as a spinoff of NEC, raised $31.6 million in Series B funding in April, bringing the company’s total financing to $74.6 million. The company, based in San Mateo, Calif., has been using the additional funding largely to accelerate product development.
Top Executive: Dmitry Petrov, Co-Founder, CEO
Iterative is an MLOps startup that builds developer tools for machine learning with the goal of streamlining data scientists’ workflows by solving the complexity of managing datasets, ML infrastructure and ML model life cycles.
The company integrates ML workflows into business practices for software development instead of creating separate AI platforms. And the company’s cloud-agnostic approach removes the need for cloud providers’ proprietary AI platforms.
Iterative, based in San Francisco, raised $20 million in Series A funding in June 2021.
Top Executive: Luis Ceze, Co-Founder, CEO
OctoML’s Software-as-a-Service Octomizer makes it easier for businesses 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.
In June OctoML unveiled OctoML CLI, a command line interface that automates model containerization and acceleration—what the Seattle-based company says are the most difficult tasks when deploying deep learning models.
Top Executive: Alexander Ratner, Co-Founder, CEO
Snorkel 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, accelerates 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, based in Redwood City, Calif., 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: Serkan Piantino, Co-Founder, CEO
The Spell DLOps platform is used to develop, train, deploy and manage machine learning and deep learning models and run machine learning experiments at scale.
New York-based Spell was just acquired in June by news aggregation, discussion and online community website Reddit. Reddit plans to use the Spell technology to enhance the machine learning capabilities of the Reddit platform.
Top Executive: Jim Rebesco, CEO
Striveworks is a pioneer in operational data science for national security and highly regulated industries. The Austin, Texas-based company said its platform is purpose-built to enable engineers and business professionals to transform data into actionable insight.
Chariot, Striveworks‘ flagship MLOps platform, is used to rapidly build and deploy models at scale and track governance and lineage with data, models and inferences.
Top Executive: Michael Del Balso, Co-Founder, CEO
Tecton develops a machine learning feature store platform that the company said can speed the deployment of machine learning applications from months to minutes. The company’s technology automates the transformation of raw data, generates training data sets and serves up features for online inference at scale.
Tecton, based in San Francisco, was founded by the developers who created Uber’s Michelangelo machine learning platform. The company exited stealth in April 2020.
On July 12 Tecton said it raised $100 million in Series C funding, bringing its total financing to $160 million. The company also said that its annual recurring revenue nearly tripled from fiscal 2021 to fiscal 2022 and that ARR growth accelerated to more than 180 percent in the fiscal quarter ended April 2022. Tecton also said that its customer base increased five-fold during the previous 12 months.