The 10 Hottest Data Science And Machine Learning Startups Of 2023 (So Far)

Here’s a look at 10 technology startups in data science and machine learning that solution providers should be aware of.

Steep Learning Curve

Machine learning systems, a segment of the red-hot AI technology arena, make business-outcome decisions and predictions based on algorithms and statistical models that analyze and draw inferences from huge amounts of data.

Machine learning technology is in huge demand as businesses and organizations look to automate a broad range of business processes including product recommendations, customer service and support, fraud detection, image recognition and classification, human resources management, self-driving cars and even medical diagnostics.

Data science combines math and statistics, advanced analytics, specialized programming and other skills and tools to help uncover actionable insight within an organization’s data, according to a definition developed by IBM.

Demand for data science and machine learning technologies is being driven by the exploding volume of data created, captured, replicated and consumed by businesses today, growing more than 20 percent every year, according to market researcher IDC.

The global market for machine learning technology reached $19.2 billion in 2022 and is expected to grow to $26.03 billion this year and reach $225.91 billion by 2030, according to Fortune Business Insights—a CAGR of 36.2 percent. The global data science platform market, meanwhile, is expected to grow from $81.47 billion in 2022 to $484.17 billion by 2029 for a CAGR of 29 percent, according to a report from the same researcher.

All this is fueling a wave of innovative startups and young companies developing leading-edge data science and machine learning tools and platforms. What follows is a look at 10 hot data science and machine learning startups that are meeting today’s machine learning and data science challenges.


Co-Founder, CEO Liran Hason

Aporia’s namesake observability platform is used by data scientists and machine learning engineers to monitor and improve machine learning models in production.

The platform provides visibility into model behavior and performance, making it easier to identify and diagnose issues that may arise in production environments and gain insight to improve models, according to the company.

Founded in 2019 and based in Tel Aviv, Israel, Aporia has raised $30 million in funding, including $25 million in Series A funding in February 2022 from Tiger Global, Samsung Next, TLV Partners and Vertex Ventures.

In April Aporia achieved compliance with HIPAA, allowing health-care organizations and their customers to use the Aporia machine learning services.


Co-Founder, CEO Tuhin Srivastava

The critical step of integrating machine learning models with real-world business processes is generally a lengthy, expensive process. Baseten’s cloud-based machine learning infrastructure makes going from machine learning model to production-grade applications fast and easy, according to the company.

The Baseten serverless technology works by giving data science and machine learning teams the ability to incorporate machine learning into business processes without back-end, front-end or MLOps knowledge.

In April 2022 the San Francisco-based company, founded in 2019, said it had raised a total of $20 million in seed and Series A funding.

Co-Founder, CEO Andrew Eye

A rising star in the health-care IT space, provides a data science platform and prebuilt content library for building, deploying and maintaining predictive applications used by health-care providers and payers.

The platform is used to develop AI/machine learning-based applications that provide predictive analytical capabilities in such areas as hospital readmissions, avoidable emergency department utilization, drug safety, and hospital-acquired conditions and infections. It even supports predictive clinical use-case applications such as early diagnosis for chronic kidney disease, diabetes risk, and maternal health and obstetric outcomes., founded in 2017 and based in Austin, Texas, raised $34 million in 2021 in a Series B funding round led by Telstra Ventures with participation from Breyer Capital, Greycroft Ventures, .406 Ventures and Healthfirst.


Founder, CEO Matt Rocklin

Coiled offers Coiled Cloud, a Software-as-a-Service platform for developing and scaling Python-based data science, machine learning and AI workflows in the cloud.

Coiled is built on Dask, the open-source Python library for parallel computing and data science that was also developed by CEO Rocklin. Coiled is the commercial entity designed to help Dask developers scale up their Dask deployments.

Founded in 2020 and based in New York, Coiled has raised $26 million in Series A funding.


Co-Founder, CEO Barry McCardel

Hex markets a data science and analytics collaboration platform that creates a modern data workspace where data scientists and analysts can connect with data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data applications and stories.

The Hex platform provides real-time collaboration, SQL support, no-code charts, and a reactive, graph-based compute engine to publish projects as interactive data applications. That’s a change from the siloed BI tools, code notebooks and spreadsheets that data scientists and analysts traditionally use to do their work.

Hex, founded in 2019 and based in San Francisco, raised $52 million in March in a Series B funding round led by Andreessen Horowitz and included Snowflake and Databricks as investors.


Co-Founder, CEO Jorge Torres

MIndsDB says its mission is to “democratize machine learning” with its open-source infrastructure that the company says enables developers to quickly integrate machine learning capabilities into applications and connect any data source with any AI framework.

At the core of the company’s technology is an open-source AI layer for existing databases—a cloud for serving AI logic—that allows programmers to develop, train and deploy state-of-the-art machine learning models using SQL queries. That makes it possible for organizations to quickly move AI-powered applications from prototyping and experimentation to production.

MindsDB was founded in 2017 and is based in San Francisco. On June 1 the company said it had raised an additional $25 million in capital from new lead investor Mayfield, along with participation from TQ Ventures and existing investors, bringing its total funding to $50 million.


Co-Founder, CEO Piero Molino

Predibase announced the general availability of its low-code, declarative machine learning platform for developers on May 31 after undergoing nearly a year of beta testing at a number of Fortune 500 companies.

San Francisco-based Predibase was founded in 2020 by CEO Molino and CTO Travis Addair who worked on the Ludwig and Horovod machine learning projects at Uber. The company emerged from stealth in May 2022 with $16.25 million in a funding round led by Greylock.

The Predibase software lets data scientists and nonexperts quickly develop sophisticated machine learning-powered AI applications with “best-of-breed” machine learning infrastructure, according to the company. Predibase offers its platform as an alternative to traditional AutoML approaches to developing machine learning models for real-world problems.

The GA release includes privately hosted, customized large language models that allow developers to build their own GPT. It also includes Data Science Copilot, which provides developers with expert recommendations on how to improve the performance of their models as they iterate.


Founder, CEO Alex Housley

Seldon provides a data-centric machine learning operations (MLOps) platform for deploying, managing, monitoring and explaining machine learning models.

The Seldon platform bridges the gap between data science and DevOps teams, according to the company, empowering data scientists and machine learning engineers to scale out machine learning models and overcome bottlenecks in team workflows, regulation and compliance constraints, and other hurdles.

Seldon, based in London, U.K., raised $20 million in March in a Series B funding round led by investor Bright Pixel.


Co-Founder, CEO Yevgeniy Vahlis

Shakudo is building what it calls an “operating system for data stacks” with the goal of empowering data scientists, data engineers and machine learning teams by eliminating the complexity of managing big data stacks.

With its comprehensive single user interface and automated DevOps capabilities, the Shakudo platform supports data stack components including data integration, data streaming, data catalog, data quality and data warehouse systems.

Shakudo helps organizations manage data systems and monitor costs across various tools and use cases including data engineering, data analytics and visualization, serving data applications and pipelines, training and serving machine learning models, and connecting to data storage and data warehouse systems.

Founded in 2021, Toronto-based Shakudo said on July 5 that it had raised $7.2 million in a Series A funding round led by GreatPoint Ventures.


Co-Founder, CEO Jim Rebesco

Striveworks develops machine learning operations (MLOps) technology for enterprise data science and data analytics teams. The company’s Chariot MLOps platform enables organizations to deploy AI/ML models at scale while maintaining full audit and remediation capabilities.

The Chariot MLOps platform is most prominently used by national security agencies and in other highly regulated sectors, according to the company.

Striveworks was founded in 2018 and is based in Austin, Texas. On June 13 the company said it had raised $33 million in its first institutional funding round, an all-equity round led by Centana Growth Partners.