17 Machine Learning Startups To Watch In 2024

In the last ten years, machine learning has transitioned from a niche academic discipline to a foundation of modern technology. It is estimated that over 463 exabytes of data will be created daily by 2025, and this is driving the need for advanced data processing and machine learning solutions. 

According to the Precedence Research report, the global machine learning market size will exceed $771.3 billion by 2032, growing at a CAGR of 35.09%. Asia Pacific is projected to expand at the fastest rate in the coming years. [1]

The proliferation of open-source machine learning tools like TensorFlow and PyTorch has already lowered the barriers to entry. Startups can now build on top of these robust tools, accelerating their development cycles and focusing on innovation. 

We have featured thriving machine learning startups that are not just advancing technology but creating new markets and solving the world’s most pressing problems. 

Did you know?

AI and machine learning startups received $22.3 billion in venture capital funding in the fourth quarter of 2023, up from $21.1 billion in the third quarter of 2023. [2]

17. MosaicML 

Founded in 2021
Location: California, United States
Total Funding: $37 million
Growth Status: Explosive 

MosaicML develops algorithms that accelerate the machine learning training and scalability process. It aims to democratize AI by decreasing the cost and time required to train complex models, making AI accessible to a broader range of businesses and industries. 

In July 2023, Databricks announced plans to acquire MosaicML for $1.3 billion. Before this acquisition, MosaicML had raised $37 million through two funding rounds.

During the same year, MosaicML published a paper introducing a novel approach that allows users to train a large language model from scratch for less than $100. [3]

In March 2024, they released a new LLM model called DBRX. This open-source model utilizes 132 billion parameters to improve efficiency. It outperforms open-source models (like xAI’s Grok and Meta’s LLaMA 2) and closed-sourced models (like GPT-3.5) in multiple benchmarks, including mathematics, language understanding, and programming ability. [4]

16. Kili Technology

Founded in 2018
Location: Paris, France
Total Funding: $31.9 million
Growth Status: Accelerated

Kili Technology provides an all-in-one data annotation platform that supports various data types, including audio and video. It facilitates collaboration among teams, offering tools for task assignment, project management, and quality control. 

This versatility makes it easy for data scientists and machine learning practitioners to obtain the high-quality labeled data they need to train robust AI systems. 

The company has raised $31.9 million from 10 investors. In 2021, they closed the Series A round, raising $25 million from London-based venture capital firm Balderton Capital. [5]

In 2024, they introduced a new AI tool, Davinci, that drafts patents and office action answers in much less time than manual methods require. [6]

15. Jina AI

Founded in 2020
Location: Berlin, Germany
Total Funding: $37.5 million
Growth Status: Accelerated

Jina develops open-source neural search solutions. It empowers developers and businesses to create advanced search capabilities that can process and understand unstructured data. 

They utilized deep learning models to enhance conventional search methods with capabilities like semantic understanding and contextual relevance. This leads to more accurate and intuitive search experiences for users. 

In 2023, Jina released a second-generation text embedding model called “jina-embeddings-v2.” This model features a context length of 8,192 tokens, a major achievement that places it in direct competition with OpenAI’s proprietary model, “text-embedding-ada-002.” [7]

14. Elementus

Founded in 2017
Location: New York, United States
Total Funding: $26.3 million
Growth Status: Accelerated

Elementus provides comprehensive insights and analytics for blockchain transactions. It serves financial institutions and regulators that need to navigate the complexities of blockchain data. 

More specifically, it helps financial institutions understand the flow of funds, identify patterns, and gain actionable insights from blockchain data. It also assists in meeting AML (Anti-Money Laundering) and KYC (Know Your Customer) requirements by offering robust compliance tools.

The company has raised $26.3 million through five funding rounds, with the latest one (Series A) occurring in 2023. In this round, they raised $10 million at a valuation of $160 million, up from their previous valuation of $52 million. [8]

13. NanoNets

Founded in 2017
Location: California, United States
Total Funding: $42 million
Growth Status: Explosive

NanoNets streamlines the process of extracting, organizing, and analyzing data from documents like invoices, receipts, forms, and contracts. Their vision is to become a go-to solution for intelligent document processing, providing scalable and easy-to-use tools that revolutionize how businesses handle their data.

They offer various pre-trained machine learning models, including OCR, object detection, image classification, image segmentation, and tagging. These models enhance productivity and streamline business operations without the need for complicated databases.

In 2024, the company raised $29.3 million in a Series B round from Accel, YCombinator, and Elevation Capital, bringing the total funding raised to $40.5 million. [9]

12. MindBridge

Founded in 2015
Location: Ontario, Canada
Total Funding: $102.3 million
Growth Status: Rapid Expansion

MindBridge leverages machine learning algorithms to uncover insights and detect anomalies in financial data. It provides detailed risk scoring for financial transactions, highlighting areas that require further investigation. 

Their platform compares data across more than 40 capabilities to identify the level of risk. This helps financial professionals prioritize their efforts in high-risk areas and improve the efficiency of audits and risk assessments. 

In 2024, the company released a Global Partner Program that offers product integration and go-to-market resources, allowing users to seamlessly integrate MindBridge’s risk intelligence engine into their existing services. 

So far, they have raised a total of $102.3 million through four funding rounds, with the latest one occurring in July 2023. In 2023, they processed over 100 billion financial entries and achieved their highest revenue results, significantly expanding their customer base and acquiring new Fortune 100 customers. [10]

11. Abacus.AI

Founded in 2019
Location: California, United States
Total Funding: $90.3 million
Growth Status: Steady

Abacus.AI simplifies the process of developing, training, and deploying AI models, making advanced AI accessible to businesses of all sizes. Its comprehensive platform covers the entire AI lifecycle, from data preparation and model training to deployment and monitoring. 

It has a library of pre-built models for common business use cases, such as demand forecasting, churn prediction, and recommendation systems. Users can also customize these models as per their specific requirements. 

In 2024, Abacus.AI unveiled the Smaug-Llama-3-70B-Instruct model, which completes directly with other popular open-source models like GPT-4 Turbo. On MT-Bench, this model scored an average of 9.2, outperforming GPT-4 Turbo and Llama-3 70B, which scored 9.18 and 9.2, respectively. [11]

To date, Abacus has raised $90.3 million through four rounds from 17 investors, including Index Ventures and Tiger Global Management. [12]

10. Seldon

Founded in 2014
Location: London, United Kingdom
Total Funding: $33.7 million
Growth Status: Accelerated

Seldon helps organizations deploy machine learning models on Kubernetes. Its open-source platform, Seldon Core, provides essential capabilities like versioning, scaling, and monitoring, making it easier to manage ML models in production environments. 

Seldon has been used to deploy and manage over 10 million machine learning models, handling more than 100,000 active nodes. According to their official website, businesses using Seldon’s platform experience an average 85% increase in productivity when deploying ML models.

In 2020, Seldon raised $7.7 million in a Series A funding round from AlbionVC and Cambridge Innovation Capital. Since then, they have achieved 400% year-on-year growth. In 2023, they raised $20 million in a Series B round led by Bright Pixel. [13]

9. Anyscale

Founded in 2019
Location: California, United States
Total Funding: $259 million
Growth Status: Rapid expansion

Anyscale builds on Ray, an open-source framework for distributed computing. It provides developers with additional tools and services to deploy, manage, and monitor Python applications. This includes capabilities for orchestrating, scaling, and optimizing ML models and AI apps. 

With Anyscale, developers can scale their applications from a single machine to a cluster of thousands of nodes with minimal code changes. As per their official website, the platform can scale applications to 1000 nodes in just 60 seconds, ensuring rapid response to changing demands. [14]

So far, they have raised $259 million through four funding rounds, with the latest one (Series C round) occurring in 2022.

In 2024, they partnered with NVIDIA to scale generative AI models into production. This partnership simplifies the deployment and management of distributed ML applications, leading to faster iteration, efficient resource utilization, and reduced costs. [15]

8. Aidoc

Founded in 2016
Location: Tel Aviv, Israel
Total Funding: $264 million
Growth Status: Explosive

Aidoc develops machine learning algorithms to enhance radiology workflows and improve patient outcomes. These algorithms analyze medical images such as X-rays, MRI scans, and CT scans in real-time, detecting abnormalities and prioritizing critical cases for radiologists. 

Aidoc’s technology seamlessly integrates with existing radiology workflows and Picture Archiving and Communication Systems (PACS). The company claims that have saved more than 83 million minutes in diagnostic turnaround times, reduced the duration of patient emergency department visits by an average of 59 minutes, and enabled healthcare providers to add $1 million in revenue. 

They have also received several FDA 510(k) clearances, including one for Aidoc’s algorithm for flagging pneumothorax (or a collapsed lung) on X-rays, and another for AI software aimed at detecting triaging brain aneurysms in CT scans. [16]

So far, Aidoc has raised $264 million through nine funding rounds. In 2023, they raised $30 million to develop first-of-its-kind AI models aimed at improving diagnostic precision and the early detection of medical conditions. [17]

7. Cohere

Founded in 2019
Location: Ontario, Canada
Total Funding: $434.9 million
Growth Status: Rapid expansion

Cohere develops Large-scale language models that understand and generate human language with exceptional fluency and accuracy. These models can handle a range of NLP tasks, including text generation, sentiment analysis, and translation. 

In 2024, the company released the Aya-23 family model to enhance multilingual capabilities. This includes two models featuring 8 billion and 35 billion parameters, making them one of the largest and most powerful multilingual models available. [18]

In terms of financial backing, Cohere has raised $434.9 million from 20 investors, including NVIDIA, SAP, Mirae Asset, Thomvest Ventures, and Schroders Capital.

6. V7 Labs

Founded in 2018
Location: London, United Kingdom
Total Funding: $43 million
Growth Status: Accelerated

V7 Labs specializes in AI-driven image and video annotation. Its proprietary platform allows users to automate and enhance the process of labeling and annotating visual data. 

This platform is already being used by a wide range of customers, from a biotech firm that uses it to mine information from research journals to an asset management company that uses it to extract data from confidential memos. 

The company employs 80 people and has raised a total of $43 million through three funding rounds. In 2022, they completed a Series A round, raising $33 million from Radical Ventures and Temasek. Other investors include Partech, Amadeus Capital Partners, and Air Street Capital. [19]

5. Explorium

Founded in 2017
Location: Tel Aviv, Israel
Total Funding: $127 million
Growth Status: Accelerated

Explorium leverages AI and machine learning models to help businesses find and integrate external data sources to improve their predictive analytics and business insights. Its platform automatically discovers relevant external data sources that can augment a business’s existing datasets. 

The platform integrates over 4,000 data signals to enrich internal datasets. It has processed more than 190 million company records and 150 million professional emails. [20]

In 2021, they closed a Series C round, raising $75 million just 10 months after announcing a $31 million Series B. This brings their total funding to $127 million. Major investors include Zeev Ventures, Emerge, and F2 Capital. [21]

4. BigID

Founded in 2016
Location: New York, United States
Total Funding: $306.1 million
Growth Status: Explosive

BigID utilizes AI and machine learning to help organizations detect, manage, and secure their sensitive data and comply with various data privacy regulations, such as GDPR, CCPA, and HIPAA. 

Their latest data discovery and intelligence platform supports hybrid scanning for cloud-native workloads. It combines direct-scanning and side-scanning methods for efficient data discovery, management, and protection. 

In the last five years, BigID has grown from almost zero to $100 million in recurring revenue, helping businesses meet their expanding data security, privacy, compliance, and governance needs in the hybrid cloud. [22]

In 2021, Inc 5000 ranked it the 19th fastest-growing private company in America. In 2024, it closed a Series E funding round, raising $60 million from Riverwood Capital, Advent International, and Silver Lake Waterman. 

3. Syntiant

Founded in 2017
Location: California, United States
Total Funding: $126.4 million
Growth Status: Accelerated

Syntiant develops ultra-low-power neural processors for edge AI applications. These processors enable AI functionalities directly on the device, reducing the need for cloud connectivity and ensuring faster, more reliable performance. 

Their technology is used in various applications, including keyword detection, sound recognition, voice control, sensor fusion, and beyond, enabling devices to respond smartly to their environments. 

In 2024, they released the third generation of their AI chip, the NDP250. This chip supports models with up to 6 million parameters (8-bit) and can deliver 30 giga-operations per second (GOPS) of compute performance. [23]

To date, the company has raised $126.4 million through seven funding rounds, with the latest one (Series D) occurring in 2022. It is backed by 20 investors, including Millennium Technology Value Partners, Renesas Electronics Corporation, Intel Capital, and M12, Microsoft’s Venture Fund.

2. Graphcore

Founded in 2016
Location: Bristol, United Kingdom
Total Funding: $682 million
Growth Status: Steady

Graphcore develops AI hardware and software to accelerate artificial intelligence and machine learning applications. Specifically, they develop Intelligence Processing Units (IPUs) that offer unparalleled performance and efficiency for AI workloads. 

The latest generation of IPU delivers up to 350 TeraFlops of AI compute, a 40% increase in performance and 16% more power efficient compared to the previous generation. [24]

They have also developed the Poplar software stack to support IPU hardware. Poplar seamlessly integrates with existing machine learning frameworks, making it easier for developers to leverage the power of IPUs.

The company has raised $682 million through seven funding rounds and is backed by 32 investors, including Bosch Ventures, Amadeus Capital Partners, Samsung Strategy and Innovation Center, and M&G Investments.

Graphcore directly competes with Nvidia and Intel. However, they have not yet become profitable. In 2022, they reported pre-tax losses of $204.6 million. The financial performance in 2023 was also challenging, and the company needs to raise more cash to break even. They are currently in talks with investors but have not finalized any agreements. [25]

1. Quantexa

Founded in 2016
Location: London, United Kingdom
Total Funding: $370 million
Growth Status: Explosive

Quantexa provides advanced data analytics and decision intelligence solutions through its Contextual Decision Intelligence (CDI) platform. Their primary customers include banks, insurance firms, and government agencies.

The CDI platform accurately identifies and links entities (such as customers, companies, or transactions) across disparate data sources. It utilizes machine learning models and network analytics to analyze and visualize interactions between entities, helping businesses detect fraud, money laundering, and other illicit activities. 

The company has raised approximately $370 million through eight funding rounds. In 2023, they closed a Series E round, raising $129 million at a valuation of $1.8 billion. [26]

In 2024, Quantexa partnered with Microsoft to make their services available on the Microsoft Azure Marketplace. [27]

Read More

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Sources Cited and Additional References

  1. Industry Analysis, Machine learning market by type, deployment, and end-user, Precedence Research
  2. News and Analysis, AI and ML report Q4 2023, PitchBook
  3. Nikhil Sardana, Beyond Chinchilla-Optimal: Accounting for inference in language model scaling laws, arXiv
  4. Will Knight, Inside the creation of the world’s most powerful open source AI model, Wired
  5. Deals, Kili Technology raises $25m in Series A funding round, Private Equity Wire
  6. Christy Burke, Kili technology launches DaVinci patent ai copilot, which helps attorneys draft better patents & office action answers in half the time, Businesswire
  7. Anthony Alford, Jina AI’s open-source embedding model outperforms OpenAI’s Ada, InfoQ
  8. Ben Weiss, Blockchain analyst firm Elementus raises funds at $160M valuation. Clients include Celsius and BlockFi creditors, Fortune
  9. Bengaluru Bureau, Workflow automation platform Nanonets raises $29 million in Series B round, BusinessLine
  10. Mindbridge Analytics, Mindbridge marks 2023 with significant investment, new executives, strong top-line results, and over 100 billion financial entries scored, PR Newswire
  11. Matthew Berman, New LLaMA 3 fine-tuned – Smaug 70b dominates benchmarks, YouTube
  12. Company Highlights, Abacus.AI Financials, Crunchbase
  13. Mike Butcher, MLOps platform Seldon raises $20M Series B to improve the productions of AI models, TechCrunch
  14. Homepage, Optimized performance and total control over costs, Anyscale
  15. News Release, Anyscale partners with Nvidia to scale generative AI models into production, Datanami
  16. News Release, Aidoc expands AI service to x-ray, receiving FDA 510(k) clearance for pneumothorax, Aidoc
  17. Jessica Hagen, AI-enabled imaging company Aidoc raises $30M, Mobi Health News
  18. Shubham Sharma, Cohere launches open weights AI model Aya 23 with support for nearly two dozen languages, VentureBeat
  19. Company Highlights, V7 Financials, Crunchbase
  20. Data Catalog, Connect to thousands of relevant external data signals instantly, Explorium
  21. Press Release, Explorium closes $75m Series C amid soaring demand for external data, Explorium
  22. BigID, AI security market fuels a $60m growth round for unicorn BigID to accelerate AI data security innovation and power acquisitions, PR Newswire
  23. Sally Ward-Foxton, Syntiant pitches latest low-power AI chip as LLM companion, EE Times
  24. B‍ow IPU Processor, Hardware designed for machine intelligence, Graphcore
  25. Max Cherney, Losses widen, cash needed at chip startup Graphcore, an Nvidia rival, filing shows, Reuters
  26. Ingrid Lunden, Quantexa raises $129M at a $1.8B valuation to help navigate online fraud and customer data management, TechCrunch
  27. Blathnaid O’Dea, UK unicorn Quantexa makes a big deal with Microsoft, SiliconRepublic
Written by
Varun Kumar

I am a professional technology and business research analyst with more than a decade of experience in the field. My main areas of expertise include software technologies, business strategies, competitive analysis, and staying up-to-date with market trends.

I hold a Master's degree in computer science from GGSIPU University. If you'd like to learn more about my latest projects and insights, please don't hesitate to reach out to me via email at [email protected].

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