Machine learning (ML) is one of the hottest and most lucrative tech trends. According to a survey on the state of AI conducted by McKinsey in 2021, 67 percent of companies that adopted AI-related technologies saw increases in revenue. Increased adoption of ML technology has given rise to some of the best machine learning startups, all of which are leading the digital transformation in the 21st Century.
These machine learning startup companies are located around the globe, including San Francisco, Santa Clara, San Jose, San Mateo, Redwood City, and the rest of Silicon Valley, as well as places like London and Tel Aviv. This article will explore exciting startups in the private sector and public sector, looking at their innovative ideas, funding, and expected growth.
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What Are Machine Learning Startup Companies?
Machine learning startups are small companies that create products or services based on ML technology. As a subfield of artificial intelligence, machine learning focuses on programming computers and machinery to learn from experience without human intervention and direction.
Machine learning is used for multiple purposes, such as pattern recognition, voice recognition, financial services and transactions monitoring, personalized web recommendations, and more.
Top Machine Learning Companies That Began as Startups
The companies in the following list have experienced exponential growth since the beginning and are now consolidated tech giant companies. They all use machine learning to produce their products or services. DataRobot, for example, has made it all the way to the series G funding level, which is in the eighth step of the investment funding process.
Big Machine Learning Companies
- InData Labs
The Best Machine Learning Startup Companies
|Company Name||Location||Funding||Number of Employees|
|Alation||Redwood City, California||$2.17M||500 – 1,000 (2022)|
|Algorithmia||Seattle, Washington||$38.1M||11 – 50 (2021)|
|Augury||New York, New York||$180M||100 (2021)|
|Avora||London, UK||$9.7M||51 – 200 (2021)|
|Boast.ai||San Francisco, California||$100M||51 – 200 (2022)|
|ClosedLoop||Austin, Texas||$34M||51 – 200 (2021)|
|Cognino.ai||London, UK||$2M||11 – 50 (2021)|
|Databand||Tel Aviv, Israel||$14.5M||11 – 50 (2020)|
|DataVisor||Mountain View, California||$54.5M||101 – 250 (2021)|
|Netra||Boston, Massachusetts||$3.9M||2 – 10 (2021)|
The Top Machine Learning Startups: A Closer Look
- Founded: 2012
- Funding: $2.17M
Alation is a promising AI startup that helps other businesses interpret data with human insight while utilizing advanced analytics and AI technology. Its system is an autonomous analytics platform with an intuitive design, and it is used for managing metadata challenges, discovering data, and performing data governance.
Big companies like Cisco, Pfizer, and Scandinavian Airlines use Alation’s machine learning tools. The exciting startup is especially popular among financial services companies.
- Founded: 2014
- Funding: $38.1M
Algorithmia is an exciting startup focused on machine learning models for cyber security. It helps potential customers optimize the ML operations process by providing security and governance. Companies can put machine learning models into production cost-effectively.
Algorithmia has been used by all types of organizations, from European tech companies to American intelligence agencies in the public sector.
- Founded: 2011
- Funding: $180M
Augury is an ML startup that uses this technology to diagnose potential issues in all types of machinery, specifically in the healthcare industry. Augury’s system combines AI-based techniques with ML to provide healthcare providers with insights into machinery conditions, and to identify malfunctions before they occur.
- Founded: 2014
- Funding: $9.7M
Avora simplifies the data analytics process for non-technical users. Businesses can easily find and interpret data using ML models and automation technologies. This software is used for marketing, financial services, supply chain logistics, and advertising to help improve customer experience. This augmented analytics software makes insights available in a matter of minutes.
- Founded: 2017
- Funding: $100M
Boast is a San Francisco-based machine learning startup and service used in financial institutions to improve insights and better analyze data. This software allows accountants and other financial professionals to gain and maximize R&D tax credits by using artificial intelligence-powered ML tools.
- Founded: 2017
- Funding: $34M
ClosedLoop has created a system to improve the healthcare industry and patient experiences with a predictive analytics approach. Among this system’s multiple predictive uses, it helps healthcare organizations reduce risks and increase profitability by utilizing machine learning for data analytics insights. The predictive model software can predict no-show appointments, track chronic disease progression, and optimize the clinical documentation process.
- Founded: 2018
- Funding: $2M
This startup company’s flagship product is an ML program that helps companies prepare and connect data from large sets of unstructured data to simplify the research and analytics process. Companies can improve their decision-making outcomes by using deep learning, search engines, and data recommendations to achieve a human-like conceptual understanding.
- Founded: 2018
- Funding: $14.5M
Databand is a Tel Aviv-based data monitoring platform that detects data errors before they arise, and works to standardize the gap between older data analytics software and ML technology. This tool also provides a bird’s-eye view of data flows, which ensures that data pipelines are successfully completed. It is compatible with tools like Apache Airflow, and is used in all sectors.
- Founded: 2013
- Funding: $54.5M
Mountain View-based DataVisor is a predictive analysis ML startup that provides cyber security technology to prevent fraudulent activity. With DataVisor, companies can detect potential financial application and payment frauds. Organizations can even prevent and proactively defeat unknown threats in real time, reducing financial risks and improving operational savings.
- Founded: 2013
- Funding: $3.9M
Netra is a Boston-based startup that uses ML facial and image recognition technology to analyze video content for contextual and security purposes. It utilizes imagery scanning to provide text metadata interpretation, and to detect and prevent potentially unsafe content, cyber security threats, and reputational damages.
Benefits of Working at a Machine Learning Startup
Choosing to work at an ML or artificial intelligence startup over a major tech giant may sound like a risky decision, but it can be a great way to bootstrap your career as a machine learning engineer, developer, or analyst.
Top 5 Reasons to Work at a Machine Learning Startup
- A great learning experience. One of the major benefits of working at an ML startup is that it is a learning opportunity. A startup is in its initial stages, so everyone is discovering new paths and strategies together. On your journey in the company, you’ll be learning these exciting new techniques first-hand.
- Flexibility. Another significant trend of many machine learning startups is that they value work-life balance. Take Augury, for example, which espouses the philosophy that work is a part of life, not your entire life. Remote working is also common in the tech industry, so you may be able to work from home.
- More responsibility than large companies. Startups have small teams, which means each team member has more responsibilities than they would working for a large corporation. You’ll be a crucial member of the company with autonomy, as well as a key contributing factor in its success.
- Professional growth. Imagine that you started working a crucial role on a small team at a machine learning startup, and the company started to see exponential growth in its first years. You will bootstrap your career as an ML specialist, and experience the excitement of helping a small company grow.
- Creative juices. A startup team is constantly learning as it builds impactful technology for other companies. As such, there will always be moments when you’ll have to think outside of the box to provide solutions for unique challenges. This will increase your logical thinking, creativity, and analytical skills.
Can a Coding Bootcamp Help Me Get a Job at a Machine Learning Startup?
A coding bootcamp can help you secure a job at an ML startup, and it is a great place to start your career in the machine learning industry. There are numerous coding bootcamps that teach you in-demand ML skills in a short amount of time. You won’t have to go through a four-year bachelor’s degree program, and bootcamps are much more affordable.
Some coding bootcamps have partnered with leading tech giants to help students receive necessary hands-on training and land a job more easily, so attending a bootcamp can be a great opportunity to get a job at one of the best machine learning startups.
Top Bootcamps With Machine Learning Programs
- Springboard. This bootcamp has a machine learning course that will teach you about deep learning models, linear regression, machine learning models, and more. Springboard also features a mentorship program, so you can learn from ML experts.
- Galvanize. Galvanize’s data science course dives into fundamental machine learning techniques like natural language processing, Python, and data analysis.
- Flatiron School. Its immersive data science course will give you the core skills you need to master machine learning, including SQL and Python programming.
- General Assembly. This bootcamp has two courses that are great for your ML journey, and they are the software engineering and data science programs. You will learn about Python, data analysis, and statistical modeling.
- BrainStation. BrainStation’s machine learning certificate course will teach you how to make data-driven decisions, and you’ll be able to put the certificate on your resume as proof of ML skills.
Common Jobs in Machine Learning
- Machine learning engineer: A machine learning engineer develops algorithms that allow computers to learn from data. To become an ML engineer, you need to know how to identify similarities, patterns, and differences in large data sets. Engineers can also build ML-driven apps.
- Data scientist: A data scientist collects, filters, and analyzes datasets to discover actionable insights. Their role is to help organizations make rational, data-backed business decisions.
- Business intelligence engineer: This technology professional’s responsibility is to analyze data to evaluate business trends and patterns, forecast events, and help companies make better business decisions.
Should I Work For a Machine Learning Startup Company?
Yes, you should work for a machine learning startup company, especially because it’s a huge learning and growth opportunity for your career. You’ll be taking on more responsibility and have a larger role in the company’s success, which can quickly lead to career advancement.
Best Machine Learning Startups FAQ
Before joining a machine learning startup, you should consider that it can be a chaotic work environment. You should evaluate whether a high-paced, collaborative job is right for you. Some of these companies haven’t figured out their best corporate structure and strategies yet, so you’ll be learning with them along the way.
According to the Bureau of Labor Statistics (BLS), the median salary of a computer information research scientist in 2020 was $131,490. Under BLS’s employment classification scheme, machine learning engineers are among the professionals included in the computer information and research scientist category.
Some of the best, world-leading machine learning startups are located in San Francisco, New York, Seattle, London, and Tel Aviv.
Yes, machine learning startups usually pay employees well. According to BLS’s analysis of a similar career path, openings for computer and information research scientists are expected to increase by 22 percent over the next decade. These competitive and high-demand jobs typically enjoy nice salaries and benefits, even at startups.
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