5 Machine Learning (ML) Startups to Watch in Q3 & Q4 2021
| 5 minutes read
Artificial intelligence (AI) has been a hot gossip and an area where every company wants to improve and implement its business or the products they offer. In recent years, Machine Learning (ML), a subsection of AI, is one of the most important AI arena segments.
Machine learning is developing intelligent algorithms and statistical models that develop software through the experience over time without the need to code those improvements explicitly. A predictive analysis application, for example, can become more precise (accurate) over time through the use of machine learning.
It sounds simple, but it has its own challenges; developing ML models need a confluence of data science and data engineering. Data is required to create and train Machine-Learning models is a substantial task. And implementing ML technology in real-life scenarios can be challenging for engineers.
According to the latest study, there are approximately 9,977 machine learning startups and companies in Crunchbase (a platform for finding business information about private and public companies. Crunchbase information includes investments and funding information) today, an 8.2% increase over the 9,216 startups listed in 2020 and a 14.6% increase over the 8,705 recorded in 2019.
Over the past five years, Machine Learning startups continue to grow and expand globally, including cities like Telaviv, Sydney, Boston, Seattle, Newyork, Toronto, Singapore, and many other cities & countries.
The following graphic compares the top ten most popular locations for A.I. & ML startups globally based on Crunchbase data as of today:
The data is based on the Crunchbase report; therefore, the information used in this article is from trusted sources.
This article will look at the Five Machine Learning startups to watch in Q3 & Q4 2021. Put on your seat belt, and let’s dive into the world of advanced Machine Learning startups.
Anodot is a Machine Learning and Artificial Intelligence startup founded in 2014 in California, United States, with offices set up in the UK, Australia, and Israel. The company offers a Machine Learning platform that delivers real-time forecasts to a company by continually analyzing their data and understanding all the business parameters in real-time. Anodot can monitor the revenue streams, digital partners, customer experience and instantaneously create an alert if there is a contradiction.
Anodot received an initial seed funding of $1.5 million in 2014, followed by three more rounds in the next three years. They also generated $35 million in Series C funding in 2020, with the total funding becoming $62.5 million.
Dataiku is a Machine Learning startup founded in Paris, France, with current offices in New York, Sydney, London, Munich, Singapore, and Dubai. It was created in 2013 to provide predictive modeling services to other companies for their business applications. Currently, Dataiku provides facilities for data cleaning, data visualization, and data wrangling. It also provides the technology to develop Machine Learning models and deploy them commercially. The main goal of Dataiku is to democratize data and permit companies to use self-service data analytics with a core team of data scientists and analysts provided by them.
The first round of funding was provided to Dataiku in 2015 when they raised $3.6 million, followed by $14 million in 2016. Dataiku was valued at $1.4 billion in 2019 when CapitalG (investment management company of Alphabet Inc.) purchased some of its shares.
The company uses AI and machine learning to bring noteworthy innovation to the workforce and logistics optimization. Not only this, but it also supports real-time tracking, on-demand management, and resource allocation automation to more than 275+ enterprise clients.
The startup has managed to raise total funding of $10.6M. And their last funding was built from a series A round led by Paytm (Payment app). It also developed applications for field workforce management, long-haul tracking and management, last-mile management, and Reverse Logistics Management.
This startup is one of the most creative and insightful platforms when it comes to revenue management. It helps reveal every revenue opportunity in sales, customer success teams, and marketing from every customer. The system is specially designed to capture all contact details and activities. It does this with the help of real-time integration. And analyzing the complete data by making use of AI and ML effectively.
The startup has managed to raise an overall fund of $100M. Their latest funding was from a Series C round by ICONIQ capital. The platform can also create sales performance analytics, feedback, and pipeline reviews to assure their freedom at the stack level.
The startup provides a data catalog based on Machine Learning for helping out people find, understand, and trust the data inside their organizations. The company has defined its solution to align the needs for leading variant personas. Usually, the startup data catalog is best known for its usability and instinctive design. It is said that more than 90+ companies have adopted the Alation Data Catalog, including some big names like Pfizer, eBay, Munich Re, and the City of San Diego.
Alation is funded by Harmony Partners, Icon Ventures, Costanoa Ventures, Salesforce Ventures, and Sapphire Ventures. The brand has managed to pull a total fund of $82M, and their latest funding was raised from a Series C round.
How To Hire a Machine Learning Developer
If you’re a founder of a mobile app startup, if you want to integrate Machine Learning into your app, you must be looking for a Machine Learning developer. However, before you start the procedure, consider the following steps to ensure that you choose a skilled developer to create an app that fulfills your needs. The Machine Learning developer you hire should have the following skills:
- Technical Expertise
An ML developer should be technically sound, but keep in mind that this is not an academic position. However, the developer should have a software engineering degree as well as significant data science experience. In addition, he/she should be familiar with the programming languages, tools, and software necessary to develop a successful ML application.
- Outstanding Thinking Capabilities
An ML developer must be creative enough to adopt your company concept and find out how to make your app unique from the crowd. He/she should provide original realistic ideas and construct a model that depicts your objectives. Check to see if the developer is capable of creating an app that is both creative and customer-centric.
- Excellent Communication Skills
The developer you recruit must have good communication skills to communicate clearly and briefly with you and other team members. Most professional developers operate in an organized manner, connecting with their clients once a week by audio or video call. Make sure your developer is also on the same page.
- Wish To Learn New Technologies
It is impossible to deny that technology is continuously growing and advancing. To deal with these developments, the developer must be enthusiastic about learning and integrating new technologies with ML. Furthermore, he/she should be knowledgeable of the many forms of machine learning and the most recent advancements.
We hope the above information will help you when your start the process of recruiting a Machine Learning developer.
This list is not complete. Many upcoming startups are making outstanding innovations and impacting various industries in exceptional ways.
The primary lesson that we can take away is that AI has oozed into most major industries. Non-AI startups and companies have started to analyze and explore this technology and even partner with AI-based companies like those above to leverage the massive potential of data and optimized statistics – in other words, this cool thing called Machine Learning.
Related Article: How AI is Changing the Face of the Startup Ecosystem?
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Nutty Gritties is a category creator and leader in trail mixes and flavored nuts in India. Along with our strong online presence, we are also available in over 2000 touch-points offline, including retail, modern trade, vending machines, corporates, railways, and airlines. The main expectation from this position is to amplify the digital presence.