Machine learning has come a long way. After decades of research, machine learning went mainstream in 2012 when an AI solution won the ImageNet challenge by a whopping margin of 10.8%, or 41% better than the runner-up score!
From very limited usage in the business world before 2012, machine learning dependency has gone up exponentially since the boom. Today there are 9k+ machine learning startups and companies according to Crunchbase.
It’s been said that in the world of AI and ML, the speed of technological advancement is extreme compared to the pre-AI era. Our new remote work culture only added more fuel to the acceleration of machine learning research. New ML models do things that were unthought-of even a year ago, which has given birth to some exciting startups to watch out for in 2021.
But first, we need to decide how we’re going to rank different machine learning startups and companies:
- Results and performance – this is a no-brainer. Game-changing results and great performance are every CIO’s dream. A big part of generating good results is the quality of data fed into the ML solution. If an ML solution offers to mine excess data to integrate with your available data, that’s a cherry on top of the ML cake. More data leads to better possibilities of accurate trend recognition. Performance depends on optimization and the processing power of the solution provider’s server.
- Easy integration with your framework – every tech company has a tech framework. For startup companies, these frameworks should be flexible and accommodative. For older companies, traditional (and usually monolith) systems form most of the framework. So, depending on the requirements and after assessing the current framework, we can decide if integration is easy. Top-ranking ML solutions will be the ones that cover the most ground.
- Ease of management – less is more. Take the aesthetic design of Apple products for example. Despite being the least cluttered and most simple, these products deliver maximum satisfaction to the user. Similarly, ML solutions that deliver value with the least clutter are often the best pick. Dashboards, clean UI, and smart linkage can make all the difference. Also, if it’s a tool for team usage, facilities such as task assignment and management overview can be great add-ons.
- Easy integration with relevant third-party tools – Machine learning keeps evolving. Every other day there’s some amazing breakthrough that data scientists jump at quickly. Similarly, new tech and tools enter the standard ML toolset every few months. A good ML solution should have the flexibility to evolve its integration capabilities over time, and should have a standard set of integrations available at all times.
Now that we have this covered, let’s move on to our list!
Ranking the top 15 Machine Learning startups to watch in 2021
There are plenty of startups that provide interesting ML solutions and fit our criteria, so I tried to focus on those that are the most interesting, but perhaps not as popular as FAANG-related ML projects.
Cybersecurity can only improve when AI comes into play, and DataVisor has proved it fair and square. DataVisor is an AI/ML solution for increasing the accuracy of fraud detection on a platform level. It has a dynamic (evolving) database of over 4.2 billion user accounts from all over the world. With the help of this data and proprietary unsupervised machine learning models, DataVisor delivers real-time learning with high-value results. It detects fraud before it’s done, by tapping into subtle data patterns that a human, or a traditional system, wouldn’t notice. DataVisor customers are mostly financial services, e-commerce platforms, marketplaces, and social platforms – companies that need protection against financial mishaps and reputational damage.
2. Delta AI
The internet has become a content hub where people (potential customers) willingly share their stories, quirks, and preferences in formats such as text, photos, and videos. The toughest nut to crack in this case is video. According to Delta AI, 85% of a given video is obscured from the usual text-based search. This is where Delta AI comes in with advanced computer vision technology, to leverage similar content to understand how a product appears in a natural context. This new approach towards understanding potential customers purely through the products they use has the promise of being way more efficient than traditional customer segmentation techniques.
Particle is an IoT platform backed by a massive community of 200,000 developers across 170+ countries. It’s an end-to-end platform that combines hardware (IoT segments), software, and AI capabilities to create powerful IoT networks. The customer base of Particle includes a wide range of startups and organizations including Jacuzzi, Opti, Continental Tires, Anderson EV, Watsco, and Shifted Energy.
Reekon provides a niche solution that optimizes the support function of IT customer inquiries and e-tickets on e-commerce platforms. As the number of digital customer-seller relations keeps growing, the number of issues surrounding this relationship also keeps increasing. Therefore AI, being the champion of speed and quality, is the need of the hour in this situation. Reekon studies historical data of past inquiries and solutions to find the best fit for new queries. What’s more, it easily integrates with multiple customer service platforms like FreshDesk, Zendesk, and WooCommerce.
Savvie is a unique AI-based startup that supports bakeries and cafe owners in improving their bottom line. This is done by taking into account huge banks of historical data that recommend actions that the owners can execute to deflect predictable hits to the bottom line. Moreover, given the current situation that has highly impacted the food industry, bakery and cafe owners can remodel their business strategies based on the data-driven patterns and insights that Savvie serves through its ML models.
CloseLoop.ai has been named as one of the fastest accelerating companies in Harvard Business School’s “Real World Data Analytics Landscapes” report. It’s a revolutionary platform that uses the power of data and machine learning to support healthcare organizations in solving a variety of challenges. It bases its recommendations on a library of healthcare-specific models and features. The pre-built models on this platform can be customized so that it caters to the specific needs of the clientele of a healthcare organization.
Some of the services that ClosedLoop.ai offers include prediction of admissions and readmissions, avoidance of no-shows, prediction of total risk and total utilization, prediction of chronic disease onset, and more – a very impressive suite of solutions.
Alation is a first-of-its-kind solution that brings data catalogs to the market. The primary objective of the company is to change the way people find and trust data sources, thus making the world of AI and machine learning much more data fluent and efficient. With more data, there is always the possibility of catching more relevant trends. Over a hundred organizations, including top brands like Pfizer and eBay, leverage the capabilities of Alation.
Imagine having a teacher that adapts to your specific learning needs, and helps you grow with a learning route that’s perfectly designed for you! This is how Overwrite is revolutionizing education. They provide an adaptive learning platform that assesses learner behavior, strengths, and weaknesses, and it streamlines the learning progression accordingly. Detailed analysis of the learner’s progression is also available for review. To add to that, Overwrite is probably the need of the hour in the new remote education culture where students are barely getting one-on-one and real-time coaching. AI is the backbone that can help close this gap and resolve critical educational challenges.
Anodot has been in the game for a long time – since 2014. Anodot uses analytics and artificial intelligence to monitor business data and provide real-time alerts and insights. Anodot has over a hundred customers across industries like fintech, e-commerce, gaming, and AdTech, and some of its top clients are Microsoft, King, and Waze. The top services offered by Anodot include customer experience monitoring, telco network monitoring, revenue monitoring, and partner monitoring.
Zest.ai is an AI platform that supports enterprises in the finance industry by identifying the borrower’s credit fitness. The assessment of borrowers is executed by the Zest Automated Machine Learning system (ZAML). It studies a person’s financial data and suggests how dependable they are as borrowers. This not only helps lenders to pump up the company’s revenue by charging suitable interests from reliable borrowers, but it also reduces risks and speeds up the overall process of credit approval and assessment, driving overall efficiency.
Prolifics revitalizes customer relationships by taking charge of the customer’s digital future. Founded in 1978, Prolifics has a long history that enables it to understand customer dynamics like expectations and demands. Prolifics leverages its huge repository of data and capabilities around Cloud, DevOps, Business Automation, Analytics, and Quality Assurance to ensure that the deliverables are wrapped in a high-quality experience for the customer. The company offers consultations to align your business needs with emerging technologies such as machine learning, AI, automation, and cybersecurity.
Argo is playing a key role in building the foundation of self-driving cars. Argo AI is a self-driving technology platform Argo that builds software, hardware, cloud-support infrastructure, and maps to power self-driving cars. Argo is partnering with top automakers to integrate Argo AI with their vehicles. This delivers the unique combination of established automaker’s expertise in manufacturing high-quality vehicles, and Argo’s expertise in robotics and AI. The launch of Argo Lidar with high resolution, long-range sensing, and 360-degree awareness gave Argo AI an edge in the arena of autonomous delivery and ride-hail services.
8topuz offers a fully automated solution for retail investors to minimize their risk during trading activities. It promises to do the heavy lifting of figuring out the optimal investment routes through automation on heaps of trading data. 8topuz has been known to provide its users with an average 3% ROI per month since 2016, and that doesn’t come as a surprise given that the 8topuz team has 13+ years of experience in the world of finance – ranging across banking, FinTech, and Investments.
Luminance is a leading AI platform for lawyers and legal experts. Its Legal Inference Transformation Engine, or LITE, is used to quickly parse and analyze legal documents. LITE is a combination of machine learning techniques such as inference, deep learning, natural language processing, and pattern recognition. The feature that stands out for luminance is its ability to combine supervised and unsupervised learning techniques to process documents. This makes it one of the most reliable AI platforms for the analysis of legal documentation.
Dataiku is a leading machine learning startup that specializes in providing predictive modeling software for business applications. The platform aims to provide all elements that can enable AI-related experts like Data Engineers, Data Scientists, and Data Analysts to leverage self-service analytics. Dataiku has a clientele of top brands like Comcast, Unilever, and General Electric. Most interestingly, Dataiku has been a two times Gartner Leader in Magic Quadrant for Data Science and ML Platforms and also has a notable investor in Alphabet Inc. All of this means that it’s a company to watch out for.
Neptune – bring ML/AI projects to the next level
Machine learning solutions can get extensive with multiple iterations of models and hyperparameters on the ever-evolving data. Therefore, documenting every step and every result is crucial so that one can easily revert or roll back to the most optimal iteration. Neptune.ai came up with a process revolutionizing solution that automatically documents all model building metadata in a single place. One can not just log, store, display, and compare the model metadata, but most interestingly can also run queries on them. With a surge in data science and ML teams in both corporate and academia, this platform is a great fit in the demand curve. It is ideal for research and production teams who indulge in high volumes of experimentation. NewYorker, deepsense.ai, and Roche are just a few names among Neptune’s exclusive clientele.
Explore Neptune’s features to see if it’s the right tool for your workflow.
If you found the above list insightful, you would definitely love to take a look at CB Insights’ Annual AI 100 list for 2021 which was curated after extensive research across 6000 AI companies. The criteria for the top picks included factors such as R&D activities, tech novelty, business relations, investor profile, news sentiment analysis, proprietary mosaic scores, market potential, competitive landscape, and team strength.
The promising private companies have been shortlisted from across 12 countries and 18 industries like CPG, healthcare, legal, gaming, ecological, and many more! Some vendors on this list are also from the computing domain working on AI deployment, data pipeline, and data processing tools, and Neptune.ai is proud and honored to be enlisted among these companies.
This list is not exhaustive. Many upcoming startups are making ground-breaking innovations and impacting various industries in unique ways.
The main lesson that we can take away is that AI has seeped into most major industries. Non-AI startups and companies have started to 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.
For more insights, you can also visit a few top machine learning conferences in 2021 or check out some amazing books and podcasts on machine learning and AI. In case you are in for something quick, you can always visit some of the latest blogs and resources on AI/ML curated with quality and recency as the top criteria.
The Best MLOps Tools and How to Evaluate Them
12 mins read | Jakub Czakon | Updated August 25th, 2021
In one of our articles—The Best Tools, Libraries, Frameworks and Methodologies that Machine Learning Teams Actually Use – Things We Learned from 41 ML Startups—Jean-Christophe Petkovich, CTO at Acerta, explained how their ML team approaches MLOps.
According to him, there are several ingredients for a complete MLOps system:
- You need to be able to build model artifacts that contain all the information needed to preprocess your data and generate a result.
- Once you can build model artifacts, you have to be able to track the code that builds them, and the data they were trained and tested on.
- You need to keep track of how all three of these things, the models, their code, and their data, are related.
- Once you can track all these things, you can also mark them ready for staging, and production, and run them through a CI/CD process.
- Finally, to actually deploy them at the end of that process, you need some way to spin up a service based on that model artifact.
It’s a great high-level summary of how to successfully implement MLOps in a company. But understanding what is needed in high-level is just a part of the puzzle. The other one is adopting or creating proper tooling that gets things done.
That’s why we’ve compiled a list of the best MLOps tools. We’ve divided them into six categories so you can choose the right tools for your team and for your business. Let’s dig in!Continue reading ->