Staying ahead of the technology curve means strengthening your competitive advantage. That is why we give you data-driven innovation insights. This time, you get to discover 5 hand-picked startups developing ensemble learning solutions.
Global Startup Heat Map: 5 Top Ensemble Learning Solutions
The 5 startups you will explore below are chosen based on our data-driven startup scouting approach, taking into account factors such as location, founding year, and relevance of technology, among others. This analysis is based on the Big Data & Artificial Intelligence (AI)-powered StartUs Insights Discovery Platform, covering over 1.3 million startups & scaleups globally.
The Global Startup Heat Map below highlights the 5 startups & scaleups our Innovation Researchers curated for this report. Moreover, you get insights into regions that observe a high startup activity and the global geographic distribution of the 80 companies we analyzed for this specific topic.
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Tex-AI works on Text Mining and Extraction
Text-based analytics have multiple uses within organizations. For example, Twitter, Facebook, Linkedin, and other social media companies use text analytics to determine public opinion. Similarly, content consumption and distribution agencies, as well as retailers use text (data) generated by their users to give smarter recommendations. The ability to collect, analyze and build data models efficiently is not simple which is where startups come in to develop new text extraction and text mining solutions.
US-based startup Tex-AI is providing a text extraction and text analytics service to businesses in order to produce structured data, metadata & insights from their text. Tex-AI’s machine learning text classification engine uses ensemble modeling to make the most optimal decision when classifying text data. Besides English, the service also extends to multiple other languages including Thai, Arabic, Latin, Mandarin, and more.
Cnvrg.IO builds an Enterprise AI Operating System
Today, many organizations that use AI or machine learning to develop products are constrained by the bridge between research being done and implanting the research into a production process. A way to avoid this problem is to work with researchers or data scientists with technical model-building know-how and the knowledge to implement the model in a product. Startups are developing solutions that allow data scientists and researchers to focus on their expertise and design models without worrying about integrating them into the production process.
Israeli startup Cnvrg.io builds an AI-based operating system that is designed to scale and accelerate the cycle of development to deployment. The platform primarily caters to data scientists who can focus on the research and development of analytical models for products and services rather than their implementation. The platform also offers other benefits such as hybrid and multi-cloud support, scalability, easy collaboration between teams, automation, language and framework flexibility, and compatibility with multiple developer environments such as JupyterLab, RStudio, and more.
Data Trading focuses on AI Analytics for Trading and Investing
Stock markets are a significantly large source of investments for people across the globe. Some stock markets also include cryptocurrencies as their popularity and adoption among both older and newer investors increases. However, many people also refrain from investing in the stock market either due to its volatile nature or the fear of not having the knowledge to enter it. Startups are developing solutions to encourage both new and existing traders and investors by offering feature-rich and intuitive trading platforms.
Ukrainian startup Data Trading provides the users of their service with an AI-based investment and trading platform. The platform uses an ensemble of neural networks and machine learning to help traders identify entry points for financial instruments and cryptocurrencies. The platform also features a trade adviser for users who do not have sufficient knowledge to manage their own trades.
UCit delivers High-Performance Computing (HPC) as a Service
High-performance computing is generally associated with fields such as DNA research, physics, chemical simulations, and rocket science, etc., which are highly specialized and academic-intensive fields. Startups extend the benefits of HPC in processing large volumes of data at high speeds to make intelligent recommendations for other fields and industries as well.
French startup UCit provides cloud-based software services to improve high-performance computing infrastructure by increasing the efficiency of the HPC clusters. Their tool Predict-IT optimizes jobs and clusters in an HPC, the framework of Predict, based on a series of machine learning models that learns from the data and constantly improves itself.
Geminus AI models Digital Twins
The digital twin is one of the most prominent developments in Industry 4.0 which enables engineers and product designers to create virtual environments and calculate the most efficient position of each individual item of a digital object replica. Most digital twin software and services use intelligent recommendation or AI to build advanced physics integrated models.
US startup Geminus develops a digital twin solution that uses AI to train and ensemble models. The tunable parameter in each of the built models includes the augmentation of the physics models with operators and functions. The insights derived from the optimized ensemble allows for very specific recommendations regarding the location and placement of assets.
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