The B2B platform for the best purchasing descision. Identify and compare relevant B2B manufacturers, suppliers and retailers
Filter
Locations
Result types
Type of company
Select company type
Industries
Select industry
Company status
Select company status preset
Number of employees
Min.
Max.
Founding year
Lock keywords
Exclude keywords
Optional keywords
San Francisco, United States
11-50 Employees
2011
Instead of relying on annotation and human training, Shazura's patented Fingerprint bio-inspired embedding translates every image into a unique fingerprint. Organizations can get the most accurate image and video recognition with Shazura's AI that works instantly without the drain on time, human resources and high-consumption servers. They've built ML/DL models that rely on extensive human training. We’ve been doing this for over a decade and provide the most powerful and market-ready Computer Vision.
+
Featured
Technology - Computer Vision with single-sample unsupervised learning. Edge to Cloud AI Leading Platform.
... Technology - Computer Vision with single-sample unsupervised learning. Edge to Cloud AI Leading Platform. ...
Austin, United States
1-10 Employees
2021
Crosstab Data Science works with companies to build new machine learning capabilites and to help existing data science teams level up. Brian’s mission is to deliver real-world impact with machine learning. We have deep expertise in the following areas and we’re always looking to expand our repertoire.
+
Featured
Crosstab Data Science
... Unsupervised Learning ...
Petah Tikva, Israel
1-10 Employees
2019
IronComm provides a cutting-edge incident management software, designed to minimize unplanned downtime in production and operational environments. With IronComm's innovative solution, companies can reduce the amount of time lost due to unexpected disruptions and keep their operations running smoothly. The AI engine and dashboard can provide a learning tool and automatically suggest improvements for the system and personnel. IronComm provides an efficient and intuitive “one stop shop” for all your communication needs. Downtime in manufacturing can cost millions of dollars an hour. Issy is a financial analyst and consultant with over 15 years of experience in the communication and automotive tech industry.
+
Featured
HOME | IronComm
... By implementing unsupervised learning AI, NLP and others, IronComm created a system that is constantly learning and improving, giving your organization all the tools it needs to manange incidents, increase efficiency, all while saving time and ...
Menlo Park, United States
11-50 Employees
2016
Don’t miss Helm.ai appearances at summits, webinars and more. Catch all the latest Helm.ai headlines and never miss a new development. Deep Teaching offers far-reaching implications for the future of computer vision and autonomous driving, as well as industries including aviation, robotics, manufacturing and even retail.
+
Featured
Pioneering a breakthrough in unsupervised learning for AI and autonomous technologies.
... Pioneering a breakthrough in unsupervised learning for AI and autonomous technologies. ...
Italy
1-10 Employees
We develop and deploy applications that are perfectly integrated into customer ecosystem environment. We provide decisional support tools which are able to process significant volumes of complex data, quickly in a flexible way and adaptable to the business changes. We believe that the adoption of AI technologies is a fundamental step for business improvement to face the quick changes of the markets. This is a great space to write a long text about your company and your services. Talk about your team and what services you provide. For example, knowing in advance the specific market trends or how much your products will sell can help the decision-making process of a company. In supervised machine learning a model is built using labeled data, with the goal of learning to make predictions on unseen observations. The environments are prepared for the release in production of the model and the monitoring for the Continuous improvement cycle is activated, in order to guarantee your company optimal performance in all circumstances.
+
Featured
Company | synapps
... Unsupervised Learning ...
Bloomfield Hills, United States
11-50 Employees
2016
Karamba Security provides industry-leading, award winning, end-to-end cybersecurity solutions for vehicles and connected systems. Karamba Security's' award-winning security solutions discover, mitigate and manage vulnerabilities throughout the vehicle and IoT device lifecycle, enabling OEM suppliers and device manufacturers to meet cybersecurity regulations, without delaying start or production schedules, or needing to change the ECU or IoT device architecture. Fortune 100 companies such as HP and Samsung, EV and SDV OEMs, and IoT product manufacturers rely on Karamba’s end-to-end product security portfolio to meet product security regulations, increase their brand competitiveness and protect their customers against cyberattacks. Karamba Security is led by an experienced executive team comprised of cybersecurity experts and seasoned entrepreneurs. David is a serial entrepreneur with go-to-market executive experience, with a track record of major increases of shareholders value. Tal is experienced in developing new, innovative products into viable, market-changing products. Karamba's end-to-end product security portfolio enables electric and software-defined vehicle OEMs and Tier-1 suppliers to comply with the ISO/SAE 21434 standard and with Executive Order 14028, without interfering with R&D schedules or post-production operations. Karamba’s award-winning XGuard Host IDPS software hardens the ECU against exploits of hidden and known vulnerabilities, with negligible performance impact of 5% CPU and RAM.
+
Featured
Seamlessly-applied Device Securityand Unsupervised Machine Learning
... Seamlessly-applied Device Securityand Unsupervised Machine Learning ...
Onna, Japan
1-10 Employees
2020
KanjuTech recognizes the most important part of the data. KanjuTech is able to understand sound, images, text, and other types of data.
+
Featured
KanjuTech – Artificial brain for automated transcription
... Unsupervised learning ...
Lucknow, India
11-50 Employees
2014
Our vision is to help customers to achieve their dream projects with our next technology innovation in the space of Embedded, AI/ML, IoT, Cloud, Web & Mobile Apps. Our vision is to help customers to achieve their dream projects with our technology innovation in the space of Embedded, AI/ML, IoT, Cloud, Web & Mobile Apps. From the day of inception, Inevitable Infotech travelled a long path in Product Engineering, Cloud Solutions and Manufacturing sectors to provide next-generation enterprise solutions to valuable customers with end-to-end design, development, and manufacturing, all under one roof. Inevitable Infotech is a software development wing of Inevitable group of companies which consist of other companies associated with different sectors, named Inevitable Electronics and Inevitable Products In correlation with our other wings, we offer a new & unique solution ecosystem of a one-stop-shop for our customers by offering complete hardware, software, mechanical, testing, prototyping, and manufacturing services. Shakti brings with him over 15+ years of experience in Embedded System Design and Corporate Business Dealing & Training. Krishna is a results driven technology leader with 30+ years of global experience in managing the entire technology operations with a successful track record of delivering on/before time and within budget. Delivering enterprise level global solutions across Telecom, EDA, embedded, mobility, and enterprise systems domains in project management, service delivery Management, and Relationship Management. Kanaujia brings more than 20 years of experience in multiple dimensions of technologies, including sectors such as Artificial Intelligence, Internet of Things, Brain Computer Interface, Healthcare and ICT.
+
Featured
About us | Inevitable Infotech
... Inevitable Infotech is a leading software development company in Lucknow offering Artificial Intelligence, MI, deep learning, unsupervised learning, supervised ...
Sydney, Australia
1-10 Employees
We are made up of passionate software engineers at heart. All our products and services have been designed and developed by our in-house team by combining our years of software engineering with our deep knowledge and expertise in AI. Use technology and code that we have acquired over many years of experience in the Industry. We use a combination of machine learning and deep learning to provide unique insights that combine sentiment, classification and prediction. No matter what stage you are at we are ready partner with you and help you navigate the data and AI landscape. Our ongoing relationships with businesses and leading academics ensure that we are not only on the cutting edge of AI technologies and research but also bring with us business knowledge and acumen to your business.
+
Featured
Home | VectorX Home
... Unsupervised Learning ...
New York, United States
1-10 Employees
The aim of thedatascienceportal is to take data science from a mysterious subject filled with boring jargon and make it into something exciting and cool which is understood by everyone. This is a space dedicated to the exciting world of Data Science and Machine Learning. Science is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe.
+
Featured
Home - The Data Science Portal
... Machine Learning: Unsupervised Learning ...
Technologies which have been searched by others and may be interesting for you:
Some interesting numbers and facts about the results you have just received for Unsupervised Learning
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 80 |
| Amount of suitable service providers | 67 |
| Average amount of employees | 1-10 |
| Oldest suiting company | 2011 |
| Youngest suiting company | 2021 |
Unsupervised learning refers to a type of machine learning algorithm that operates on data without labeled responses, essentially learning from the data itself without any guidance. Unlike supervised learning that learns from a known output, unsupervised learning identifies patterns, similarities, or differences within the data, clustering them into groups based on their attributes. This method is pivotal in discovering the underlying structure of data, making it highly valuable in fields such as anomaly detection, customer segmentation, and recommendation systems. Its ability to analyze and interpret complex data sets without prior human intervention allows for the identification of more nuanced patterns and relationships that might not be immediately apparent. As a result, unsupervised learning is instrumental in enhancing machine learning models' efficiency and accuracy, providing a deeper insight into data analytics and predictive modeling. This approach not only accelerates the pace of innovation in AI technologies but also significantly impacts sectors reliant on data interpretation, from healthcare diagnostics to market trend analysis, by offering a more sophisticated, autonomous means of data exploration.
1. Efficiency in Handling Unlabeled Data
Unsupervised learning thrives on unlabeled data, making it a powerful tool for analyzing vast datasets where annotating or labeling information is impractical. This capability allows for the exploration and discovery of hidden patterns without the need for extensive human intervention, saving time and resources.
2. Discovery of Hidden Patterns
This approach excels in identifying underlying structures or patterns within data that might not be immediately apparent. By analyzing data without preconceived notions, unsupervised learning can uncover novel insights that could be overlooked by supervised methods, providing a deeper understanding of data characteristics.
3. Flexibility
Unsupervised learning algorithms are inherently flexible, adapting to the data's intrinsic properties rather than following a rigid set of rules. This adaptability makes them suitable for a wide range of applications, from customer segmentation to anomaly detection, where the specific outcomes may not be known in advance.
4. Enhanced Data Preparation
By revealing the natural groupings and structures in data, unsupervised learning methods can significantly improve the preparation of data for further analysis or supervised learning tasks. This pre-processing step can enhance the performance of subsequent models by providing clearer, more defined datasets.
While evaluating the different suppliers make sure to check the following criteria:
1. Technical Expertise in Unsupervised Learning Algorithms
Ensure the supplier has a proven track record in deploying unsupervised learning projects, with a focus on clustering, association, and dimensionality reduction techniques.
2. Data Security and Privacy Measures
Evaluate the supplier's commitment to data security, especially how they handle and protect sensitive and proprietary data during unsupervised learning processes.
3. Scalability and Integration Capabilities
Check if the supplier's solutions can easily scale and integrate with existing systems to support growing data volumes and evolving business needs.
4. Customization and Flexibility
The supplier should offer customizable solutions that can be tailored to specific business requirements, ensuring flexibility in adapting to different unsupervised learning scenarios.
5. Support and Maintenance Services
Assess the quality of ongoing support and maintenance services, ensuring that the supplier provides timely assistance and updates for their unsupervised learning solutions.
Unsupervised learning, a type of machine learning where algorithms learn patterns from untagged data, has diverse applications in the B2B sector. One prominent use case is in customer segmentation. Companies leverage unsupervised learning to analyze customer data and identify distinct groups based on purchasing behavior, preferences, and demographics. This segmentation allows businesses to tailor marketing strategies and products to each specific group, enhancing customer satisfaction and loyalty. Another significant application is in fraud detection, especially within the banking and finance industries. Unsupervised learning algorithms can sift through vast amounts of transaction data to detect unusual patterns that may indicate fraudulent activity. By identifying these anomalies quickly, companies can mitigate risks and protect their clients' assets. In the realm of supply chain management, unsupervised learning helps businesses optimize their operations. By analyzing data on supply chain logistics, these algorithms can uncover inefficiencies and bottlenecks. Companies can then address these issues to streamline operations, reduce costs, and improve delivery times. Lastly, unsupervised learning is instrumental in predictive maintenance within the manufacturing sector. By monitoring equipment data, algorithms can predict when a machine is likely to fail, allowing for timely maintenance. This proactive approach reduces downtime and extends the lifespan of machinery, significantly impacting productivity and operational costs.
Unsupervised learning, a subset of machine learning algorithms that operate on unlabeled data without explicit instructions, is currently at a Technology Readiness Level (TRL) ranging between 4 and 6. This estimation reflects the technology's transition from laboratory research to being validated in relevant environments. The primary technical reason for this positioning is the complexity and variability of real-world data, which often presents challenges in achieving consistent and reliable outcomes without human supervision. While unsupervised learning techniques, such as clustering and dimensionality reduction, have shown promise in discovering hidden patterns and data insights, their applicability and effectiveness can significantly vary depending on the domain and the quality of the data. Additionally, the interpretability of models generated through unsupervised learning remains a challenge, limiting their readiness for broader application in critical systems without further validation. Although advancements in computational power and algorithmic efficiency have propelled the development of unsupervised learning, the need for further research and practical validation in specific use cases is evident to advance its TRL. This ongoing development underscores the potential of unsupervised learning while highlighting the current limitations that must be addressed to enhance its reliability and applicability across various industries.
In the Short-Term, unsupervised learning is poised to see significant improvements in anomaly detection algorithms, which are critical for cybersecurity and fraud detection. Advances in dimensionality reduction techniques will also enable better handling of large, complex datasets, facilitating more efficient data processing and analysis. These enhancements will improve the precision of pattern recognition, making unsupervised learning more applicable in sectors like finance and healthcare where quick, accurate insights are paramount. The Mid-Term outlook focuses on the integration of unsupervised learning with reinforcement learning to create more sophisticated AI models. This combination will lead to the development of AI systems capable of more nuanced decision-making and problem-solving without explicit programming. Such advancements will be instrumental in autonomous vehicles and robotics, where machines must navigate and adapt to unpredictable environments. Additionally, we'll see unsupervised learning techniques being used to refine natural language processing, enhancing machines' understanding of human language in a more contextual and nuanced manner. Looking into the Long-Term, unsupervised learning is expected to revolutionize the way AI systems learn from data, moving towards true artificial general intelligence (AGI). The development of algorithms that can understand and conceptualize the world in a manner similar to humans will mark a major milestone. This will open up unprecedented opportunities in every field, from personalized education programs that adapt to a student's learning style to advanced healthcare diagnostics that predict and prevent diseases before they manifest. Unsupervised learning's role in achieving AGI will be pivotal, marking a new era in technology where machines can autonomously learn and evolve.
