GPU-accelerated deep learning solutions by Softweb enable clients to discover new ideas and processes, as well as adapt to mercurial business scenarios. We promise to deliver deep learning services that foster superior business models, improvised products and innovative services.
Our Image recognition models enable users to detect an object or attribute in an image. With image recognition capabilities, users can identify the following attributes:
Our video intelligence models help users to identify and tag different entities in video or motion pictures. Video analytics is used in wide range of domains including the following:
Using algorithms for acoustic and language modeling, we provide speech recognition capabilities to enhance dictation ability. We provide users with following speech recognition capabilities:
Instead of relying only on natural language interaction, with conversational interface, we present users with enhanced natural language understanding that provides a richer and more dynamic user experience. We provide the following natural language processing applications:
Softweb’s deep learning cloud service helps to not only reduce operating costs across the board but also process large volumes of data to derive insightful actions. The key highlight of our unique solution is to perform feature detection from massive amounts of unlabeled training data.
Deep Learning Market Size Worth $10.2 Billion By 2025. – Grand View Research
Gathering unlabeled training data to make concise conclusions
Generating actionable results with data processing models when solving data science tasks
Training neural network models to automate the learning of complex representations of data
Labeling large amounts of data and determining their matching characteristics
It’s a complex process of managing orders, shipping, warehousing, inventory control, and utilization. Supply chain visibility and delivery planning are important aspects for logistics companies.
By gathering and feeding data continuously to the GPU-driven AI models, businesses can predict and recommend future processes that help determine the best combinations of carriers and routes for delivering loads.
The key challenge that the retail industry faces is to aggregate online and offline data to recognize patterns in the data that could positively influence pricing, inventory, customer experience, and profitability.
Deep learning makes it possible for retailers to discover patterns in their data. With the analyzed data they can influence their customers’ experience. Moreover, they can utilize the insights to offer personalized product recommendations that ultimately leads to increase in sales.
Diagnosing rare diseases traditionally is complex and time-consuming. At times CT or MRI scans are highly complex and traditional analytics tools have been unable to explore them fully.
Image diagnostic tools powered by deep learning help in early diagnosis of rare diseases. With EHR systems in place, more healthcare data is generated that can be used to train algorithms that help understanding important features related to the disease from a group of medical images.
A security guard or systems manager may never have time to watch or review the growing hours of surveillance video.
GPU-accelerated intelligent video analytics can notify security teams of potential threats as they happen, helping to prevent break-ins or illegal activities. This use of deep learning algorithms can be highly beneficial to secure schools, houses, and organizations. It can spot a person loitering at the perimeter of a schoolyard and alert on-the-ground security officials or it can help in detecting and reporting shop-lifting.
Manufacturers want to excel at product quality while still being able to reduce lead-time production runs from customers. Challenges that manufacturers face are: new products and competitors that are proliferating in manufacturing today, and tightening delivery windows.
Deep learning-based root-cause analysis, predictive maintenance, and reducing testing costs using AI optimization are the top three areas where deep learning will improve the manufacturing ecosystem. This will give them a leverage to reduce cost of production and maintenance, and ensure on-time delivery.
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