NanoNets is a Deep Learning web platform that makes it easier than ever before to use Deep Learning in practical applications. It combines the convenience of a web-based platform with Deep Learning models to create image recognition and object classification applications for your business. You can easily build and integrate deep learning models using NanoNets’ API. You can also work with our pre-trained models which have been trained on huge datasets and return accurate results. NanoNets has leveraged recent advances in Deep Learning to build rich representations of data which are transferable across tasks. It’s as simple as uploading your input, generating the output and getting a functioning and highly accurate Deep Learning model for your AI needs. NanoNets is revolutionary because it allows you to train models without large datasets. With just 100 images you can train a model on our platform to detect features and classify images with a high degree of accuracy. NanoNets benefits you in four important ways: ● It reduces the amount of data needed to build a Deep Learning Model ● NanoNets handles the infrastructure for hosting and training the model, and for the run time ● It reduces the cost of running deep learning models by sharing infrastructure across models ● It is possible for anyone to build a deep learning model
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Based on our record, NewRelic seems to be a lot more popular than Nanonets. While we know about 81 links to NewRelic, we've tracked only 6 mentions of Nanonets. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
*1. New Relic *— it’s a tool to check on the slow performance of your app. If any action of the user takes longer than usual, NewRelic will inform you about that. - Source: dev.to / 2 days ago
Tip: You can use tools like DataDog, perf (Linux), New Relic etc. To monitor cache performance. - Source: dev.to / about 1 month ago
Using APM tools like NewRelic, Sentry, Datadog, etc to monitor the performance of your application and while you're on it, they can help you identify N+1 queries. - Source: dev.to / about 2 months ago
These tools track server and underlying infrastructure and backend performance. They monitor several metrics, like disk I/O, CPU and memory usage, network traffic, and more. Some examples of these tools include New Relic, Datadog, and AppDynamics. Web administrators can use them to see what's causing slow SRT, like high CPU usage or network traffic. Server-side monitoring tools also provide real-time alerts to... - Source: dev.to / 3 months ago
11 Application performance: Before we even perform a deployment, we should configure monitoring tools like Retrace, DataDog, New Relic, or AppDynamics to look for performance problems, hidden errors, and other issues. During and after the deployment, we should also look for any changes in overall application performance and establish some benchmarks to know when things deviate from the norm. - Source: dev.to / 3 months ago
Want to automate repetitive manual tasks? Check our Nanonets workflow-based document processing software. Source: almost 2 years ago
Nanonets is a no-code, workflow-based, and AI-enhanced intelligent document processing platform. It automates all document processes and is built on a robust, intelligent, self-learning OCR API that allows users to extract required data from documents in minutes. Source: almost 2 years ago
Check out our website here https://nanonets.com/ for more. We also have some free tools where you can experience our product for free (like https://nanonets.com/online-ocr). Source: about 2 years ago
Here is another company, which I just came across by accident, which do the same: https://nanonets.com/. Source: about 2 years ago
We will be using Python3.6+, Django web framework, Nanonets for character extraction from an image, Cloudinary for image storage and Google Search API for performing the searches. - Source: dev.to / over 2 years ago
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