Neuro
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Machine Learning Playground
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Filestack
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AssetPicker
Filestack is a cloud-based file management platform that provides tools for uploading, transforming, and delivering files in web, mobile, and desktop applications.
Its features include a picker UI, which allows users to upload files from their local computers and various external sources, and the Transformation UI, which provides a range of options for modifying and processing uploaded files.
When integrating these features into their applications, Filestack's APIs give developers flexibility and control.
Filestack can help add file management functionality to an application. Still, it's essential to carefully consider the specific needs of your application and evaluate whether Filestack or other similar tools would be the best fit.
They are simple to implement and offer a lot of flexibility. We can also provide insights into how your users use the system and how that affects your business objectives for your business teams. Users can upload files from a variety of sources, including their local computers, using the uploads feature. Picker offers a user-friendly interface for selecting and uploading files, and it can be customized and configured to meet the needs of a specific application.
Tools for modifying and processing uploaded files are provided by our Transormations API. This can include operations like resizing, cropping, and rotating images. Furthermore, the delivery component includes tools for optimizing file delivery performance and responsiveness.
Neuro FilestackNo features have been listed yet.
Based on our record, Neuro seems to be more popular. It has been mentiond 4 times since March 2021. 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.
Projects are definitely the best way to learn models. Build things for fun that do things in topics/fields that you care about or think is cool. a few years ago when I was getting into ML stuff I build fantasy football things that weren't even useful but provided an actual use case. Then I did more complicated stuff with photography and lighting because I did real estate photography. As far as ML libraries go,... Source: almost 3 years ago
So far I’ve seen AWS Sagemaker kind of allows for a situation like this, but would rather not deal with all that config. Algorithmia and Nuclio are too enterprise focused. Neuro is new and looks great, but from my understanding I would still need to create a lambda instance myself that then calls neuro’s servers - too indirect. Is there a total solution out there for this? Source: almost 3 years ago
A couple of weeks ago I put out a post on DeepSpeech running on the serverless setup at Neuro (https://getneuro.ai), and I've now got Silero running there as well. I've found this model is a lot faster than DS and way more accurate. Seeing around 300ms per request at the moment, hopefully will be closer to 100ms soon but this is a pretty decent speed in this application already. Source: about 3 years ago
I just made a streaming script connecting Deepspeech to serverless GPUs at Neuro (https://getneuro.ai). Was a fun piece of work, and cool to play around with. You can find the source here: https://github.com/neuro-ai-dev/npu_examples/tree/main/deepspeech. Source: about 3 years ago
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