avin_regmi

That's really good to know. Why do SEOs use this? Would you be able to connect them with me?

This requires you to upload a labeled dataset. My idea was more of like you enter instances of text without label and ML model will label those texts for you.

This looks really good. I would like to speak with you. Do you have an email I can contact you?

what about playing with different hyperparameters? I always found that time consuming? What do you guys think?

Thanks for the clarification.

This looks interesting but who are your targeted customers? Are you building this for developers? Also what kind of models do you support?

I'll take your feedback into consideration :)

Our platform is free for the beta users to try it with limit of 2GB per model. We are just starting and we haven't decided on our business model yet.

If a user downloads panini to their private server and use it that will always be free since there is not infrastructure cost for us. If you're deploying it in our website we will be charging you to pay for the infrastracture cost.

Our main goal currently is to find out if people find this product useful and if it's worth for us to spend more time working on it. Thanks for watching the YouTube tutorial and if you have further questions, please contact us. Thanks

It really depends on the application. Such as content recommendation, prediction of popular items are requested frequently. We maintain prediction cache so we can serve the frequent cache without passing into the model. We also use cache for selecting a model. To do this we join the original prediction with the feedback it receives. Feedbacks are received soon after the prediction, even unique query can benefit from a cache. Most of the prediction models are not Deep learning these days. Most companies are using classical machine learning. In our case, we trained SVM in SciKit learn feedback throughput of 1.8x. We have a simple LRU eviction for cache and use normal cache eviction algorithm.

What are you currently using to server ML models?

Optimized TF serving would perform similarly to Panini however, it's really hard to find good documentation on optimizing TF serving compilation parameters. Panini automatically finds the right batch size to maximize the throughput and it adaptively changes. We also have a technique to reduce bound tail latency. I would love it for you to try it and provide me some feedback. Thanks

I would love it if you try and provide me some feedback.

Sorry, I should've been more clear. Both predictions for TF serving and panini serving was done in a single thread in the same specification machine. We used a simple model for image classification of CIFAR dataset. Roughly, 500 predictions were made for panini and 200 predictions for TF serving. The graph on the website is for throughput. I'm planning to write a medium post soon regarding the benchmark test.

1. Caching the input will save lots of time. Inputs are not unique each time. In a production environment, lots of inputs are the same. Many platforms in fact will do caching such as Algorithmia, TF Serving, and Sagemaker. If a time to do a search in Redis database is faster than forward pass, caching will reduce time dramatically. Watch my youtube video where I give an example.

2. It's up to you if you want to use it in GPU or CPU. Benchmark was done in a CPU but you're free to download panini via Helm and use GPU in your private kubernetes.

3. For now, during beta testing, we're offering free inference and there is a limit of model size cannot exceed over 2GB.

Hope this was helpful.

Sorry, I should've been more clear. Both predictions for TF serving and panini serving was done in a single thread in the same specification machine. We used a simple model for image classification of CIFAR dataset. Roughly, 500 predictions were made for panini and 200 predictions for TF serving. The graph on the website is for throughput. I'm planning to write a medium post soon regarding the benchmark test. There are many other projects getting higher throughput compare to TF serving. I've heard TF Serving could be optimized to make it more efficient but making it more optimized is not documented properly. We're planning to make it open source if there is enough interest from the community!

Hey, both prediction for TF serving and panini serving was done in a single thread in the same specification machine. We used a simple model for image classification of CIFAR dataset. Roughly, 500 predictions were made for panini and 200 predictions for TF serving.

You can always download the entire panini in your own private server and not pay anything. Ie. used Helm to install in your own kubernetes or DockerHub. For now, We're making it free for models under 2GB. Our main goal is to make it usable and we don't want cost to be a factor.

Hey, you don't have to deploy in GKE and it's not GKE that makes it faster. We also give you option to deploy in your own private Kubernetes via Helm or private server via DockerHub. GKE may not be the right option for you depending on your application. Your feedback would be very valuable to us. Please tell me why you think its fishy? We're always tryiing to make it better.

How big is low latency issue for you? What happens if it's more than 100ms? Also we do offer our software to be deployed in your kubernetes couster via helm.

Inferring the model with Flask is slow and requires custom code for caching and batching. Scaling in multiple machines using Flask also causes many complications. To address these issues, we have developed Panini. https://www.panini.ai What do you guys think?

What frameworks are you using currently in production? Do you think classical machine learning algorithms are being used more than deep learning?

Yes, I think it has been lot cheaper and and more easier to train state of the art models such as fast ai.

I just checked Uber's Ludwig. It looks very cool and super easy to use. Are companies using this in production? How will I serve the model once I train it on Ludwid?

Do you know which frameworks, companies are using in production?

What framework are you using?

Shoot me an email avin@panini.ai I can defintely help you with this!

What kind of model are you looking into deploy? It it pytorch/tensorflow?