Keras vs. Portainer

Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch

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Many developers, especially lesser experienced developers, don't have a really good background in setting up containers from the command line. Portainer is invaluable to them. Giving a UI to them gives them much more confidence and allows them to learn properties and capabilities of containers under far less stress. On the flip side of this, giving then a UI on a production system can lead to chaos...never give junior developers access to production servers.

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• One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
• It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
• It also provides functionality to develop models on mobile device.

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• Separating server maintenance with application development, providing a clear user interface for developers who don't want to worry about the underlying server.
• RBAC for container deployment linked to a SAML IDP, not something particularly easy in a native Docker instance but point and shoot in Portainer, allowing the use of Azure / Okta etc to provide user access.
• Image management with multiple repositories is super clear and reduces incidents

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• As it is a kind of wrapper library it won't allow you to modify everything of its backend
• Unlike other deep learning libraries, it lacks a pre-defined trained model to use
• Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

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• When setting up static IPs for a new container, having the used ones for a specific network at hand would be cool or something like a mini IPAM of some sort...
• Using the developertools to see "oh, it's a 40x or 500" when something doesn't seem to load because the ui just states "Didn't work" is kinda annoying. expandable toasts or something would be nice.

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It is an excellent tool.

I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.

Accessibility for Non-Experts: even with some people having a bit longer on-boarding it is still very simple Quick setup is insanely useful, we can get it running in 10 seconds after installing docker Portainer has once again super clean UI and is very user friendly. Deployment/monitoring and management are super easy. I can tell just from a glance if something is out of date (watching at you Watchtower not doing your job for some reason)

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Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

One of their staff members jumped on a video call immediately with me and led me through the problem and solution during a quick session of screen sharing. In this day and age that is above and beyond, especially when it comes to software. It took approximately 5-10 minutes to diagnose and fix, including pleasantries!

It is really easy. Just follow the documentation.

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Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.

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Portainer takes the cup in terms of usability and features. It is also more useful for smaller deployments, whereas Kubernetes in our opinion and experience, could probably be more suited to certain other use cases. Portainer is also a fresh feel among all the preexisting container management solutions and brings positivity and a new breeze in the industry.

• Easy and faster way to develop neural network.
• It would be much better if it is available in Java.
• It doesn't allow you to modify the internal things.

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• Increased productivity: Portainer's user-friendly interface and streamlined container management can help increase the productivity of IT teams.
• Cost savings: By simplifying container management, Portainer can help reduce the time and resources required to manage container environments, potentially leading to cost savings.

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