Amazon Deep Learning AMIs vs. Gluon vs. Keras

Amazon AMIs has been very useful for the quick setup and implementation of deep learning for data analysis which is something I have used the service for in my own research. We commonly use the service to enable students to run intensive deep learning algorithms for their assessments. This service works well in this scenario as it allows students to quickly set up a suitable environment and get started with little hassle. If you are looking to run simple, surface level deep learning algorithms (kind of contradictory statement I know) then AMI is more complicated than most will need. When it comes to teaching the basics of Machine Learning, this kind of system is unnecessary and there are other alternatives which can be used. That being said this service is a must if you are looking to run complex deep learning via the cloud.

Incentivized

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

Incentivized

• Setting up environment
• Support for different types of machines
• Perfect for Machine Learning / Deep Learning use cases
• Nvidia / Cuda / Conda support easily

Incentivized

• 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.

Incentivized

• Some aspects of the User Interface are quite confusing and activating packages can be a bit convoluted
• It can be a bit confusing to switch between frameworks for novice users

Incentivized

• 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

Incentivized

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.

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.

Both of these services provide similar functionality and from my experience both are top class services which cover most of your needs. I think ultimately it comes down to what you need each service for. For example Amazon DL AMIs allows for clustering by default meaning I am able to run several clustering algorithms without a problem whereas IBM Watson Studio doesn't provide this functionality. They both provide a wide range of default packages such as Amazon providing caffe-2 and IBM providing sci-kitlearn. My main point is that both are very good services which have very similar functionality, you just need to think about the costs, suitability of features and integration with other services you are using.

Incentivized

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.

Incentivized

• Saves a lot of Infra Costs
• Saves a lot of time in handling environment issues
• Easy to start a new instance

Incentivized

• 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.

Incentivized