Likelihood to Recommend Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
Read full review 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 Read full review Pros Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models. Customization and easy to alter and change. If you already are an Amazon user, you do not need to transition over to another software. Read full review 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. Read full review Cons The UI can be eased up a bit for use by business analysts and non technical users For huge amount of data pull from legacy solutions, the platform lags a bit Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized Read full review 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 Read full review Usability 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.
Read full review Support Rating 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.
Read full review Alternatives Considered Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
Read full review 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.
Read full review Return on Investment We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers. We can prototype more rapidly because it is easy to configure notebooks to access AWS resources. For our use-cases, serving models is less expensive with SageMaker than bespoke servers. Read full review 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. Read full review ScreenShots