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 Caffe is only appropriate for some new beginners who don't want to write any lines of code, just want to use existing models for image recognition, or have some taste of the so-called Deep Learning.
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 Caffe is good for traditional image-based CNN as this was its original purpose. 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 Caffe's model definition - static configuration files are really painful. Maintaining big configuration files with so many parameters and details of many layers can be a really challenging task. Besides imagine and vision (CNN), Caffe also gradually adds some other NN architecture support. It doesn't play well in a recurrent domain, so we have to say variety is a problem. Caffe's deployment for production is not easy. The community support and project development all mean it is almost fading out of the market. The learning curve is quite steep. Although TensorFlow's is not easy to master either, the reward for Caffe is much less than the TensorFlow can offer. 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 TensorFlow is kind of low-level API most suited for those developers who like to control the details, while
Keras provides some kind of high-level API for those users who want to boost their project or experiment by reusing most of the existing architecture or models and the accumulated best practice. However, Caffe isn't like either of them so the position for the user is kind of embarrassing.
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 Since we stopped using Caffe before it can reach the production phase, there is no clear ROI that can be defined. Read full review ScreenShots