Caffe Deep Learning Framework vs. IBM Watson Studio on Cloud Pak for Data

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.

Incentivized

It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

Incentivized

• Caffe is good for traditional image-based CNN as this was its original purpose.

Incentivized

• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

Incentivized

• 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.

Incentivized

• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

Incentivized

because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

Incentivized

The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

Incentivized

From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

Incentivized

Never had slow response even on our very busy network

Incentivized

I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

Incentivized

The trainers on the job are very smart with solutions and very able in teaching

Incentivized

The Platform is very handy and suggests further steps according my previous interests

Incentivized

It surprised us with unpredictable case of use and brand new points of view

Incentivized

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.

Incentivized

The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

Incentivized

It helped us in getting from 0 to DSX without getting lost

Incentivized

• Since we stopped using Caffe before it can reach the production phase, there is no clear ROI that can be defined.

Incentivized

• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

Incentivized