Amazon Deep Learning AMIs vs. IBM Watson Natural Language Understanding

Suitable: 1. Best for quickly setting up an instance with pre-installed libraries. 2. Ideal for people in Deep Learning space who struggle with Cuda / Nvidia driver installations. Not suitable: 1. People who want to install custom libraries or different version of those. 2. In these cases, updating the version of libraries many times leads to version mismatch which can cause many errors.

Incentivized

IBM Watson Natural Language Understanding is a Swiss Army knife that can be used in many scenarios. An extensive list of easy to use APIs is provided making it very easy to integrate it in any environment. The text analysis is decent and above market average. It generates results in many forms to suit may scenarios (important keywords, concepts, sentiment analysis, etc.).

Incentivized

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

Incentivized

• Easy to use and extensive APIs.
• Decent accuracy.
• It recognizes concepts and semantic roles.

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

• Improve Sentiment Analysis accuracy.
• Prevent having conflicting results (sad and happy, etc.).
• Foreign names detection.

Incentivized

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

• It has made our Data Science/ Machine Learning Courses easier to manage/ need less human input therefore allowing us to increase the cohort size for this degree
• It has unified a lot of technologies reducing the load on our IT team

Incentivized

• Reduced development time.
• Increased solution efficiency in understanding the user.
• Increased solution scalability.

Incentivized