Today, there are multiple large companies offering cloud services for AI and NLP.

Google has its Cloud Machine Learning Engine; Microsoft has a series of Cognitive services APIs through Azure; IBM’s Watson Ecosystem has spread out into various sectors, and it’s Natural Language Processing services have been at the forefront; and Amazon, the biggest cloud player, has many Machine Learning tools offered through AWS such as Rekognition, Polly and Lex.

From the perspective of NLP, there are several services available to engineers based on the level of customization you’re interested in. On one hand you have open source, hands-on, APIs like Tensor Flow, Stanford’s Core NLP suite, Caffe, Theano, Torch, CNTK and more. Dealing with such APIs requires you to create, train and deploy your own data-set, which might not be feasible for a lot of teams.

If, however, you’re looking to build something quick to test your hypothesis, you can pick from services like API.AI, Watson Conversation, Amazon Lex or MS Cognitive Services. They are all comparable in price and some of them have lucrative free credit offerings for startups and entrepreneurs. (e.g. IBM Global Entrepreneurship, AWS Activate Program). This makes them highly attainable to small developers.

Overall, the current landscape of NLP is booming, as it becomes possible for anyone to create anything. This development will revolutionize AI, and in the near future we can expect human-like bots to join us in our daily lives.

For our own product Archie.AI- The Artificially Intelligent Data Scientist, we used a combination of Watson NLC, api.ai, and our own Machine Learning algorithms.

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If you would like to know more about my work with AI/ML, check out Archie.AI