This post is intended to help data scientists and engineers who, in some capacity, have implemented routines/algorithms/data that does a specialized function (e.g. machine learning) using a dynamically typed language, such as Python. The goal is to web-enable these routines/algorithms using an application programming interface (API).
Exposing these functions/data as an API allows for:
Easier, faster, and consistent sharing of functionality/data that could further progress the research. A good example of this is the Materials Project from the Lawrence Berkeley National Laboratory. They deemed it necessary for the scientific community to have access to their data, hence exposing it as
UPDATE: We’ve got a lot of comments and suggestions on the ‘Unicorn’ naming. We apologize for the confusion and are working on finding a new name. Have a suggestion? Tweet us @mashape
UPDATE #2: We picked a new name, unirest.io
is up and running.
UPDATE #3: Check our latest post on “Unicorn has become Unirest
Here at Mashape, we use a lot of open source. Our tech stack includes a lot of popular frameworks and libraries which have allowed us to rapidly iterate and develop what we feel is the best cloud API proxy and marketplace the universe…
In this short tutorial we will use Python in our Mac Terminal
to consume one of the Machine Learning APIs in this list
. Let’s pick DuckDuckGo
DuckDuckGo lets us define people, places, things, words, and concepts. It also provides a list of related topics. This is very useful if you want to get some context on a certain text.
Although we’re picking DuckDuckGo, note that the steps below also applies to the rest of the APIs in the Machine Learning APIs
list, and all Mashape APIs
. That means you can swap in and swap out APIs for…