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8+ OpenAPI Linters

#artificialintelligence

OpenAPI Specification can be used to secure and accelerate the API lifecycle. OpenAPI is now a widely-adopted method for describing web APIs. With that fact comes the pressure to validate these specifications are up to date, accurately constructed, and presented for optimal developer usage. Especially with the shift from OpenAPI v2 to v3, developers may require further assistance to ensure their specification matches the current v3 structure and format. Thankfully, there are plenty of open source tools developers can use to lint their API specification against OpenAPI v3 as well as best practices and custom rules.


JSON Schema bundling finally formalised

#artificialintelligence

I've been known to say "If you haven't rewritten your OpenAPI bundling implementation recently, then you don't support OpenAPI 3.1". This observation may be true, but perhaps some more detail would be helpful? When implementing support for OAS 3.1 and JSON Schema draft 2020-12 in oas-kit, reading the sections of the JSON Schema spec on bundling compound documents, I still wasn't totally clear on what was expected of compliant tooling. Thankfully, Ben Hutton is here to set the record straight with a worked example. OpenAPI has long since put the spotlight on JSON Schema, and the release of OpenAPI 3.1 has huge implications for the future of both projects.


Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution

arXiv.org Machine Learning

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs. To build trust with users and reduce potential application risk, it is important to interpret how such predictive models hidden behind APIs make their decisions. The biggest challenge of interpreting such predictions is that no access to model parameters or training data is available. Existing works interpret the predictions of a model hidden behind an API by heuristically probing the response of the API with perturbed input instances. However, these methods do not provide any guarantee on the exactness and consistency of their interpretations. In this paper, we propose an elegant closed form solution named \texttt{OpenAPI} to compute exact and consistent interpretations for the family of Piecewise Linear Models (PLM), which includes many popular classification models. The major idea is to first construct a set of overdetermined linear equation systems with a small set of perturbed instances and the predictions made by the model on those instances. Then, we solve the equation systems to identify the decision features that are responsible for the prediction on an input instance. Our extensive experiments clearly demonstrate the exactness and consistency of our method.


Intelligent Data And Content Streams Using Machine Learning APIs

#artificialintelligence

We have been profiling a number of machine learning APIs lately, not because there is an opportunity to proxy and stream the APIs, but because of the possibilities around applying common machine learning models to the data and content streams our customers are producing. One of the interesting machine learning APIs we are profiling currently is called ParallelDots, which provide a suite of common, yet powerful machine learning models that anyone can integrate into their applications. As we profile the ParallelDots API, we are considering the possibilities for trickling, or streaming updates via the APIs our customer's are proxying using our service. Consider some of the opportunities for posting stream updates to any of the following APIs: - Sentiment - Sentiment API accepts input text, language code and API key to return a JSON response classifying the input text into a sentiment. API can extract this information from any type of text, web page or social media network.


7 Helpful HTTP Tools

#artificialintelligence

An API provider is akin to a handyman. To fix a specific problem, a specific solution is needed, and a skilled handyman is the only one with the experience, the know-how, and the ability to fix these issues. The same is true in the API space. To continue the allegory, just like a handyman has a helpful toolbox of tools to alleviate common gripes and concerns, so too should an API provider have a toolbox full of their own speciality tools. Today, we're going to look at some of these tools, and briefly summarize what they do.