> For the complete documentation index, see [llms.txt](https://sliu583.gitbook.io/blog/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sliu583.gitbook.io/blog/specific-work/seminar-and-talk/fall-21-reading-list/are-machine-learning-cloud-apis-used-correctly.md).

# Are Machine Learning Cloud APIs Used Correctly?

### Presentation 

* Machine learning provides effective solutions 
* Software development: problems --> bugs 
* ML cloud API
  * Function as a service 
  * Help incorporating learning solutions into software systems 
    * Require less domain knowledge 
    * No need to design and train neural networks 
* ML APIs raise unique challenges 
  * Performing cognitive tasks: how people ask questions greatly affect the result  
  * Largely defined by training data: properties might not be known by API users  
  * Numeric vector output: high-dim, tricky to interpret
  * Complicated accuracy - performance tradeoffs 
*  Corpus 
  * Google / Amazon ML cloud API 
  * 3 ML domains: vision, language, speech 
  * 18 months, size of 2,200 lines 
* Anti-pattern identification methodology
  * Manual examine
  * Design test cases 
  * Report bugs 
* Result 
  * Most applications: misuses! 
  * Pattern
    * Calling the wrong API 
      * Subtle semantics difference among cognitive tasks 
      * e.g. image classification, object detection. Which one to use? 
      * e.g. text-detection, document-text-detection 
      * Escape the traditional testing 
    * Misinterpreting outputs 
      * Numeric vector outputs are difficult to interpret 
    * Misuse of async APIs 
      * complicated accuracy-performance tradeoffs 
    * Necessarily high-resolution inputs 
      * higher resolution - performance degrades 
    * Many other misuses --> what types of impact (reduce functionality, degraded performance, increased cost) 
* Design checkers 
  * Three static analysis tools for three misuses 
  * API wrappers for four misuses