Performance of Machine Learning Webservice API

Hi Team,
I have some questions about the performance of Machine Learning Web Service API.
I've recently grabbed the tweeter sentiment analysis model sample and tested the web service performance in both single request/response and batch execution mode. Based on the result, single request API takes about 5 seconds to process one
tweet, which is not applicable to real-time processing as the speed that tweet get generated is much faster than this. So I have to buffer tweet stream and send batch execution requests which takes about 0.25 sec per tweet when sending 500 tweets per batch.
The questions are:
1. Given a task such as tweeter sentiment analysis, can the API running under single request mode do better than a few seconds per record, in order to enable real-time processing? Currently I am using the free version ML service.
Could standard version have better performance in terms of processing single web service request?
2. How to evaluate the performance and estimate the execution time/pricing of a model built on Azure ML? From the pricing web page it says "The best way to estimate prediction API call times is to benchmark a model on the Machine Learning
service.". Can anyone explain this a bit more in details about how to "benchmark a model" ?
Thanks

Hi Dexter,
It is likely that the initial calls to the web service are triggering model initialization and are taking longer than steady state calls. To measure the performance of a fully initialized service - you would need to send multiple concurrent requests at once.
There is an unofficial tool that helps you do this today:
https://github.com/paveldournov/RRSWarmer but you can do that with simple client code as well.
There is an option in the ML APIs to send multiple entries (tweets in your case) in a single request to the Request/Response service. In this case - all of the entries will be sent to the model at once, which can be faster per each tweet, while it will not
require asynchronous batch processing. Sending 500 entries per call in this case should not be a problem.
The free workspace allows you to build services that can scale only to 4 concurrent calls at a time. A paid workspace will allow scaling this to 20 concurrent calls by default, and to 200 with an API call to adjust the max concurrent calls. The performance
of an individual call should not change with going from Free to Paid, but the amount of call volume and rps is up to 50 times more in the Paid case.
Re. the pricing - the resources on this page are outlining the key pricing points:
http://azure.microsoft.com/en-us/pricing/details/machine-learning/ . The API pricing is composed out of two components - the number of calls and the time to process the calls.
I'd recommend to setup a web service and do perf testing of the service from a single client sending serialized requests. This will allow you to measure the compute time per call. Then a simple multiplication by the expected number of calls should give
an idea about the total cost.
Thanks!

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