A Performance Tester adds value by –
Recording/Automating each module and running the flow for concurrent users according to future expected load.
Documenting which request is taking more time and concurrency errors, if any.
Analyzing possible causes of slowness and errors – for instance whether it is related to source code, database query, front end and/or environment.
Examining whether the current system resources like CPU Cores, RAM, disk space are sufficient for current as well as future expected load.
Finding the Breaking point of the application server with reason(s) of failure.
Checking your server, database configuration for optimization.
Running the performance tests again after the code changes to make sure the shortcomings are all addressed.
IBM Watson
Keras
TensorFlow
Microsoft Azure
PyTorch
DMTK
DL4J
H2O
Apache Spark
Mahout
Open AI
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