I'm working on an application that is a "predictive-model-as-a-service", structured as follows: train a model offline periodically upload model parameters to a
rowcount
xctestcase
vimgrep
odbc
np-complete
tymon-jwt
executemany
range-based-loop
ts-morph
arithmetic-overflow
sqljdbc
lossless-compression
wininet
pbiviz
gwmodel
appixia
gcp-databricks
cyclic-reference
timetk
query-help
icacls
transparentproxy
pipes-filters
nano-server
gkmatchmaker
jsr310
visualsvn
app-lab
transcrypt
mailmessage