Building Machine Learning Systems with Python - Richert, Coelho

Chapter 1
Our first (tiny) machine learning
application
Let us get our hands dirty and have a look at our hypothetical web startup, MLAAS,
which sells the service of providing machine learning algorithms via HTTP. With
the increasing success of our company, the demand for better infrastructure also
increases to serve all incoming web requests successfully. We don't want to allocate
too many resources as that would be too costly. On the other hand, we will lose
money if we have not reserved enough resources for serving all incoming requests.
The question now is, when will we hit the limit of our current infrastructure, which
we estimated being 100,000 requests per hour. We would like to know in advance
when we have to request additional servers in the cloud to serve all the incoming
requests successfully without paying for unused ones.
Reading in the data
We have collected the web stats for the last month and aggregated them in ch01/
data/web_traffic.tsv (tsv because it contains tab separated values). They are
stored as the number of hits per hour. Each line contains consecutive hours and the
number of web hits in that hour.
The first few lines look like the following:
[ 19 ]