From DS to MLOPs

From Data Science to MLOPs workshop


Breast Cancer Wisconsin (Diagnostic) Data Set

For this workshop we are going to work with the following dataset:

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.
n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: “Robust Linear Programming Discrimination of Two Linearly Inseparable Sets”, Optimization Methods and Software 1, 1992, 23-34].

Attribute Information:

  1. ID number
  2. Diagnosis (M = malignant, B = benign)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area – 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension (“coastline approximation” – 1)

Virtual Environment

Firt we need to create a virtual environment for the project, to keep track of every dependency, it is also useful to use and explicit version of Python

Install the package for creating a virtual environment:
$ pip install virtualenv

Create a new virtual environment
$ virtualenv venv

Activate virtual environment
$ source venv/bin/activate

Python packages

Now with the virtual environment we can install the dependencies written in requirements.txt

$ pip install -r requirements.txt


After we have install all the dependencies we can now run the script in code/, this script takes the input data and outputs a trained model and a pipeline for our web service.

$ python code/

Web application

Finally we can test our web application by running:

$ flask run -p 5000


Now that we have our web application running, we can use the Dockerfile to create an image for running our web application inside a container

$ docker build . -t from_ds_to_mlops

And now we can test our application using Docker

$ docker run -p 8000:8000 from_ds_to_mlops


Test by using the calls in tests/example_calls.txt from the terminal


View Github