The document discusses the construction of machine learning (ML) pipelines, detailing steps such as data normalization, noise removal, and feature extraction before training models. It emphasizes the importance of cross-validation and hyper-parameter optimization to ensure the effectiveness of the models used. Additionally, it highlights customizable metrics and configurable data sources that enhance ML pipeline performance.

1. Building ML Pipelines
qplum_team qplum.co info@qplum.co

2. What do ML Pipelines Look Like?

3. TRAINING
DATA
AWESOME
ML
TECHNIQUE
MODEL
TESTING DATA
PREDICTIONS

4. Let’s build one now!

5. UserID Pet Children Salary
1 cat 4 90
2 dog 6 24
3 dog 3 44
4 fish 3 27
5 cat 2 32
6 dog 3 59
7 cat 5 36
8 fish 4 27
Predict the salary from the kind of pets and the number of children a person has

6. You may need to:
1. Binarize/normalize data
2. Remove noise
3. Reduce dimensionality of data
4. Make features from raw data
…
before you get to train your model !!

7. C D F N S
1 0 0 0.21 90
0 1 0 1.88 24
0 1 0 -0.63 44
0 0 1 -0.63 27
1 0 0 1.46 32
0 1 0 -0.63 59
1 0 0 1.04 36
0 0 1 0.21 27
Neural Net
Training Set
YX
Model

8. But is this enough?

9. No ML Pipeline is complete without
Cross-validation and Hyper-parameter optimization

10. So how does our ML Pipeline look now?

11. RAW
DATA
AWESOME
ML
TECHNIQUE
with
PARAMETERS 1
BEST MODEL
TESTING DATA
PREDICTIONS
PRE-
PROCESSED
DATA
EXTRACT
FEATURES
TRAINING DATA
AWESOME
ML
TECHNIQUE
with
PARAMETERS K
AWESOME
ML
TECHNIQUE
with
PARAMETERS N

12. What does ‘best’ model mean?

13. ML Pipeline in Code

14. Series of transformations
Transformations might involve making models
Models can be used to transform or predict
Grid-search on Parameters

15. >>> clf.set_params(svm__C=10)
Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None, whiten=False)),
('svm', SVC(C=10, cache_size=200, class_weight=None,
coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True, tol=0.001,
verbose=False))])
>>> from sklearn.grid_search import GridSearchCV
>>> params = dict(reduce_dim__n_components=[2, 5, 10],
... svm__C=[0.1, 10, 100])
>>> grid_search = GridSearchCV(clf, param_grid=params)

16. Extra features:
Configurable data sources
Customized scoring metrics(average, median of results
etc.)

17. Customize cross-validation based on nature of data
How do you cross-validate on time-series data?

18. Why use ML pipelines?

19. DRY

20. Libraries with ML Pipelines
Sci-kit Learn, Pandas and Scikit-Mapper
Sparks MLLib
Write your own!!

21. qplum_team qplum.co info@qplum.co
Thanks for coming!
Debidatta Dwibedi
@debidatta

22. qplum_team qplum.co info@qplum.co
EXTRA

23. UserID Pet Children Salary
1 cat 4 90
2 dog 6 24
3 dog 3 44
4 fish 3 27
5 cat 2 32
6 dog 3 59
7 cat 5 36
8 fish 4 27
Need to binarize this column Might also want to normalize this column

24. Is Pet a Cat? Is Pet a Dog? Is Pet a Fish? Normalized number of children
1 0 0 0.21
0 1 0 1.88
0 1 0 -0.63
0 0 1 -0.63
1 0 0 1.46
0 1 0 -0.63
1 0 0 1.04
0 0 1 0.21