machine learning pipelines runner on spark and image classification and retrieval implementation

1. SPARK MBUTO
Design & Engineering Machine Learning Pipelines
Gianvito Siciliano
Use Case: Image Classification and Retrieval

2. OUTLINE
1. Spark ‘Mbuto intro
2. ML problems overview
3. Classification & retrieval logic
4. Classification Models
5. Image Pipeline

3. OUTLINE
1. Spark ‘Mbuto intro
• Abstractions
• Basic Examples
2. ML problems overview
3. Classification & retrieval logic
4. Classification Models
5. Image Pipeline

4. SPARK MBUTO
• Spark poc to (easy) create, run and test pipelines and
workflow
• Pipelines are made by sequential steps in a SparkJobApp
• Each steps is a SparkJob
• Each job share the same Spark/SQL context
• Jobs are consecutively run by JobRunner

5. SPARKJOB

6. JOBRUNNER

7. SPARKJOBAPP

8. PIPELINE
App .main
JobRunner .run
Job
Job
.execute
.execute
next job

9. JOB READYTO USE

10. READABLE APP
App .main
JobRunner .run
Job
Job
.execute
.execute
next job

11. PERFORMANCE LOOKUP
A
JobR
J
J

12. OUTLINE
1. Spark ‘Mbuto intro
2. ML problems overview
• Classification
• Retrieval
3. Classification & retrieval logic
4. Classification Models
5. Image Pipeline

13. IMAGE CLASSIFICATION
• Multiclass image classification:
1. Choose model (NN, SVM,TREE…)
2. Train/test model (with labeled images)
3. Predict the label of new images
4. Tune the model

14. IMAGE RETRIEVAL
• Multiclass image classification:
1. Choose metric (Euclidean, cosine…)
2. Build dictionary
3. Train/test the model
4. Query and search
5. Tune the model

15. WHAT CHANGES?
• Pipelines architecture
• Classification logic
• How to update the model?

16. CLASSIFICATION PIPELINE
DATA
TRAIN
CLASSIFIER
MODEL
NEW
DATA
PREDICTION

17. RETRIEVAL PIPELINE
DATA
TRAIN
CLASSIFIER
MODEL QUERY
PREDICTION

18. OUTLINE
1. Spark ‘Mbuto intro
2. ML problems overview
3. Classification & retrieval logic
4. Classification Models
5. Image Pipeline

19. CLASSIFICATION & RETRIEVAL
• Keypoints extraction from each images
• Clustering on the keypoints universe
• Represent each image with weighted cluster
vector
• Train &Test the model
• Query the model (finding the most similar
images)
Features
Engineering
Build the
Dictionary
Build the
classifier
Query
the model

20. C. & R. JOBS
• Load whole dataset
• Extract keypoints
• Reduce the keypoints universe
• Transform the features space
• Create the dictionary (aka Codebook)
• Train, test & evaluate the classifier
• Query and get prediction
DATA
TRAIN
CLASSIFIER
MODEL
PREDICTION

21. KMeans
CLASSIFIER
Image
LOADER
.transform
Sift
EXTRACTOR
KMeans
QUANTISER
.fit
CLUSTERS
CfIif
TRANSFORMER
ClusterVector
PIVOTER
CODEBOOK
Features
Engineering
Build the
Dictionary
DICTIONARY
TRANSFORMER
ESTIMATOR

22. Vector
ASSEMBLER
.transform
Label
INDEXER
KNN
CLASSIFIER
.fit
.transform
.fit
KMeans
CLASSIFIER
TRAIN TEST
.split
EVALUATOR
Train
classifier
Evaluate
classifier
INSAMPLE
PREDICTION
OUTSAMPLE
PREDICTION
CLASSIFIER
TRANSFORMER
ESTIMATOR

23. KNN IMPLEMENTATION
• Is a comparison model: the similarity metric is crucial!
• Nearest Neighbour search (in the codebook) is the panic point:
• KDTree: not parallel (anche se…)
• LSH: hyperparams difficult to tune
• MetricTree: disjoint features points area
• Spill tree: too many shared points
=> HybridTree

24. HYBRIDTREE
• TopTree is a Metric tree
• SubLeaf Tree are Spill tree, trained in parallel
• Nodes can be:
• OVERLAP => defeatist search
• NON OVERLAP => backtracking

25. NEURAL NETWORK
• Convolutional works well with images
• Hyperparameters tuning is the panic point, but can
be automatised (guarda il nuovo algo)
• Training is not trivial, update the model is easy to
complain

26. WHAT MORE?
• Features engineering
• Hyperparameters tuning
• Parallel optimizations
• Persist/update steps
• Ensemble models
DATA
Combiner
PREDICTION
Normalizer
pipelineModel
Cross
Validator

27. https://github.com/gianvi
Thanks!