The document discusses automating machine learning as a system-level service using APIs and RESTful architectures. It outlines workflows utilizing BigML through client-side bindings, highlighting the strengths and challenges of automation, including flexibility and complexity issues. Additionally, it emphasizes the need for efficient, high-level machine learning operations and simplifies processes via one-click automation techniques.

1. Automating Machine Learning
API, bindings, BigMLer and Basic Workflows
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December 2016
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2. Outline
1 Introduction: ML as a System Service
2 ML as a RESTful Cloudy Service
3 Client-side workflows: REST API and bindings
4 Client-side workflows: Bigmler
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3. Outline
1 Introduction: ML as a System Service
2 ML as a RESTful Cloudy Service
3 Client-side workflows: REST API and bindings
4 Client-side workflows: Bigmler
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4. Machine Learning as a System Service
The goal
Machine Learning as a system
level service
The means
• APIs: ML building blocks
• Abstraction layer over feature
engineering
• Abstraction layer over
algorithms
• Automation
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5. The Roadmap
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6. Outline
1 Introduction: ML as a System Service
2 ML as a RESTful Cloudy Service
3 Client-side workflows: REST API and bindings
4 Client-side workflows: Bigmler
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7. RESTful-ish ML Services
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8. RESTful-ish ML Services
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9. RESTful-ish ML Services
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10. RESTful-ish ML Services
• Excellent abstraction layer
• Transparent data model
• Immutable resources and UUIDs: traceability
• Simple yet effective interaction model
• Easy access from any language (API bindings)
Algorithmic complexity and computing resources
management problems mostly washed away
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11. RESTful done right: Whitebox resources
• Your data, your model
• Model reverse engineering becomes
moot
• Maximizes reach (Web, CLI, desktop,
IoT)
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12. Outline
1 Introduction: ML as a System Service
2 ML as a RESTful Cloudy Service
3 Client-side workflows: REST API and bindings
4 Client-side workflows: Bigmler
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13. Higher-level Machine Learning
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14. Example workflow: Batch Centroid
Objective: Label each row in a Dataset with its associated centroid.
We need to...
• Create Dataset
• Create Cluster
• Create BatchCentroid from Cluster
and Dataset
• Save BatchCentroid as new Dataset
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15. Example workflow: building blocks
curl -X POST "https://bigml.io?$AUTH/dataset"
-D '{"source": "source/56fbbfea200d5a3403000db7"}'
curl -X POST "https://bigml.io?$AUTH/cluster"
-D '{"source": "dataset/43ffe231a34fff333000b65"}'
curl -X POST "https://bigml.io?$AUTH/batchcentroid"
-D '{"dataset": "dataset/43ffe231a34fff333000b65",
"cluster": "cluster/33e2e231a34fff333000b65"}'
curl -X GET "https://bigml.io?$AUTH/dataset/1234ff45eab8c0034334"
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16. Example workflow: Web UI
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17. Automation via bindings
from bigml.api import BigML
api = BigML()
project = api.create_project({'name': 'ToyBoost'})
orig_source =
api.create_source(source,
{"name": "ToyBoost",
"project": project['resource']})
api.ok(orig_source)
orig_dataset =
api.create_dataset(orig_source, {"name": "Boost"})
api.ok(orig_dataset)
trainset = api.get_dataset(trainset)
for loop in range(0,10):
api.ok(trainset)
model = api.create_model(trainset, {
"name": "ToyBoost - Model%d" % loop,
"objective_fields": ["letter"],
"excluded_fields": ["weight"],
"weight_field": "100011"})
api.ok(model)
batchp =
api.create_batch_prediction(model, trainset, {
"name": "ToyBoost - Result%d" % loop,
"all_fields": True,
"header": True})
api.ok(batchp)
batchp = api.get_batch_prediction(batchp)
batchp_dataset =
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18. Example workflow: Python bindings
from bigml.api import BigML
api = BigML()
source = 'source/5643d345f43a234ff2310a3e'
# create dataset and cluster, waiting for both
dataset = api.create_dataset(source)
api.ok(dataset)
cluster = api.create_cluster(dataset)
api.ok(cluster)
# create a batch centroid with output to dataset
centroid = api.create_batch_centroid(cluster, dataset,
{'output_dataset': True,
'all_fields': True})
api.ok(centroid)
# wait again, via polling, until the dataset is finished
batch_dataset_id = centroid['object']['output_dataset_resource']
batch_dataset = api.get_dataset(batch_dataset_id)
api.ok(batch_dataset)
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19. Client-side automation via bindings
Strengths of bindings-based solutions
Versatility Maximum flexibility and possibility of encapsulation (via
proper engineering)
Native Easy to support any programming language
Offline Whitebox models allow local use of resources (e.g.,
real-time predictions)
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20. Client-side automation via bindings
Strengths of bindings-based solutions
from bigml.model import Model
model_id = 'model/5643d345f43a234ff2310a3e'
# Download of (whitebox) resource
local_model = Model(model_id)
# Purely local calculations
local_model.predict({'plasma glucose': 132})
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21. Client-side automation via bindings
Problems of bindings-based solutions
Complexity Lots of details outside the problem domain
Reuse No inter-language compatibility
Scalability Client-side workflows are hard to optimize
Not enough abstraction
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22. Outline
1 Introduction: ML as a System Service
2 ML as a RESTful Cloudy Service
3 Client-side workflows: REST API and bindings
4 Client-side workflows: Bigmler
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23. Higher-level Machine Learning
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24. Simple workflow in a one-liner
# 1-clikc cluster
bigmler cluster
--output-dir output/job
--train data/iris.csv
--test-datasets output/job/dataset
--remote
--to-dataset
# the created dataset id:
cat output/job/batch_centroid_dataset
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25. Simple automation: “1-click” tasks
# "1-click" ensemble
bigmler --train data/iris.csv
--number-of-models 500
--sample-rate 0.85
--output-dir output/iris-ensemble
--project "vssml tutorial"
# "1-click" dataset with parameterized fields
bigmler --train data/diabetes.csv
--no-model
--name "4-featured diabetes"
--dataset-fields
"plasma glucose,insulin,diabetes pedigree,diabetes"
--output-dir output/diabetes
--project vssml_tutorial
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26. Rich, parameterized workflows: cross-validation
bigmler analyze --cross-validation # parameterized input
--dataset $(cat output/diabetes/dataset)
--k-folds 3 # number of folds during validation
--output-dir output/diabetes-validation
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27. Rich, parameterized workflows: feature selection
bigmler analyze --features # parameterized input
--dataset $(cat output/diabetes/dataset)
--k-folds 2 # number of folds during validation
--staleness 2 # stop criterium
--optimize precision # optimization metric
--penalty 1 # algorithm parameter
--output-dir output/diabetes-features-selection
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28. Client-side Machine Learning Automation
Problems of client-side solutions
Complex Too fine-grained, leaky abstractions
Cumbersome Error handling, network issues
Hard to reuse Tied to a single programming language
Hard to scale Parallelization again a problem
Hard to generalize CLI tools like bigmler hide complexity at the cost of
flexibility
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29. Client-side Machine Learning Automation
Problems of client-side solutions
Complex Too fine-grained, leaky abstractions
Cumbersome Error handling, network issues
Hard to reuse Tied to a single programming language
Hard to scale Parallelization again a problem
Hard to generalize CLI tools like bigmler hide complexity at the cost of
flexibility
Algorithmic complexity and computing resources management
problems mostly washed away are back!
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30. Questions?
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