Deploy AutoML as a service using Flask

Last update: Nov 04, 2022

Related tags

Overview

AutoML Service

Deploy automated machine learning (AutoML) as a service using Flask, for both pipeline training and pipeline serving.

The framework implements a fully automated time series classification pipeline, automating both feature engineering and model selection and optimization using Python libraries, TPOT and tsfresh.

Check out the blog post for more info.

Resources:

TPOT– Automated feature preprocessing and model optimization tool
tsfresh– Automated time series feature engineering and selection
Flask– A web development microframework for Python

Architecture

The application exposes both model training and model predictions with a RESTful API. For model training, input data and labels are sent via POST request, a pipeline is trained, and model predictions are accessible via a prediction route.

Pipelines are stored to a unique key, and thus, live predictions can be made on the same data using different feature construction and modeling pipelines.

An automated pipeline for time-series classification.

The model training logic is exposed as a REST endpoint. Raw, labeled training data is uploaded via a POST request and an optimal model is developed.

Raw training data is uploaded via a POST request and a model prediction is returned.

Using the app

View the Jupyter Notebook for an example.

Deploying

# deploy locally
python automl_service.py

# deploy on cloud foundry
cf push

Usage

Train a pipeline:

train_url = 'http://0.0.0.0:8080/train_pipeline'
train_files = {'raw_data': open('data/data_train.json', 'rb'),
               'labels'  : open('data/label_train.json', 'rb'),
               'params'  : open('parameters/train_parameters_model2.yml', 'rb')}

# post request to train pipeline
r_train = requests.post(train_url, files=train_files)
result_df = json.loads(r_train.json())

returns:

{'featureEngParams': {'default_fc_parameters': "['median', 'minimum', 'standard_deviation', 
                                                 'sum_values', 'variance', 'maximum', 
                                                 'length', 'mean']",
                      'impute_function': 'impute',
                      ...},
 'mean_cv_accuracy': 0.865,
 'mean_cv_roc_auc': 0.932,
 'modelId': 1,
 'modelType': "Pipeline(steps=[('stackingestimator', StackingEstimator(estimator=LinearSVC(...))),
                               ('logisticregression', LogisticRegressionClassifier(solver='liblinear',...))])"
 'trainShape': [1647, 8],
 'trainTime': 1.953}

Serve pipeline predictions:

serve_url = 'http://0.0.0.0:8080/serve_prediction'
test_files = {'raw_data': open('data/data_test.json', 'rb'),
              'params' : open('parameters/test_parameters_model2.yml', 'rb')}

# post request to serve predictions from trained pipeline
r_test  = requests.post(serve_url, files=test_files)
result = pd.read_json(r_test.json()).set_index('id')

example_id	prediction
1	0.853
2	0.991
3	0.060
4	0.995
5	0.003
...	...

View all trained models:

r = requests.get('http://0.0.0.0:8080/models')
pipelines = json.loads(r.json())

{'1':
    {'mean_cv_accuracy': 0.873,
     'modelType': "RandomForestClassifier(...),
     ...},
 '2':
    {'mean_cv_accuracy': 0.895,
     'modelType': "GradientBoostingClassifier(...),
     ...},
 '3':
    {'mean_cv_accuracy': 0.859,
     'modelType': "LogisticRegressionClassifier(...),
     ...},
...}

Running the tests

Supply a user argument for the host.

# use local app
py.test --host http://0.0.0.0:8080

# use cloud-deployed app
py.test --host http://ROUTE-HERE

Scaling the architecture

For production, I would suggest splitting training and serving into seperate applications, and incorporating a fascade API. Also it would be best to use a shared cache such as Redis or Pivotal Cloud Cache to allow other applications and multiple instances of the pipeline to access the trained model. Here is a potential architecture.

A scalable model training and model serving architecture.

Author

Chris Rawles

Deploy AutoML as a service using Flask

Related tags

Overview

AutoML Service

Architecture

Using the app

Deploying

Usage

Running the tests

Scaling the architecture

Author

Owner

Chris Rawles

Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale.

Ml based project which uses regression technique to predict the price.

PySpark + Scikit-learn = Sparkit-learn

50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

This machine learning model was developed for House Prices

Pandas Machine Learning and Quant Finance Library Collection

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Distributed Deep learning with Keras & Spark

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

LibTraffic is a unified, flexible and comprehensive traffic prediction library based on PyTorch

A simple guide to MLOps through ZenML and its various integrations.

Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing

30 Days Of Machine Learning Using Pytorch

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

ANNchor is a python library which constructs approximate k-nearest neighbour graphs for slow metrics.

cleanlab is the data-centric ML ops package for machine learning with noisy labels.

使用数学和计算机知识投机倒把

A toolkit for making real world machine learning and data analysis applications in C++

NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.