├── .gitignore ├── LICENSE ├── README.md ├── build_model.py ├── layers ├── pandas │ ├── python │ │ └── lib │ │ │ └── python3.6 │ │ │ └── site-packages │ │ │ └── keep_file_structure.txt │ └── requirements.txt └── sklearn │ ├── python │ └── lib │ │ └── python3.6 │ │ └── site-packages │ │ └── keep_file_structure.txt │ └── requirements.txt ├── pandas_lambda_function.py └── zip_layers.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Build and Release Folders 2 | bin-debug/ 3 | bin-release/ 4 | [Oo]bj/ 5 | [Bb]in/ 6 | 7 | # Other files and folders 8 | .settings/ 9 | 10 | # Executables 11 | *.swf 12 | *.air 13 | *.ipa 14 | *.apk 15 | 16 | # Project files, i.e. `.project`, `.actionScriptProperties` and `.flexProperties` 17 | # should NOT be excluded as they contain compiler settings and other important 18 | # information for Eclipse / Flash Builder. 19 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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Thank god. 3 | But now someone wants to use your model. That's great news ain't it?  4 | It definitely is, but a whole new problem comes up. You've gotta put it some where and make it available to other people in the business.  5 | This tutorial will show you how to use AWS to do this. In specific we'll be using the following components: 6 | API gateway 7 | S3 buckets 8 | Lambda functions/lambda layers 9 | 10 | I'm not using Sagemaker because it can be quite expensive £100 p/m per model opposed to lambda functions where you just pay per request. 11 | You'll need a few things to complete this. You'll need an installation of Python (scikit learn, pandas etc) and Docker installed. 12 | Docker will be needed to build the correct lambda layer files if you are on a Windows machine (I learned this the hard way) -------------------------------------------------------------------------------- /build_model.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | from sklearn.model_selection import train_test_split 4 | from sklearn.linear_model import ElasticNet 5 | from sklearn.pipeline import Pipeline 6 | from sklearn.compose import ColumnTransformer 7 | from sklearn.preprocessing import OneHotEncoder 8 | from sklearn.impute import SimpleImputer 9 | from sklearn.externals import joblib 10 | from sklearn.metrics import r2_score 11 | 12 | import zipfile 13 | # Defining global variables 14 | 15 | rand_seed = 42 16 | 17 | # Reading in the data 18 | 19 | df = pd.read_csv('uk-housing-prices-paid/price_paid_records.csv', low_memory=True) 20 | print(df.columns.tolist()) 21 | 22 | # Dropping all columns instead of price, property type, old/new, duration. 23 | # Some of these columns have odd input values, but since this is just a test example I'm not too worried about that. 24 | 25 | df = df[['Price', 'Property Type', 'Old/New', 'Duration']] 26 | 27 | # Outputting the possible values for the inputs because we'll need to know. 28 | 29 | print(df['Property Type'].value_counts()) 30 | print(df['Old/New'].value_counts()) 31 | print(df['Duration'].value_counts()) 32 | 33 | # Splitting the data into train/test before building the model 34 | 35 | y = df['Price'] 36 | X = df.drop('Price', axis=1) 37 | 38 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=rand_seed) 39 | 40 | # Building a pipeline to handle build a simple model. I'm just building a simple model, it probably won't be that accurate but that's OK. 41 | 42 | cat_featuress = ['Property Type', 'Old/New', 'Duration'] 43 | cat_feat_processer = Pipeline(steps=[ 44 | ('imputer', SimpleImputer(strategy='constant', fill_value='missing')), 45 | ('onehot', OneHotEncoder(handle_unknown='ignore'))] 46 | ) 47 | 48 | preprocessor = ColumnTransformer( 49 | transformers=[ 50 | ('cat', cat_feat_processer, cat_featuress)]) 51 | 52 | model = Pipeline(steps=[ 53 | ('preprocessor', preprocessor), 54 | ('regressor', ElasticNet(random_state=rand_seed)) 55 | ] 56 | ) 57 | 58 | model.fit(X_train, y_train) 59 | preds = model.predict(X_test) 60 | 61 | print(r2_score(y_test, preds)) 62 | # Ha thank god this isn't a Kaggle competition, this model is horrible. 63 | 64 | joblib.dump(model, 'house_price_model.pkl') 65 | 66 | # Zipping model file for the lambda layers 67 | zipfile.ZipFile('model.zip', mode='w').write("house_price_model.pkl") 68 | -------------------------------------------------------------------------------- /layers/pandas/python/lib/python3.6/site-packages/keep_file_structure.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/evanmiller29/aws_model_blog_post/49255b150eb6e3d2e9f78055df5dad2392c86289/layers/pandas/python/lib/python3.6/site-packages/keep_file_structure.txt -------------------------------------------------------------------------------- /layers/pandas/requirements.txt: -------------------------------------------------------------------------------- 1 | pandas==0.24.1 2 | pytz -------------------------------------------------------------------------------- /layers/sklearn/python/lib/python3.6/site-packages/keep_file_structure.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/evanmiller29/aws_model_blog_post/49255b150eb6e3d2e9f78055df5dad2392c86289/layers/sklearn/python/lib/python3.6/site-packages/keep_file_structure.txt -------------------------------------------------------------------------------- /layers/sklearn/requirements.txt: -------------------------------------------------------------------------------- 1 | scikit-learn==0.20.3 -------------------------------------------------------------------------------- /pandas_lambda_function.py: -------------------------------------------------------------------------------- 1 | import json 2 | import pandas as pd 3 | 4 | 5 | def lambda_handler(event, context): 6 | 7 | print((pd.DataFrame(data={'col1': [1, 2, 3, 4]}))) 8 | -------------------------------------------------------------------------------- /zip_layers.py: -------------------------------------------------------------------------------- 1 | # Code stub taken from https://stackoverflow.com/questions/1855095/how-to-create-a-zip-archive-of-a-directory 2 | 3 | import os 4 | import zipfile 5 | 6 | def zipdir(path, ziph): 7 | # ziph is zipfile handle 8 | for root, dirs, files in os.walk(path): 9 | for file in files: 10 | ziph.write(os.path.join(root, file)) 11 | 12 | base_dir = os.getcwd() 13 | # I hate doing this but you need to actually change your working directory to the folder you need 14 | # otherwise the zip will include subfolders for all folders between your current working directory 15 | # and the directory you want to zip 16 | 17 | os.chdir('layers/pandas') 18 | zipf = zipfile.ZipFile('pandas_lambda.zip', 'w', zipfile.ZIP_DEFLATED) 19 | zipdir('python/', zipf) 20 | zipf.close() --------------------------------------------------------------------------------