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AWS Lambda: Advanced Coding Session (slides)

Live demos:

1. Amazon API Gateway Access Control
2. Amazon Kinesis Streams processing
3. Amazon Cognito Sync trigger
4. AWS CloudFormation Custom Resources

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import boto3

iam = boto3.resource('iam')

def get_policy_body(arn, version_id=None):
    """ Return IAM Policy JSON body """
    if version_id:
        version = iam.PolicyVersion(arn, version_id)
    else:
        policy = iam.Policy(arn)

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Amazon Rekognition - Python Code Samples

1. Labels Detection
2. Faces Detection
3. Faces Comparison
4. Faces Indexing
5. Faces Search

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def lambda_handler(event, context):
    name = event.get('name') or 'World'
    print("Name: %s" % name)
    return "Hello %s!" % name

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#!/bin/bash

BUCKET="YOUR_BUCKET_NAME" # bucket name
FILENAME="deployment-package.zip" # upload key
TMP_FOLDER="/tmp/lambda-env-tmp/" # will be cleaned
OUTPUT_FOLDER="/tmp/lambda-env/" # will be cleaned


HERE=${BASH_SOURCE%/*} # relative path to this file's folder
LAMBDA_FOLDER="$HERE/lambda/" # relative path

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def from_sklearn(docs,vect,lda,**kwargs):
    
    """Create Prepared Data from sklearn's vectorizer and Latent Dirichlet
    Application
    
    Parameters
    ----------
    docs : Pandas Series.
        Documents to be passed as an input.
    vect : Scikit-Learn Vectorizer (CountVectorizer,TfIdfVectorizer).

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# List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

# Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

# Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(valuelist)]

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import os
import math
import re
import pandas as pd
from collections import Counter
from sklearn.datasets import fetch_20newsgroups

#get a subset of the dataset

categories = [