Building a Machine Learning App with AWS Lambda
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The document provides a detailed overview of building a machine learning application using AWS Lambda, highlighting its key features, such as automatic management of compute resources and server maintenance. It outlines the major steps involved, including training a model, exporting it, and deploying it as a Lambda function for domain name classification between legitimate and malicious domains. Additionally, it discusses performance metrics, common errors, pricing, and includes resources for further learning.
![LAMBDA FUNCTION HANDLER
public static ResponseClass myHandler(RequestClass
request, Context context) throws PyException {
PyModule module = new PyModule();
//Prediction code is in pymodule.py
double[]predictions=module.predict(request.domain);
return new ResponseClass(predictions);
}
REST
endpoint
Jython
Feature
Munging
Lambda
Function
Handler
H2O Model
POJO
Prediction](https://image.slidesharecdn.com/buildinganappwithawslambda-160404052951/85/Building-a-Machine-Learning-App-with-AWS-Lambda-10-320.jpg)
![JYTHON FEATURE MUNGING
def predict(domain):
domain = domain.split('.')[0]
row = RowData()
functions = [len, entropy, p_vowels, num_valid_substrings]
eval_features = [f(domain) for f in functions]
names = NamesHolder_MaliciousDomainModel().VALUES
beta = MaliciousDomainModel().BETA().VALUES
feature_coef_product = [beta[len(beta) - 1]]
for i in range(len(names)):
row.put(names[i], float(eval_features[i]))
feature_coef_product.append(eval_features[i] * beta[i])
#prediction
model = EasyPredictModelWrapper(MaliciousDomainModel())
p = model.predictBinomial(row)
REST
endpoint
Jython
Feature
Munging
Lambda
Function
Handler
H2O Model
POJO
Prediction](https://image.slidesharecdn.com/buildinganappwithawslambda-160404052951/85/Building-a-Machine-Learning-App-with-AWS-Lambda-11-320.jpg)
![H2O MODEL POJO
• static final class BETA_0 implements java.io.Serializable {
static final void fill(double[] sa) {
sa[0] = 1.49207826021648;
sa[1] = 2.8502716978560194;
sa[2] = -8.839804567200542;
sa[3] = -0.7977065034624655;
sa[4] = -14.94132841574946;
}
}
REST
endpoint
Jython
Feature
Munging
Lambda
Function
Handler
H2O Model
POJO
Prediction](https://image.slidesharecdn.com/buildinganappwithawslambda-160404052951/85/Building-a-Machine-Learning-App-with-AWS-Lambda-12-320.jpg)
Building a Machine Learning App with AWS Lambda
- 2. BUILDING A MACHINE LEARNING APPLICATION WITH AWS LAMBDA Q: What is AWS Lambda? A: AWS Lambda is a compute service that runs code – a Lambda function - on-demand. It simplifies the process of running code in the cloud by managing compute resources automatically. Offloads DevOps tasks related to VMs: • Server and operating system maintenance • Capacity provisioning • Scaling • Code monitoring and logging • Security patches
- 3. MAJOR STEPS Step 1: Identify problem to solve Step 2: Train model on data Step 3: Export the model as a POJO Step 4: Write code for Lambda handler Step 5: Build deployment package (.zip file) and upload to Lambda Step 6: Map API endpoint to Lambda function Step 7: Embed endpoint in application
- 4. A CONCRETE USE CASE: DOMAIN NAME CLASSIFICATION Malicious domains • Carry out malicious activity - botnets, phishing, malware hosting, etc • Names are generated by algorithms to defeat security systems Goal: Classify domains as legitimate vs. malicious Legitimate Malicious h2o zyxgifnjobqhzptuodmzov zen-cart c3p4j7zdxexg1f2tuzk117wyzn fedoraforum batdtrbtrikw
- 5. FEATURES • String length • Shannon Entropy o Measure of uncertainty in a random variable • Number of substrings that are English words • Proportion of vowels
- 6. DATA • Domains and whether they are malicious o http://datadrivensecurity.info/blog/data/2014/10/legit-dga_domains.csv.zip o 133,927 rows • English words o https://raw.githubusercontent.com/dwyl/english-words/master/words.txt o 354,985 rows
- 7. MODEL INFORMATION Malicious Domain Model Algorithm: GLM Model family: Binomial Regularization: Ridge Threshold (max F1): 0.4935 Class 0 1 Error 0 15889 315 FPR 0.0194 1 346 10043 FNR 0.0333 Confusion matrix on validation data Actual Predicted
- 10. LAMBDA FUNCTION HANDLER public static ResponseClass myHandler(RequestClass request, Context context) throws PyException { PyModule module = new PyModule(); //Prediction code is in pymodule.py double[]predictions=module.predict(request.domain); return new ResponseClass(predictions); } REST endpoint Jython Feature Munging Lambda Function Handler H2O Model POJO Prediction
- 11. JYTHON FEATURE MUNGING def predict(domain): domain = domain.split('.')[0] row = RowData() functions = [len, entropy, p_vowels, num_valid_substrings] eval_features = [f(domain) for f in functions] names = NamesHolder_MaliciousDomainModel().VALUES beta = MaliciousDomainModel().BETA().VALUES feature_coef_product = [beta[len(beta) - 1]] for i in range(len(names)): row.put(names[i], float(eval_features[i])) feature_coef_product.append(eval_features[i] * beta[i]) #prediction model = EasyPredictModelWrapper(MaliciousDomainModel()) p = model.predictBinomial(row) REST endpoint Jython Feature Munging Lambda Function Handler H2O Model POJO Prediction
- 13. HANDS-ON DEMONSTRATION STEP 1: Build $ git clone https://github.com/h2oai/app-malicious-domains $ cd app-consumer-loan $ gradle wrapper $ ./gradlew build STEP 2: Create Lambda function and set API endpoint See instructions and screenshots in README.md STEP 3: Use the app in a web browser $ ./gradlew jettyRunWar –x generateModel http://localhost:8080
- 14. TROUBLESHOOTING • Common Py errors o Another H2O is already running • Py script can’t find the data in h2o.import_file() • Common Java errors o Java not installed at all • Also, must install a JDK (Java Development Kit) so that the Java compiler is available (JRE is not sufficient) o Not connected to the internet • Gradle needs to fetch some dependencies from the internet • Common Lambda errors o Error in uploading .zip file • Check if the function already exists and, if not, try again. For slower internet connections, try uploading .zip file with S3 link. o Timeout error when testing Lambda function • Go to advanced settings and increase Timeout value o Gateway Timeout (504 error) • This is Lambda’s cold start behavior. Keep trying, eventually Lambda kicks in
- 15. CAVEATS • Stateless o Can access stateful data by calling other web services, such as Amazon S3 or Amazon DynamoDB. • Cold start behavior o containers are instantiated and reused after the first request and stay active for a window of time (10-20 minutes) o “the longer I leave it between invocations, the longer the function takes to warm up” • API Gateway timeout of 10 secs o Can request longer timeout
- 16. CONFIGURING LAMBDA FUNCTIONS • Memory o Allocates proportional CPU power, network bandwidth, and disk I/O o Easy single-dial solution o Log shows how much memory was used for tuning and cost savings • Timeout
- 17. LAMBDA RESOURCE LIMITS Resource Default Limit Memory 512 MB Number of file descriptors 1,024 Number of processes and threads (combined total) 1,024 Maximum execution duration per request 300 seconds Invoke request body payload size 6 MB Invoke response body payload size 6 MB Concurrent executions per region 100 Item Default Limit Lambda function deployment package size (.zip/.jar file) 50 MB Size of code/dependencies that you can zip into a deployment package (uncompressed zip/jar size) 250 MB
- 18. LAMBDA PRICING • Lambda o Requests • First 1 million per month are free • $0.20 per 1 million requests thereafter o Duration • First 400,000 GB-seconds of compute time per month are free • $0.00001667 for every GB-second thereafter • API Gateway o $3.50 per million API calls received plus data transfer costs • Estimate for Malicious Domain Application: • Lambda: $0.37/hour with 10 threads after free-tier • API Gateway: $0.71/hour • Total: ~$1/hr
- 19. LAMBDA PERFORMANCE Memory (MB) Threads Loops Samples Median (ms) Min (ms) Max (ms) % Error Throughput (calls/sec) 512 1 10000 10000 102 85 2137 0 8.4 512 10 1000 10000 102 85 30330 0.18 44 512 100 100 10000 149 85 30307 0.43 168
- 20. LAMBDA SCALING • Automatically scales to support the rate of incoming requests • “No limit to the number of requests your code can handle” • Starts as many instances of Lambda function as needed
- 21. RELATED EXAMPLES • H2O Generated Model POJO in a Java Servlet container o Github: h2oai/app-consumer-loan • H2O Generated Model POJO in a Storm bolt o GitHub: h2oai/h2o-world-2015-training o tutorials/streaming/storm • H2O Generated Model POJO in Spark Streaming o GitHub: h2oai/sparkling-water o examples/src/main/scala/org/apache/spark/examples/h2o /CraigslistJobTitlesStreamingApp.scala
- 22. RESOURCES ON THE WEB • Slides o GitHub h2oai/h2o-tutorials/tree/master/tutorials/aws-lambda-app • Source code o GitHub h2oai/app-malicious-domains • Latest stable H2O for Python release o http://h2o.ai/download/h2o/python • Generated POJO model Javadoc o http://h2o-release.s3.amazonaws.com/h2o/rel-turan/3/docs- website/h2o-genmodel/javadoc/index.html • AWS Lambda o http://docs.aws.amazon.com/lambda/latest/dg/welcome.html
- 23. Q & A • Thanks for attending! • Send follow up questions to: Ludi Rehak ludi@h2o.ai