Скачать или смотреть Analytics and machine learning on aws lambda srv402

Download 1M+ code from https://codegive.com/0d7e349
analytics and machine learning on aws lambda (srv402): a deep dive

this tutorial will guide you through building a serverless analytics and machine learning pipeline on aws lambda, utilizing the srv402 architecture. we'll cover key concepts, code examples, best practices, and potential pitfalls.

*srv402 architecture:*

srv402 refers to the specific aws well-architected framework pillar – **reliability**, and how it applies to serverless (lambda) architectures. it emphasizes the importance of designing lambda functions and related services to be resilient, fault-tolerant, and able to automatically recover from failures. key aspects of srv402 in this context include:

*idempotency:* ensuring functions can be retried without unintended side effects.
*error handling:* implementing robust error handling and retry mechanisms.
*dead letter queues (dlqs):* capturing messages that fail processing for later investigation.
*concurrency control:* managing simultaneous invocations to prevent resource exhaustion and conflicts.
*monitoring and alerting:* tracking key metrics and setting up alerts for anomalies.

*what we'll build:*

we will create a simple system that:

1. *data ingestion:* receives data (simulated in this example) from an external source. we'll use a dummy event trigger.
2. *data processing:* cleans and transforms the data.
3. *model training/inference:* performs either model training (if appropriate given the dataset size limitations of lambda) or more likely, inference with a pre-trained model.
4. *data storage:* stores the processed data and inference results.
5. *monitoring:* logs metrics and alerts on anomalies.

*architecture diagram:*



*prerequisites:*

an aws account.
basic knowledge of python.
basic understanding of aws lambda, s3, dynamodb, cloudwatch, and iam.
aws cli configured with appropriate credentials.
python 3.x installed.

*step 1: setting up the environment*

1. **creat ...

#Analytics #MachineLearning #performancetesting
AWS Lambda
Analytics
Machine Learning
Serverless
Data Processing
Real-time Analytics
Event-driven Architecture
Scalability
Cost-effective
Predictive Analytics
Data Streaming
Lambda Functions
Serverless Framework
Cloud Computing
Automated ML