45 points by cloud-guru 1 year ago flag hide 18 comments
serverless_enthusiast 1 year ago next
This is a great case study on how to optimize serverless architecture for ML workloads! I'm curious about the approach used for data preprocessing and feature engineering?
ml_engineer 1 year ago next
We leveraged AWS Lambda and TensorFlow to handle real-time data preprocessing. We also used Amazon SageMaker to perform feature engineering tasks in our serverless pipeline.
another_user 1 year ago prev next
Very interesting! Would love to learn more about the challenges faced during the switch from traditional architecture to serverless.
serverless_enthusiast 1 year ago next
One of the main challenges was managing dependencies in a serverless environment. We used AWS Lambda layers to manage dependencies and simplify the architecture.
cloud_expert 1 year ago prev next
Impressive case study! Have you considered using containerization for your serverless ML workloads? Containerization can help with better dependency management and resource utilization.
serverless_enthusiast 1 year ago next
Yes, we explored that option. However, we wanted to maximize infrastructure cost savings without sacrificing performance, and we achieved that by going completely serverless.
cost_optimization_guru 1 year ago prev next
Cost optimization was a notable concern during this study. How have you optimized the AWS Lambda invocation charges and other associated costs?
serverless_enthusiast 1 year ago next
We used AWS's provisioned concurrency feature to reduce Lambda's cold start times, keep responses within SLAs, and minimize invocation charges. We also utilized reserved instances for other supporting services like ECS and EKS.
machine_learning_intern 1 year ago prev next
This is very helpful! I am planning to implement serverless architecture for ML workloads, would you recommend any particular AWS service?
aws_certified_expert 1 year ago next
Absolutely! AWS Lambda combined with Amazon SageMaker is a powerful solution for serverless ML. SageMaker handling ML model training and deployment while Lambda is responsible for preprocessing, triggering the job, and post-processing tasks.
architecture_design_ninja 1 year ago prev next
Have you deployed the application behind Application Load Balancer or API Gateway? How do you maintain states between Lambda functions?
serverless_enthusiast 1 year ago next
We deployed the application behind Amazon API Gateway, enabling serverless computing with AWS Lambda functions. To maintain the state between Lambda functions, we used AWS Step Functions for state management.
another_user 1 year ago prev next
Querying data from databases often becomes a bottleneck in serverless architecture. How did you address this problem?
serverless_enthusiast 1 year ago next
We utilized a hybrid approach by leveraging AWS Lambda with DynamoDB for low-latency real-time data access and Amazon S3 for moderately-accessed historical data.
ml_beginner 1 year ago prev next
Serverless technology looks promising for ML projects. How long did it take for the whole infrastructure migration process? Did you have any fail-safes along the way?
serverless_enthusiast 1 year ago next
It took us around 6 months to complete the migration, with proper planning, and a set of fail-safes including canary releases, gradual switch, circuit breakers, and comprehensive testing.
data_obsessed 1 year ago prev next
Security is critical in ML projects. Can you share details on how you approached authentication and authorization within your serverless architecture?
serverless_enthusiast 1 year ago next
We used AWS Cognito for user authentication and integration with Amazon API Gateway for access control. Server-side encryption was used for securing data stored in Amazon S3, and SSL certificates were used for HTTPS.