Workflow · workflow
Serverless deployment of a KNN image classifier on AWS Lambda using Docker and SAM
Traditional cloud deployments like AWS Elastic Beanstalk require always-active provisioned resources (EC2 instances, Elastic Load Balancers) even when idle, burdening developers with infrastructure management overhead and unnecessary cost.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Train and serialize KNN model
A K-nearest neighbour classifier is trained on the MNIST dataset and prepared for deployment as a docker container.
Tools used
AWS SAMDockerAWS S3API Gatewayboto3Amazon ECR
Outcome
A KNN classifier achieving 96% cross-validation accuracy on MNIST was successfully containerized with Docker and deployed as a serverless AWS Lambda function, accessible via an API Gateway endpoint and invocable on demand.
What failed first
The previous approach using AWS Elastic Beanstalk was largely automated but kept servers provisioned continuously, making it less cost-effective for infrequent ML prediction workloads.
Results
Volume96%
Grounding & classification
Source type: technical build writeup
14 fields verified against source quotes, 2 dropped as unverifiable.
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