Efsync My First Open-source MLOps toolkit

Efsync My First Open-source MLOps toolkit

Efsync My First Open-source MLOps toolkit. Automatically Sync Python Dependencies and ML models to AWS EFS for your AWS Lambda function. Part of using Machine Learning successfully in production is the use of MLOps. MLOps enhances DevOps with continuous training (CT).

Automatically Sync Python Dependencies and ML models to AWS EFS for your AWS Lambda function.

Introduction

Part of using Machine Learning successfully in production is the use of MLOps. MLOps enhances DevOps with continuous training (CT). The main components of MLOps therefore include continuous integration (CI), continuous delivery (CD), and continuous training (CT).  Nvidia wrote an article about what MLOps is in detail.

My Name is Philipp and I live in Nuremberg, Germany. Currently, I am working as a machine learning engineer at a technology incubation startup. At work, I design and implement cloud-native machine learning architectures for fin-tech and insurance companies. I am a big fan of Serverless and providing machine learning models in a serverless fashion. I already wrote two articles about how to use Deep Learning models like BERT in a Serverless Environment like AWS Lambda.

A big hurdle to overcome in serverless machine learning with tools like  AWS Lambda,  Google Cloud Functions,  Azure Functions was storage.  Tensorflow and  Pytorch are having a huge size and newer “State of the Art” models like BERT have a size of over 300MB.

In July this year, AWS added support for Amazon Elastic File System (EFS), a scalable and elastic NFS file system for AWS Lambda. This allows us to mount AWS EFS filesystems to  AWS Lambda functions.

Until today it was very difficult to sync dependencies or model files to an AWS EFS Filesystem. You could do it with  AWS Datasync or you could start an EC2 instance in the same subnet and VPC and upload your files from there.

For this reason, I have built an MLOps toolkit called efsync. Efsync is a CLI/SDK tool, which syncs files from S3 or local filesystem automatically to AWS EFS and enables you to install dependencies with the AWS Lambda runtime directly into your EFS filesystem. The CLI is easy to use, you only need access to an AWS Account and an AWS EFS-filesystem up and running.

aws devops aws-lambda serverless machine-learning

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Setup —Serverless Machine Learning Inference with AWS Lambda + Amazon EFS

A step-by-step tutorial to set up ML inferences with AWS Lambda using its newly released integration with Amazon Elastic File System.

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Serverless ML. Is this the cheapest way to host Machine Learning models on AWS?

Serverless ML. Is this the cheapest way to host Machine Learning models on AWS? I’m hosting my Resnet-152 (152 layer CNN) model on AWS for pretty much free right now. Does it scale? What’s the trade-offs? This is where “serverless” comes in.