Simon Kneller

Simon Kneller

Head of Industrial Analytics & IoT and Principal Consultant Data Science at esentri AG

Posts by Tags

AWS

Using AWS Lambda with custom container image for Machine Learning inference

8 minute read

Published:

Using serverless offerings from cloud providers has many advantages like no provisioning and managing of servers and automatic scaling. But serverless functions also come with some limitiations like complicated deployment package management, limited package size and limited RAM. Especially if you want to use lambda functions for Machine Learning model inference, these limitations can be quite restrictive. AWS also recognized that so since December 2020 they extended their lambda offering by container image support. In this post I’ll describe my experiences using aws lambda with custom container images and describe the ups and downs of this approach.

Custom image support

Using AWS Lambda with custom container image for Machine Learning inference

8 minute read

Published:

Using serverless offerings from cloud providers has many advantages like no provisioning and managing of servers and automatic scaling. But serverless functions also come with some limitiations like complicated deployment package management, limited package size and limited RAM. Especially if you want to use lambda functions for Machine Learning model inference, these limitations can be quite restrictive. AWS also recognized that so since December 2020 they extended their lambda offering by container image support. In this post I’ll describe my experiences using aws lambda with custom container images and describe the ups and downs of this approach.

Docker

Using AWS Lambda with custom container image for Machine Learning inference

8 minute read

Published:

Using serverless offerings from cloud providers has many advantages like no provisioning and managing of servers and automatic scaling. But serverless functions also come with some limitiations like complicated deployment package management, limited package size and limited RAM. Especially if you want to use lambda functions for Machine Learning model inference, these limitations can be quite restrictive. AWS also recognized that so since December 2020 they extended their lambda offering by container image support. In this post I’ll describe my experiences using aws lambda with custom container images and describe the ups and downs of this approach.

ECR

Using AWS Lambda with custom container image for Machine Learning inference

8 minute read

Published:

Using serverless offerings from cloud providers has many advantages like no provisioning and managing of servers and automatic scaling. But serverless functions also come with some limitiations like complicated deployment package management, limited package size and limited RAM. Especially if you want to use lambda functions for Machine Learning model inference, these limitations can be quite restrictive. AWS also recognized that so since December 2020 they extended their lambda offering by container image support. In this post I’ll describe my experiences using aws lambda with custom container images and describe the ups and downs of this approach.

Homematic IP

IoT

Lambda

Using AWS Lambda with custom container image for Machine Learning inference

8 minute read

Published:

Using serverless offerings from cloud providers has many advantages like no provisioning and managing of servers and automatic scaling. But serverless functions also come with some limitiations like complicated deployment package management, limited package size and limited RAM. Especially if you want to use lambda functions for Machine Learning model inference, these limitations can be quite restrictive. AWS also recognized that so since December 2020 they extended their lambda offering by container image support. In this post I’ll describe my experiences using aws lambda with custom container images and describe the ups and downs of this approach.

Serverless

Using AWS Lambda with custom container image for Machine Learning inference

8 minute read

Published:

Using serverless offerings from cloud providers has many advantages like no provisioning and managing of servers and automatic scaling. But serverless functions also come with some limitiations like complicated deployment package management, limited package size and limited RAM. Especially if you want to use lambda functions for Machine Learning model inference, these limitations can be quite restrictive. AWS also recognized that so since December 2020 they extended their lambda offering by container image support. In this post I’ll describe my experiences using aws lambda with custom container images and describe the ups and downs of this approach.

iobroker

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