Package size: 44.7 Mb

This is an optimized AWS Lambda layer that includes Scikit-Learn 0.24.1, Pandas 1.2.2, Numpy 1.20.1 and SciPy 1.6.1 for Python 3.8 runtime.

Scikit-learn is a machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities. Additionally to Scikit-learn, NumPy and SciPy, this layer includes Pandas as well which all together create a very common and popular bundle that you can now leverage within AWS Lambdas.

Why buy our AWS Lambda layers?

Save time and avoid the pain: The process of building, optimizing, and maintaining a Lambda layer is hard and painful, especially for Machine Learning with Python. It could take between a few days and a couple of weeks for a well-experienced geek.

Benefit from our experience: Our layers have been optimized over the years and extensively tested through thousands of deployments.

Small size: When it comes to a Lambda layer, every bit counts. Our layers are very small what allows more space for your application.

Keep up with the latest versions: We release new layers with the latest versions regularly.

What is an AWS Lambda?

AWS Lambda is a compute service that lets you run code without managing servers. Lambda runs your code only when needed and scales automatically, from a few requests per day to thousands per second, all with zero administration. All you need to do is supply your code.

Check our tutorial (5 min) about how to create and call a simple AWS Lambda function.

What is an AWS Lambda layer?

A layer is a .zip file archive that can be attached to your Lambdas. It contains libraries that become available to your Lambda code in the exact same way virtualenv does.

In theory, you could zip your virtualenv directory and create a Lambda layer with it but in practice isn't that simple. In fact, it’s quite a pain!

Check our tutorial (5 min) about how to create an AWS Lambda function with a layer.

Why use an AWS Lambda for Machine learning?

The simplicity and cost-efficiency of Lambdas make it worthwhile to consider for models deployment and real-time inferencing.

Particularly for Machine Learning and real-time inference, Lambdas functions could be just a few lines of code that can handle millions of requests without the need of investing time and effort in building servers and high availability infrastructures.

Check ours tutorials (5 min) about how to use Scikit-learn and XGBoost within an AWS Lambda.