Machine
Learning
Deployment

Deploy projects developed in Python, to Kubernetes

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Bodywork terminal demo.

Serve Models, Execute Batch Jobs and Run Complex Pipelines

Automate repetitive DevOps tasks and free your team to focus on machine learning problems.

With just one Command

From your terminal, you can serve models, schedule batch jobs or deploy complex ML pipelines, using the Bodywork CLI.

Bodywork command line interface (CLI).

Easy to Configure

Avoid countless hours writing and debugging Kubernetes manifests.

Deploy Direct from Git

Avoid needing to build and manage your own Docker images.

Scalable and Resilient

Automatic retires and roll-backs by default. Services can be backed by multiple container replicas to handle high request volumes.

Open Source

Built and maintained by machine learning engineers, for machine learning engineers, and committed to remaining 100% open-source.

Download

Bodywork is distributed as a Python package, exposing a CLI for configuring Kubernetes to run Bodywork deployments - get it here:

GitHub logo.
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FAQ

How does Bodywork compare against AWS SageMaker or Microsoft AzureML?

SageMaker and AzureML are powerful platforms for machine learning. Understanding how to use them requires significant investment, together with a willingness to adopt their workflows, APIs and tools. Bodywork is focused only on machine learning deployment. It is easy to configure and imposes no constraints on how models, services and workflows can be engineered. Using Bodywork for deployment allows you to retain the freedom to use any component you choose to integrate into your MLOps stack. You also retain the freedom to deploy to any cloud platform and can even test entire deployments locally - something that is not currently possible with SageMaker or AzureML.

How does Bodywork compare to Kubeflow?

Kubeflow is a collection of cloud native technologies for machine learning, that have been loosely coupled together into a platform for machine learning on Kubernetes. The learning curve is steep, as it requires an understanding of how to use and adopt each component to meet your requirements. It assumes basic knowledge of Kubernetes and users are also responsible for building and maintaining the Docker images that define Kubeflow pipelines. By contrast, Bodywork does not require machine learning engineers to build and maintain Docker images or become comfortable with Kubernetes manifests. Instead, Bodywork will pull your codebase into pre-built containers and deploy workflows based on a single file of project-level configuration. You retain full-flexibility in how pipelines and services can be engineered, fitting Bodywork around your codebase for deployment.

Do I need to have experience with Kubernetes to use Bodywork?

No. One of Bodywork’s objectives is to bridge the gap between machine learning code and machine learning deployments. Having some understanding of Kubernetes is useful, but it isn’t necessary.

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