lines orchestrator, and an ECR container registry.Registering the stack components and creating a ZenML stack.

By following these steps, you can leverage the power of AWS services, such as S3 for artifact storage, SageMaker Pipelines for orchestration, and ECR for container management, all within the ZenML framework. This setup allows you to build, deploy, and manage machine learning pipelines efficiently and scale your workloads based on your requirements.

The benefits of using an AWS stack with ZenML include:

Scalability: Leverage the scalability of AWS services to handle large-scale machine learning workloads.

Reproducibility: Ensure reproducibility of your pipelines with versioned artifacts and containerized environments.

Collaboration: Enable collaboration among team members by using a centralized stack and shared resources.

Flexibility: Customize and extend your stack components based on your specific needs and preferences.

Now that you have a functional AWS stack set up with ZenML, you can explore more advanced features and capabilities offered by ZenML. Some next steps to consider:

Dive deeper into ZenML's production guide to learn best practices for deploying and managing production-ready pipelines.

Explore ZenML's integrations with other popular tools and frameworks in the machine learning ecosystem.

Join the ZenML community to connect with other users, ask questions, and get support.

By leveraging the power of AWS and ZenML, you can streamline your machine learning workflows, improve collaboration, and deploy production-ready pipelines with ease. Happy experimenting and building!

PreviousPopular integrations

NextRun on GCP

Last updated 15 days ago