Pipelines vs Polyaxon: What are the differences?

The key differences between Pipelines and Polyaxon are outlined below:

1. **Deployment Flexibility**: Pipelines provide a more flexible deployment option as they can be deployed on various platforms like Kubeflow, while Polyaxon is mainly deployed on Kubernetes clusters, limiting its flexibility in deployment choices.

2. **Extensibility**: Pipelines allow for easy integration with various tools and libraries, making it highly extensible for different use cases, whereas Polyaxon is more focused on providing a streamlined platform for machine learning experimentation and development, with less emphasis on extensibility.

3. **Automation Capabilities**: Polyaxon offers more advanced automation capabilities such as hyperparameter tuning, distributed training, and experiment management out of the box, while Pipelines may require additional configurations or integrations for similar level of automation.

4. **User Interface**: Polyaxon provides a comprehensive web-based user interface for monitoring and managing machine learning experiments, while Pipelines may offer a less user-friendly interface depending on the deployment platform and setup.

5. **Version Control Integration**: Pipelines often have better integrations with version control systems like Git for tracking changes in machine learning workflows, while Polyaxon may not have as robust built-in version control features, requiring additional setup for seamless integration.

6. **Scalability**: Polyaxon is designed for scalability and handling large-scale machine learning workloads, offering features like distributed training across multiple GPUs, which may be more challenging to achieve with Pipelines depending on the infrastructure and setup.

In Summary, Pipelines and Polyaxon differ in deployment flexibility, extensibility, automation capabilities, user interface, version control integration, and scalability.