Advanced usage
While Canonical DSS streamlines the entire life cycle, making it easy to create reliable and reproducible machine learning development environments, it does not limit your capabilities. You can easily scale up your workflow to meet your needs:
- Customizing environments: Tailor your DSS environment to your needs by adding new libraries, frameworks, and tools. This flexibility allows you to create a personalized data science workspace that caters to your unique requirements.
- Integrating with other tools: Seamlessly integrate DSS with other data science tools and platforms, such as cloud-based services, data pipelines, and specialized libraries. This expands the capabilities of DSS and allows you to build comprehensive data science workflows.
- Scaling for production: You can quickly scale your DSS environment by adding more nodes to your MicroK8s cluster for production deployments. This ensures that your data science applications can handle increasing...
