Leveraging Data Science Stack
Canonical’s DSS streamlines managing the entire life cycle of your machine learning journey, from model development through model deployment.
Developing models
DSS provides a ready-to-use environment for developing machine learning models. You can leverage the power of Jupyter Notebook to write code, experiment with different algorithms, visualize your data, and document your findings in a single, interactive document.
Training models
DSS harnesses MicroK8s’ capabilities to distribute training workloads across multiple nodes. This enables faster and more efficient model training, especially when dealing with large datasets or complex models.
Tracking experiments
Mlflow’s integration with DSS lets you track your experiments, log parameters and metrics, and compare different model versions. This helps you gain a deep understanding of your models’ performance, identify areas for improvement, and make informed...
