Integrations#

HPE Machine Learning Development Environment seamlessly integrates with popular ML ecosystem tools to enhance your model training workflow. From data transformation to monitoring and alerting, our integrations help streamline your ML pipeline.

Key Integrations#

  • Data Transformation: Integrate with tools like Pachyderm to streamline your data preprocessing.

  • Development Environments: Use HPE Machine Learning Development Environment shells in popular IDEs, similar to remote SSH hosts. Learn more at IDEs.

  • Workload Alerting: Set up Workload Alerting through webhooks to stay informed about your experiments in real-time. For a comprehensive overview of notification options, see Notifications.

  • Monitoring: Enable Grafana dashboards to monitor hardware and system metrics on cloud clusters. See Prometheus & Grafana for details.

Getting Started#

To make the most of these integrations, we recommend using the Python SDK Client Module Reference to interact with HPE Machine Learning Development Environment.

Explore More#

Visit our Works with HPE Machine Learning Development Environment repository for examples of using HPE Machine Learning Development Environment with various ML ecosystem tools, including:

  • HPE Machine Learning Data Management

  • DVC

  • Delta Lake

  • Seldon

  • Spark

  • Argo

  • Airflow

  • Kubeflow

These examples demonstrate how HPE Machine Learning Development Environment can enhance your existing ML workflows and tools.