The Platform's Application Services
The platform's application development ecosystem includes
- Distributed data frameworks and engines — such as Spark, Trino, Horovod, and Hadoop.
- The Nuclio serverless framework.
- Jupyter Notebook and for development and testing of data science and general data applications.
- A web-based shell shell) service and Jupyter terminals, which provide bash command-line shells for running application services and performing basic file-system operations.
- Integration with popular Python machine-learning and scientific-computation packages for development of ML and artificial intelligence (AI) applications — such as TensorFlow, Keras, scikit-learn, pandas, PyTorch, Pyplot, and NumPy.
- Integration with common Python libraries that enable high-performance Python based data processing — such as Dask and RAPIDS.
- Support for Data Science Automation (MLOps) Services using the MLRun library and Kubeflow Pipelines — including defining, running, and tracking managed, scalable, and portable ML tasks and full workflow pipelines.
- The V3IO Frames open-source unified high-performance DataFrame API library for working with NoSQL, stream, and time-series data in the platform.
- Support for executing code over GPUs.
- Integration with data analytics, monitoring, and visualizations tools — including built-in integration with the open-source Grafana metric analytics and monitoring tool and easy integration with commercial business-intelligence (BI) analytics and visualization tools such as Tableau, Looker, and QlikView.
- Logging and monitoring services for monitoring, indexing, and viewing application-service logs — including a log-forwarder service and integration with Elasticsearch.
For basic information about how to manage and create services in the dashboard, see Working with Services. For detailed service specifications, see the platform's Support and Certification Matrix.