The Platform's Application Services
The platform's application development ecosystem includes
- Distributed data frameworks and engines — such as Spark, Presto, Horovod, and Hadoop.
- The Nuclio serverless framework.
- Enhanced support for time-series databases (TSDBs) — including a CLI tool, serverless functions, and integration with Prometheus.
- 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.