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Suricate

  1. 1. Suricate Open Data Science Platform http://www.engjoy.eu
  2. 2. tl;dr Suricate allows: ● you to upload or stream data ● a Data Scientist to perform Analytics (using Python or …) and visualize the results. ● supports the processing and decision making (trigger actions) on the results of the analysis. Thereby creation of continuously adjusted agile Systems & Strategies based on up-to-date insights.
  3. 3. Data Science Platform ProcessingAnalyticsData Aggregate Interact Act Compute Aggregate Interact Compute Act Stream data into the service or upload the data in an internal or external Storage (Object, Relational, etc). The data can then be aggregated, preprocessed and cached. Use Python interactive scripting capabilities to perform analytics and visualize the results. The Data Scientist can interact with the Service via a Web UI or REST API. Uses Cloud Computing features to perform the computational aspect of the analytics. Process the learned models and trigger actions on insights gained, to create agile Systems & Strategies.
  4. 4. ProcessingAnalyticsData Aggregate Interact Act Compute Stream or Upload ● Compute Cloud ● Hadoop as a Service ● ... Agile Strategies or Systems Browser RESTful API
  5. 5. Use case: NFL statistics Suricate Service live game feed 4. stream game analysis game processing5. analyze stream 7. trigger analysis Suggestions 2. analyze game statistics 3. provide model 6a. suggest game statistics 6b. update statistics Data Scientist 1. Provide user defined logic and user defined data.
  6. 6. Suricate’s architecture Suricate Open Data Science Service Web based UI Storage Data Scientist 1. Provide user defined logic and user defined data. RESTful API API Execution node (per tenant)Execution node (per tenant)Execution node (per tenant) Messaging Data Source Stream/Upload
  7. 7. Suricate as Cloud Service
  8. 8. Features ● Runs as a service in the cloud with user isolation, scaling features etc. ● Provides ways of uploading / streaming of data ● Organization of Python/R/Julia notebooks in projects ● Within the interactive Python notebooks the definition of user-defined logic for analytics and processing (decision taking) of data is supported ○ Supports many existing packages for visualization and analytics like Pandas, scikit-learn, NumPy, … ● Offers an RESTful API to support machine based interactions to trigger the analytics and processing ● Provides hooks to interact with external systems ● Easy to deploy (as WSGI application) into existing private and public clouds ● (Optional) Integration with Hadoop and IaaS based service to enhance the computational aspects ● ...
  9. 9. Resources mailto: info@engjoy.eu http://www.engjoy.eu

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