Integrated energy data science platform
A method implements an integrated energy data science platform. The method includes presenting a data discovery component, including a map view to select data using the map view. The method further includes presenting a model selection component to select a machine learning model configured with deployment settings and configured to use the data, wherein the deployment settings identify sample features of the data. The method further includes authorizing access to the machine learning model and the data, deploying the machine learning model using the deployment settings, and presenting results generated from the sample features using the machine learning model, wherein the sample features are extracted from the data.
1 . A method comprising:
presenting a map on a graphical user interface;
presenting energy data at a geographical location on the map, wherein the energy data includes spatial coordinates of the energy data, the energy data selectable using the map;
based on a data selection of the energy data on the map, presenting a window overlaid over the map, the window including a plurality of machine learning models configured with deployment settings and configured to use the energy data, wherein the deployment settings identify sample features of the energy data;
based on a model selection of a machine learning model of the plurality of machine learning models at the window, authorizing access to the machine learning model and the energy data;
deploying the machine learning model using the deployment settings; and
presenting results generated from the sample features using the machine learning model, wherein the sample features are extracted from the energy data.
2 . The method of claim 1 , further comprising presenting a model lineage, wherein the model lineage identifies training steps and training data used to generate the machine learning model.
3 . The method of claim 1 , further comprising presenting a data lineage, wherein the data lineage identifies data sources and machine learning models used to generate the results.
4 . The method of claim 1 , further comprising periodically retraining the machine learning model.
5 . The method of claim 1 , further comprising registering the machine learning model with a publication service.
6 . The method of claim 1 , further comprising publishing the machine learning model subsequent to presenting the window.
7 . The method of claim 1 , further comprising deploying the machine learning model as a microservice.
8 . The method of claim 1 , further comprising executing the machine learning model to generate the results from the energy data.
9 . The method of claim 1 , further comprising presenting the results with a dashboard.
10 . The method of claim 1 , further comprising transmitting the results to a domain application.
11 . The method of claim 1 , wherein the map includes a geographic information system (GIS) map.
12 . A system comprising:
a processor and memory, the memory including instructions that cause the processor to:
present a map on a graphical user interface;
present energy data at a geographical location on the map, wherein the energy data includes spatial coordinates of the energy data, the energy data selectable using the map;
based on a data selection of the energy data on the map, present a window overlaid over the map, the window including a plurality of machine learning models configured with deployment settings and configured to use the energy data, wherein the deployment settings identify sample features of the energy data;
based on a model selection of a machine learning model of the plurality of machine learning models at the window, authorize access to the machine learning model and the energy data;
deploy the machine learning model using the deployment settings; and
presenting results generated from the sample features using the machine learning model, wherein the sample features are extracted from the energy data.
13 . The system of claim 12 , wherein the instructions further cause the processor to present a model lineage, wherein the model lineage identifies training steps and training data used to generate the machine learning model.
14 . The system of claim 12 , wherein the instructions further cause the processor to present a data lineage, wherein the data lineage identifies data sources and machine learning models used to generate the results.
15 . The system of claim 12 , wherein the instructions further cause the processor to periodically retrain the machine learning model.
16 . The system of claim 12 , wherein the instructions further cause the processor to register the machine learning model with a publication service.
17 . The system of claim 12 , wherein the instructions further cause the processor to publish the machine learning model subsequent to presenting the window.
18 . The system of claim 12 , wherein the instructions further cause the processor to deploy the machine learning model as a microservice.
19 . The system of claim 12 , wherein the instructions further cause the processor to execute the machine learning model to generate the results from the energy data.
20 . The system of claim 12 , wherein the instructions further cause the processor to present the results with a dashboard.