Oracle Developers | New: Spatial Enhancements in Oracle Machine Learning @oracledevs | Uploaded July 2024 | Updated October 2024, 20 hours ago.
A new spatial enhancement in Oracle Machine Learning enables organizations to include location relationships in their ML models for improved model accuracy. Developers and data scientists can now create machine learning models incorporating location relationships and location-based predictive analysis at scale.
Why does this matter to you?
Data scientists may try to perform such specialized modeling locally; however, that requires moving massive amounts of data, writing complex algorithms, and managing the development environment independently. In contrast, the new Spatial enhancement to Oracle Machine Learning for Python (OML4Py) allows developers to detect spatial patterns through quantitative approaches—such as spatial clustering, regression, classification, and anomaly—without moving the data outside the database. For example, you can now build an ML model that may recognize that transformers in coastal areas are more sensitive to age and moisture, where saltwater effects accelerate degradation.
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0:00 - Intro and overview of Spatial enhancements of Oracle Machine Learning
1:25 Tour of Oracle Machine Learning
4:22 OML4Py Spatial AI capabilities and common scenarios
12:13 OML4Py Spatial AI architecture, feature set, and algorithms
22:50 SpatialDataFrame
27:50 Examples
42:00 Takeaways and resources
43:27 Demo of Example Notebooks in OML4Py
58:03 Wrap up
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Using Oracle Spatial AI on Autonomous Database Serverless: docs.oracle.com/en/cloud/paas/autonomous-database/serverless/cspai
Oracle Spatial AI API: docs.oracle.com/en/cloud/paas/autonomous-database/serverless/saipy
Oracle Spatial technologies: oracle.com/database/spatial
Oracle LiveLabs: bit.ly/golivelabs-spatial
Blog: blogs.oracle.com/database/category/db-spatial
Slack (Please join #spatial channel): bit.ly/Join-ANDOUC-Slack
YouTube: bit.ly/Spatial-Graph-YouTube
AskTOM video series: bit.ly/AskTOMSpatial
LinkedIn: bit.ly/Spatial-Graph-LinkedIn
Twitter: @SpatialHannes, @JeanIhm
A new spatial enhancement in Oracle Machine Learning enables organizations to include location relationships in their ML models for improved model accuracy. Developers and data scientists can now create machine learning models incorporating location relationships and location-based predictive analysis at scale.
Why does this matter to you?
Data scientists may try to perform such specialized modeling locally; however, that requires moving massive amounts of data, writing complex algorithms, and managing the development environment independently. In contrast, the new Spatial enhancement to Oracle Machine Learning for Python (OML4Py) allows developers to detect spatial patterns through quantitative approaches—such as spatial clustering, regression, classification, and anomaly—without moving the data outside the database. For example, you can now build an ML model that may recognize that transformers in coastal areas are more sensitive to age and moisture, where saltwater effects accelerate degradation.
____
0:00 - Intro and overview of Spatial enhancements of Oracle Machine Learning
1:25 Tour of Oracle Machine Learning
4:22 OML4Py Spatial AI capabilities and common scenarios
12:13 OML4Py Spatial AI architecture, feature set, and algorithms
22:50 SpatialDataFrame
27:50 Examples
42:00 Takeaways and resources
43:27 Demo of Example Notebooks in OML4Py
58:03 Wrap up
____
Using Oracle Spatial AI on Autonomous Database Serverless: docs.oracle.com/en/cloud/paas/autonomous-database/serverless/cspai
Oracle Spatial AI API: docs.oracle.com/en/cloud/paas/autonomous-database/serverless/saipy
Oracle Spatial technologies: oracle.com/database/spatial
Oracle LiveLabs: bit.ly/golivelabs-spatial
Blog: blogs.oracle.com/database/category/db-spatial
Slack (Please join #spatial channel): bit.ly/Join-ANDOUC-Slack
YouTube: bit.ly/Spatial-Graph-YouTube
AskTOM video series: bit.ly/AskTOMSpatial
LinkedIn: bit.ly/Spatial-Graph-LinkedIn
Twitter: @SpatialHannes, @JeanIhm
