New capabilities in Oracle Machine Learning (OML), including the Data Science Agent, are making it easier to develop, evaluate, and scale predictive models. In this webinar, we’ll explore how AI-assisted guidance and in-database machine learning can help teams work with large volumes of data, build models efficiently, and assess performance through a familiar SQL interface.
OML has been part of Oracle’s machine learning portfolio for more than 25 years. With each release, it has added new algorithms, expanded SQL-based machine learning functions, and introduced integrations with OCI services such as Oracle Analytics Cloud. This case study demonstrates how these capabilities were applied to a real-world challenge: processing large datasets and building models for a large number of watersheds.
Whether you’re new to OML or already exploring Oracle’s machine learning solutions, you’ll see how OML and the Data Science Agent can simplify model development, performance assessment, and scalable machine learning workflows.
Agenda:
- What’s new in OML, including the Data Science Agent and AI-assisted model development
- An overview of OML and its role within Oracle’s broader machine learning portfolio
- Scaling model development across large volumes of data and many watersheds
- Using the Data Science Agent to guide model building and assess performance
- Lessons learned and practical takeaways
Duration: 1 hour
