Presenting a blog series encompassing diverse topics related to leveraging AI for business applications. Our goal is to offer insights into harnessing the potential of AI within Fusion SaaS. The following parts have been published, with more exciting content to come. Enjoy your reading journey!
Part 1: Using SaaS Data
Explore various options for extracting data from Fusion, providing a foundation for training robust data models.
Part 2 : Model data preparation using OCI Data Science
This part is about model data preparation using OCI Data Science from the files stored in the Object Storage bucket.
Part 3: Text classification without Training Dataset
Discover an innovative approach to text classification through the utilization of embeddings and cosine similarity, eliminating the need for a traditional training dataset.
Part 4: Using SaaS data with LangChain Prompt Templates for Few-Shot learning
This part showcases a RAG Pattern using LLM , VectorDB, and LangChain to facilitate Few-Shot learning.
Part 5: Modelling using a SaaS Data Pool on Premise
Gain insights into utilizing an on-premise Oracle DB for complex data modeling using a SaaS Data Pool on-premise.
Part 6: Modelling using a SaaS Data Pool on OCI
Gain insights into utilizing an on-premise Oracle DB for complex data modeling using a SaaS Data Pool on OCI.
Part 7: Enabling MLOps using OCI Data Science Pipelines
Read about using OCI Data Science Pipelines to streamline end-to-end machine learning workflows as part of enabling MLOps.
Part 8: OCI Generative AI Integration with LangChain Use Cases
Explore how to enable AI in Fusion Applications using OCI Generative AI Large Language Models and LangChain integration.
Part 9: Using Vector Embeddings and Classification models within Oracle Machine Learning(OML) in Database
Read about using Oracle Machine Learning (OML) capabilities for generating vector embeddings and training models.
Thanks to Ulrich Janke, Dipak Chhablani, Lyudmil Pelov, and Magesh Kesavapillai for contributing to this blog series.