Item type | Current library | Collection | Shelving location | Call number | Copy number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|---|
Books | BITS Pilani Hyderabad | 003-007 | General Stack (For lending) | 006.31 HEL-H (Browse shelf(Opens below)) | INR 999.00 | Available | 47542 |
Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle’s Autonomous Database cloud offering. The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book focuses on Oracle Machine Learning as part of the Oracle Autonomous Database collaborative environment. Also covered are advanced topics such as delivery and automation pipelines.
Throughout the book you will find practical details and hand-on examples showing you how to implement machine learning and automate deployment of machine learning. Discussion around the examples helps you gain a conceptual understanding of machine learning. Important concepts discussed include the methods involved, the algorithms to choose from, and mechanisms for process and deployment. Seasoned database professionals looking to make the leap into machine learning as a growth path will find much to like in this book as it helps you step up and use your current knowledge of Oracle Database to transition into providing machine learning solutions.
You will:
* Use the Oracle Machine Learning (OML) Notebooks for data visualization and machine learning model building and evaluation
* Understand Oracle offerings for machine learning
* Develop machine learning with Oracle database using the built-in machine learning packages
* Develop and deploy machine learning models using OML4SQL and OML4R
* Leverage the Oracle Autonomous Database and its collaborative environment for Oracle Machine Learning
* Develop and deploy machine learning projects in Oracle Autonomous Database
* Build an automated pipeline that can detect and handle changes in data/model performance
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