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Data scientists can easily build and deploy high-performance machine learning models with more than 30 scalable algorithms built into Oracle Database.
Explore machine learning techniques and support algorithms (PDF)
Data scientists, and developers are able to process data where it resides to help simplify model building and deployment, reducing application development time, and helping ensure data security.
Data scientists can maximize the timeliness and relevance of machine learning processes by analyzing current business data inside the database.
Data scientists are able to accelerate time to insight by automating commonly required transformations for in-database machine learning algorithms.
Developers achieve immediate machine learning model availability with simple SQL and easy deployment options using representational state transfer (REST) interfaces.
Data scientists, and developers use an easy-to-use, interactive multiuser collaborative interface based on Apache Zeppelin notebook technology, supporting availability for SQL, and PL/SQL interpreters for Oracle Autonomous Database.
Data scientists are able to simplify the creation of machine learning models using familiar SQL and PL/SQL for data preparation, and machine learning model building, evaluation, and deployment inside Oracle Database.
R users gain the performance and scalability of Oracle Database for data exploration, preparation, and machine learning from a well-integrated R interface which helps in easy deployment of user-defined R functions with SQL on Oracle Database.
Data scientists and data analysts can use SQL Developer add-in drag-and-drop interface to explore data and build analytical methodologies (workflows) that can be shared and scheduled to solve data-driven problems.
Data scientists are able to use all nodes of a big data cluster with scalable Spark-based algorithms on data from Hive, Impala, HDFS via an R API for faster model building and data scoring.
Data scientists are able to avoid performance issues during data preparation, model building, and data scoring using the built-in parallelism and scalability of Oracle Database, with optimizations for Oracle Exadata and other environments.
Developers can easily deploy Oracle Machine Learning in diverse applications using multiple types of data, such as spatial and graph data, by leveraging converged Oracle Database capabilities.
Data scientists, and developers can rapidly score large volumes of data using Exadata “smart-scan” technology to deliver faster results.
Data scientists and developers using Oracle Machine Learning are protected with Oracle Database built-in security, encryption, and role-based access to user data and Oracle Machine Learning in-database models.
Customers around the world take advantage of Oracle’s in-database machine learning capabilities to solve complex and important data-driven problems.
Predict customer purchase behavior, attrition, and loan defaults based on a 360-degree view of customers to increase profits and customer satisfaction.
Identify fraud in financial transactions, claims, and expense reports in real-time to reduce risks and losses.
Find hidden opportunities to grow the business with new markets, customer segments, and previously unknown user profiles.
Process large volumes of transactions to identify new offers and upsell opportunities for targeted marketing.
Enhance customer experiences with topic, sentiment, and similarity identification in unstructured text.
Read the latest in a series of blog posts explaining in detail the 6 steps in a machine learning lifecycle. Ideal for non-data scientists who want to understand best practices and get started with Oracle Machine Learning.Read the complete post
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