Analytics and Data Summit 2019

Oracle has been embedding machine learning algorithms into the SQL kernel of the Oracle Database for many years. Oracle Advanced Analytics 18c delivers significant performance gains and new algorithms including Random Forests, Neural Networks, Unsupervised Feature Selection, Partitioned models and Market Basket Analysis improvements. Data scientists can build models in-database on billions of records in minutes and “scores” millions of records in seconds. Now, Oracle Machine Learning, bundled in the Oracle Autonomous Database, adds collaborative machine learning notebooks for data scientists. Oracle also has added a Python API (OML4Py), ORAAH and new Cognitive Analytics for images, text and deploying models as Microservices. Oracle “moves the algorithms to the data” for hybrid data management and machine learning platforms that enable enterprise-wide machine learning model deployment. Come hear about the latest developments and what’s coming next from Oracle.

Learn the fast and easy way to get started performing machine learning with Oracle SQL Developer 18.3’s Oracle Data Miner drag-and-drop workflow UI. In this introductory hands-on lab, learn from experts who will provide one-on-one coaching, guidance, and instruction as requested in several easy to follow Hands-On-Lab Tutorials. Learn how to explore data and apply machine learning algorithms for common ML use cases e.g. prediction, clustering, key factors identification, anomaly detection and market basket analysis. Follow step-by-step tutorial instructions in a machine learning-friendly environment. Ask questions, get answers and come away with basic understanding and a comfort level for using Oracle’s machine learning capabilities on your own data and use cases.

This session focuses on how Oracle is applying various Machine Learning technologies to the area of AI Opts to prevent performance issues and maintain availability. Details about the architectures and algorithms used for this Autonomous Health initiative will be covered for both cloud and on-premise database deployments. Specific operational use cases will be covered detailing the strategies and algorithms used to proactively detect performance degradation through the phases of detection, root-cause analysis and targeted corrective actions. The first section focuses on detection based upon data from the operating system and software layers and how normality can be determined and deviations detected in realtime with the goal of preserving session, database, and infrastructure availability. The second section focuses on detection using log and trace file data were the analysis is based on anomalous events or log record signatures. The final section will cover integration into DevOps processes.

Most machine learning projects fail to go beyond the data scientist. They hit a wall when they try to “operationalize” the ML models into enterprise production IT environments. Open source Python and R are great for data scientists but create problems with the DBA. This dichotomy between IT and data science is eliminated when the ML algorithms are part of the enterprise data management platform. By “moving algorithms to the data”, Oracle has extended the Oracle Database, Autonomous Database and Big Data platforms into hybrid data management and machine learning platforms. Data scientists and application developers can use their language or UI of choice (R, Python, SQL, “drag and drop” UIs and notebooks) to cooperatively build, develop and deploy ML models on premise or in the cloud. Learn how data scientists move beyond “ML tools” and now collaborate with IT, DBAs and application developers to deliver ML enabled applications. Several “Predictive Applications” will be shown.

Python has become an extremely popular scripting language for working with geospatial data. A rich ecosystem of Python geospatial libraries provides algorithms to answer questions such as “How are outcomes correlated with location?” “What areas are the most or least similar?” and “Where are the hotspots?” Using a dataset of nationwide traffic accident data, this presentation shows you how to address these questions by combining the geospatial features of popular Python libraries and Oracle Database.