AI and machine learning are turning a corner, marking this year with new and improved platforms and use cases. However, database administrators don’t always have the tools and skills necessary to manage this new minefield of technology.
DBTA recently held a webinar featuring Charlie Berger, senior director, product management, machine learning, AI, and, Cognitive Analytics, Oracle who discussed how to gain an attainable, logical, evolutionary path to add machine learning to users’ Oracle data skills.
Operational DBAs spend a lot of time on maintenance, security, and reliability, Berger said. The Oracle Autonomous Database can help. It automates all database and infrastructure management, monitoring, tuning; protects from both external attacks and malicious internal users; and protects from all downtime including planned maintenance.
The Autonomous Database removes tactical drudgery, allowing more time for strategic contribution, according to Berger.
Machine learning allows algorithms to automatically sift through large amounts of data to discover hidden patterns, new insights, and make predictions, he explained.
Oracle Machine Learning extends Oracle Autonomous Database and enables users to build AI applications and analytics dashboards. OML delivers powerful in-database machine learning algorithms, automated ML functionality, and integration with open source Python and R.
From a database developer to a data scientist, Oracle can transform the data management platform into a combined/hybrid data management and machine learning platform.
There are 6 major steps to becoming a data scientist that include:
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling (ML)
- Evaluation
- Deployment
An archived on-demand replay of this webinar is available here.