In September, Oracle unveiled a self-sufficient autonomous database. This database automatically manages provisioning, patching, and maintenance. Oracle announced today that it is bringing these autonomous capabilities to its PaaS offerings.
Artificial intelligence and machine learning have been the talk of the IT community for years and it seems like 2018 could be their launch pad. The Oracle Cloud Platform will have machine learning and AI for all its services. This will help clients reduce cost, lower risk, accelerate internal innovation, and get predictive insights.
Oracle said that their PaaS services will be self-driving, self-securing, and self-repairing. They aim to eliminate monotonous day-to-day management issues. Oracle’s cloud services can automate tuning, patching, backups, and upgrades. The platform is also adding autonomous capabilities to application development, mobile and bots, app and data integration, analytics, security, and management.
Oracle President of Product Development, Thomas Kurian, stated, “The future of tomorrow’s successful enterprise IT organization is in full end-to-end automation. We are weaving autonomous capabilities into the fabric of our cloud to help customers safeguard their systems, drive innovation, and deliver the ultimate competitive advantage.”
Automation can be intimidating for IT professionals, but Oracle emphasizes that automation will be used to eliminate time spent on monotonous tasks. It will allow IT professionals to focus on more pressing work. I recently wrote an article regarding skills that IT professionals must learn moving forward. One of these skills is robotic process automation (RPA). The entire IT industry is built on technological progress, gaining knowledge about automation is essential to maintaining personal success.
The press release listed some specific benefits that this platform will provide enterprises and IT professionals:
- Automated artifact discovery, dependency management, and policy-based dependency updates increasing code quality and developer productivity
- Automated identification and remediation of security issues throughout the CI/CD pipeline significantly reducing security risks
- Automated code generation with single button deployment enabling rapid application development even by line of business users
Mobile and Bots
- Self-learning chatbots observing interaction patterns and preferences to automate frequently performed end-user actions freeing up time for higher productivity tasks
- Unsupervised, smart bots using machine learning to learn from user conversations enabling fluid, contextual conversations
- Automated caching of API calls to the nearest data center in real time for lowest latency responses based on end-user location
Application and Data Integration
- Self-defining integrations automate business processes across different SaaS and on-premises apps
- Self-defining data flow with automated data lake and data prep pipeline creation for ingesting data (streaming and batch)
- Automated data discovery and preparation
- Automated analysis for key findings along with visualization and narration delivering quicker real-time insights
Security and Management
- Machine learning-driven user and entity behavior analytics to automatically isolate and eliminate suspicious and malicious users
- Preventative controls to intercept data leaks across structured and unstructured data repositories
- Unified data repository across log, performance, user experience and configuration data with applied AI/ML, eliminating need to set and manage performance and security monitoring “metadata” such as thresholds, server topology, and configuration drift
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