Oracle Delivers Next Set of Autonomous Cloud Platform Services

As part of its pledge to extend autonomous capabilities across its entire Cloud Platform, Oracle today announced the availability of Oracle Autonomous Analytics Cloud, Oracle Autonomous Integration Cloud, and Oracle Autonomous Visual Builder Cloud. With built-in advanced artificial intelligence (AI) and machine learning algorithms, these groundbreaking new PaaS services automate and eliminate key tasks to enable organizations to lower cost, reduce risk, accelerate innovation, and get predictive insights.

Earlier this year, Oracle made available the world’s first Autonomous Database for data warehouse workloads. The company is committed to adding self-driving, self-securing, and self-repairing capabilities to all of its PaaS services coupled with specific autonomous capabilities for each functional area. In addition to today’s news, Oracle plans to release more autonomous services later in calendar year 2018 focused on mobile and chatbots, data integration, Blockchain, security and management, and additional database workloads, including OLTP.

“Embedding AI and machine learning in these cloud services will help organizations innovate in revolutionary new ways,” said Amit Zavery, executive vice president of development, Oracle Cloud Platform. “These new cloud services are the latest in a series of steps from Oracle to incorporate industry-first autonomous capabilities that will enable customers to significantly reduce operational costs, increase productivity, and decrease risk.”

As organizations focus on delivering innovation fast, they want a secure set of comprehensive, integrated cloud services to build new applications and run their most demanding enterprise workloads. Only Oracle’s cloud services can automate key operational functions like tuning, patching, backups and upgrades while running to deliver maximum performance, high availability, and in demand security features.

Brake Parts Speeds Ahead with Oracle Autonomous Integration Cloud

Brake Parts, a global company that supplies the world’s top brake brands for cars, vans, SUVs, light trucks and heavy duty vehicles, has partnered with Oracle to build an intelligent sales operation. By automating its end-to-end customer change, RFQ, and new product introduction processes, Brake Parts was able to eliminate manual steps and increase customer responsiveness.

“Using Oracle Autonomous Integration Cloud with Oracle Sales Cloud, we were able to modernize our applications and automate customer-facing activities across departments—transforming our sales operations,” said Lisa Oliver, Sales Operations, Brake Parts. “Oracle’s autonomous PaaS services enable us to digitize many of our manual processes so we can decrease costs, increase business insights, and better innovate.”

New Oracle Autonomous Cloud Platform Services

Newly available autonomous services include Oracle Autonomous Analytics Cloud, Oracle Autonomous Integration Cloud, and Oracle Autonomous Visual Builder Cloud. These services help organizations easily build and deploy modern applications, as well as integrate and analyze critical organizational data. 

Oracle Autonomous Analytics Cloud

Oracle Autonomous Analytics Cloud combines machine learning, adaptive intelligence, and service automation to create an analytics platform that breaks down barriers between people, places, data, and systems, fundamentally changing the way people analyze, understand, and act on information.

• Empowers business users to uncover more insights, quickly. Users can ask questions on their mobile devices, and natural language processing converts those questions into queries in the backend to deliver rich visualizations on their device. It uses machine learning to gain intelligence and proactively suggest insight on data the user might not even have asked for.
• Reveals hidden patterns and performance drivers through predictive insights and automatic natural-language explanations powered by machine learning.
• Provides predictive analytics on IoT data applying domain specific machine learning algorithms on large volumes of sensor data and historical patterns of failure.