Business case for Oracle AI Cloud

Oracle’s Platform as a Service (PaaS) will now feature artificial intelligence (AI) capabilities. This platform provides fast and easy access to machine learning and data science capabilities from the cloud. As Oracle is embracing Open Source in this AI Cloud it brings together the power of the scientific and data science community and the strength of Oracle with all its integration capabilities.

This blog post is the first of a series in which we will look into different aspects of this upcoming Oracle AI Cloud. Although Oracle never provides promise dates, delivery will likely either be linked to a major event, or come as a surprise. Either way, we can already deduce quite a bit about the new offering from the short description on the Oracle PaaS Cloud website.

Open Source

The basis for the Oracle AI Platform is Open Source, supported by various data science and machine learning libraries that are part of the Python ecosystem. Oracle benefits here from all the work that is already done by the scientific and data science communities, as many of the libraries with complex functionality are already available. Oracle included the most prominent of these AI and machine learning capabilities into three areas:

Libraries and Tool, containing the Python libraries that are crucial for complex operations on large data sets
Deep learning Frameworks, with Tensorflow / Keras originating from Google supporting neural networks for deep analysis of data
Elastic AI and machine learning Infrastructure underpins the platform with a rich set of high performance components

Oracle rightly chose to reuse what is already available in the market with regards to AI and machine learning and combine that with its own strong integration capabilities. This platform delivers the capabilities to extend and improve existing cloud and on-premises applications based on data and usage figures. Three use cases demonstrate these capabilities:

Learn from usage of applications

SaaS, PaaS and on-premise applications are often designed based upon a lot of assumptions. In real-life, however, the applications can be used in other ways then designed. A nice way to depict this is the way bicycles and pedestrians make short-cuts next to roads, such as shown below.

When designing a chatbot, we make assumptions regarding the conversations an end user wants to be engaged in. The assumptions concern both topics and conversation flow. When the chatbot is brought into production, we may learn that the conversation is always abandoned at a specific point or that the end user really wanted to discuss something else. With this information, we can improve conversation flow on short notice.

Another example is getting a better understanding of the risk rate of products sold to customers in the insurance world. Machine learning makes it easier to extract answers from data. By learning the machine learning model to understand the different product and customer risk features, it will be able to predict a risk rate for a given customer and product combination.

Learn from API usage

APIs are designed to support different user groups with specific sets of use cases. In the digital economy, we need to understand how APIs are used and where they can be improved, both in terms of functionality and scalability, in order to address ever-changing end-user demands.

Improve costs structures of cloud usage

The cloud economic model is different from the model most customers are used to in their on-premises data centers. When going toward the cloud, a different financial model is in place, where usage and interfacing determine the bills to be paid at the end of the month. Understanding the inter connectivity and impact on cloud usage and economics makes it possible to continuously measure and save costs. The upcoming Oracle AI platform combines the power of open source, data science and machine/deep-learning capabilities, with Oracle’s integration capabilities to improve the usage and costs of both on-premises and cloud applications with knowledge about application and API usage.

In the forthcoming blogs, we will prepare for the AI cloud by looking at the tooling and frameworks, machine learning and infrastructure.

This blog series was co-authored by Léon Smiers and Johan Louwers. Léon Smiers is an Oracle ACE and a thought leader on Oracle cloud within Capgemini. Johan Louwers is an Oracle ACE director and global chief architect for Oracle technology. Both can be contacted for more information about this, and other topics, via email; Leon.Smiers@capgemini.com and Johan.Louwers@capgemini.com