This article is sponsored by Oracle – redefining data management with the world's first autonomous database. In the coming years, the amount of data we create worldwide will grow to 175 zettabytes of data per year by 2025, up from 33 zettabytes in 2018. Over half of this data will be created by the Internet of Things devices and over 60% of it will be enterprise data. By 2025, 30% of all the data created will be in real-time, offering organisations great opportunities to constantly optimise their business. Clearly, the organisation of tomorrow is a data organisation.

If you have access to Oracle Database, then you probably already have access to Oracle Machine Learning (OML). In this hands-on workshop based on Autonomous Database, you will learn how to use OML to build a machine learning model and integrate it into an APEX application. The workshop is suitable for Oracle data professionals looking to build their first machine learning model, and data scientists who want the simplest way to apply machine learning to enterprise data.

Did you know humans now generate an estimated 2.5 quintillion bytes of data each day? More data has been created in the last couple of years as compared to data created in all of human history! How can businesses cope with this data flood and use it to their advantage? In the data-driven economy, businesses around the world are struggling to unlock more strategic value from their data. The sheer volume of data and its exponential growth is making its management complicated.

According to McKinsey's study "Visualizing the uses and potential impact of AI and other analytics", 2018, the estimated impact of artificial intelligence and other analytics on all industries regarding anomaly detection is between $1.0T and $1.4T. Anomaly detection is the critical success factor in predictive maintenance, which tries to anticipate when maintenance is required. This differs from the classical preventive approach, in which activities are planned on a regularly scheduled basis, or condition-based maintenance activities, in which assets are monitored through IoT sensors. Applying anomaly detection algorithms based on machine learning, it's possible to perform prognostics to estimate the condition of a system or a component and its remaining useful life (RUL), in order to predict an incoming failure. One of the most famous algorithms is the MSET-SPRT, well-described with a use case in this blog post: "Machine Learning Use Case: Real-Time Support for Engineered Systems."

Truly "autonomous" systems are starting to replace or augment many of the routine tasks and processes people perform every day, improving efficiency while freeing individuals for higher-level pursuits. But what's often overlooked is how much progress is happening in other areas and industries: healthcare, air travel, energy provision, retail, logistics, agriculture, and construction. Autonomous systems are even helping governments match refugees with the most suitable communities to live, as detailed in one of the four real-world vignettes we present below. Such optimism makes sense, given advances such as self-managing and self-patching databases in IT. But our survey's other findings might underestimate the pace of change: Just 24% say they expect to see significant use of autonomous tech in construction, for example, even though self-driving bulldozers already are in use on select projects.

Delivering extreme performance and availability, Oracle Exadata is the foundation for Oracle Autonomous Database, the world's first self-driving database, and Oracle Cloud Applications. In fiscal year 2018, Exadata set all-time product sales records with continued adoption across multiple workloads such as OLTP, Analytics, and IoT, and multiple verticals, including finance, retail, electronics, and telecommunications. "For the past 10 years, Exadata has been running the most critical workloads for thousands of customers around the world. Exadata now powers Oracle Autonomous Database and Oracle Cloud Applications," said Juan Loaiza, executive vice president, Mission-Critical Database Technologies, Oracle. "Today, we are improving the performance and capacity of the platform, and adding a broad range of capabilities based on artificial intelligence and machine learning to further increase Exadata's advantages."

For machine learning development, I will choose a user created in the "Manage Oracle ML Users" page, named MLUSER. This notebook predict customers most likely to be positive responders to an Affinity Card loyalty program. Select the template and click on the "Create Notebook" button, enabled after template selection. To execute the notebook just created, we need to enable in terms of interpreter bindings, since by default only %md is selected. Execute all the paragraphs included into the notebook; everything is well-documented.

The dataset used for building a network intrusion detection classifier is the classic KDD you can download here, released as first version in the 1999 KDD Cup, with 125.973 records in the training set. It was built for DARPA Intrusion Detection Evaluation Program by MIT Lincoln Laboratory. The dataset is already split into training and test dataset. The sub-classes into training dataset are 22 for attacks, and one "normal" for traffic allowed. The list of attacks and the associations with the four categories reported above is hold in this file.

This article is sponsored by Oracle – redefining data management with the world's first autonomous database. In the coming years, the amount of data we create worldwide will grow to 175 zettabytes of data per year by 2025, up from 33 zettabytes in 2018. Over half of this data will be created by the Internet of Things devices and over 60% of it will be enterprise data. By 2025, 30% of all the data created will be in real-time, offering organisations great opportunities to constantly optimise their business. Clearly, the organisation of tomorrow is a data organisation.

Humans are now generating an estimated 2.5 quintillion bytes of data every single day, with more data being created in the past two years than in all of human history. Managing this growing flood is complex and the task comes with a high level of responsibility. The 24/7 requirements on business and huge security challenges mean that'manual" management is no longer an option. Particularly when combined together they will let businesses manage and get value from their information more easily, effectively, and with less effort. One technology in particular that is unlocking new levels of value is the autonomous database.