Oracle releases new cloud analytics offering for Oracle Fusion SCM offering

Posted on by Teddy Nee

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Oracle, a global provider of integrated cloud applications and platform services, announced it rolled out a new cloud analytics offering for its shipper customers using its Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) platform, which connects shippers’ supply networks with an integrated suite of cloud business applications.

Earlier this month, Oracle, a global provider of integrated cloud applications and platform services, announced it rolled out a new cloud analytics offering for its shipper customers using its Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) platform, which connects shippers’ supply networks with an integrated suite of cloud business applications.

Oracle said that the new cloud analytics provide shippers with the needed insights “to detect, understand, and resolve issues faster throughout the supply chain.” And they added that in leveraging Oracle Analytics Cloud and Oracle Autonomous Data Warehouse, the new Oracle Fusion SCM Analytics provides shippers with pre-built metrics and dashboards that utilize machine learning capabilities that help shippers on various fronts, including reducing costs, ensuring customer satisfaction, and driving revenue.

“Supply chains are under immense scrutiny as organizations face new and unexpected disruptions,” said T.K. Anand, senior vice president, Oracle Analytics, in a statement. “Now more than ever, organizations need real-time insights into every element of their supply chain to help them make the right decisions and get ahead of disruptive events and changing customer expectations. With Oracle Fusion SCM Analytics, customers can quickly uncover supply chain performance insights, identify issues, increase efficiency, and minimize supply chain disruption.”

Jon Chorley, GVP of SCM Product Strategy and Chief Sustainability Officer, Oracle, provided LM with a detailed overview this new offering in interview.

LM: What drove the need for Oracle to roll out Oracle Fusion SCM Analytics?
LM: What are the main benefits of the new analytics capabilities for shipper customers?
LM: Can you please provide a basic example of how it functions?

This example highlights how Oracle Fusion SCM Analytics provides customers with new ways of working with data by using machine learning-powered predictions, which helps organizations gain actionable insights to improve supply chain performance – and ultimately deliver the best possible customer experience.

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Architecting a Machine Learning System for Risk

Posted on by Teddy Nee

Architecting a Machine Learning System for Risk

Online risk mitigation

At Airbnb, we want to build the world’s most trusted community. Guests trust Airbnb to connect them with world-class hosts for unique and memorable travel experiences. Airbnb hosts trust that guests will treat their home with the same care and respect that they would their own. The Airbnb review system helps users find community members who earn this trust through positive interactions with others, and the ecosystem as a whole prospers.

We can mitigate the potential for bad actors to carry out different types of attacks in different ways.

1) Product changes

Many risks can be mitigated through user-facing changes to the product that require additional verification from the user.

2) Anomaly detection

Scripted attacks are often associated with a noticeable increase in some measurable metric over a short period of time.

3) Simple heuristics or a machine learning model based on a number of different variables

Fraudulent actors often exhibit repetitive patterns.

Machine Learning Architecture

Different risk vectors can require different architectures. For example, some risk vectors are not time critical, but require computationally intensive techniques to detect. An offline architecture is best suited for this kind of detection. For the purposes of this post, we are focusing on risks requiring realtime or near-realtime action. From a broad perspective, a machine-learning pipeline for these kinds of risk must balance two important goals:

1) The framework must be fast and robust.

That is, we should experience essentially zero downtime and the model scoring framework should provide instant feedback.

2) The framework must be agile.

Since fraud vectors constantly morph, new models and features must be tested and pushed into production quickly.

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KB – Neural Data Mining with Python sources

Posted on by Teddy Nee

KB – Neural Data Mining with Python sources

The aim of this book is to present and describe in detail the algorithms to extract the knowledge hidden inside data using Python language, which allows us to read and easily understand the nature and the characteristics of the rules of the computing utilized, as opposed to what happens in commercial applications, which are available only in the form of running codes, which remain impossible to modify.

The algorithms of computing contained within the book are minutely described, documented and available in the Python source format, and serve to extract the hidden knowledge within the data whether they are textual or numerical kinds. There are also various examples of usage, underlining the characteristics, method of execution and providing comments on the obtained results.

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