Skip to Content
Author's profile photo Former Member

Predictive Analytics Whitepapers

This blog is intended to collate several whitepapers / thought leadership papers that the Data Science team of SAP Advanced Analytics have put together for the externally focused teams.


1. An Introduction to Deep learning – Examining the advantages of Hierarchical learning [Link]


This whitepaper, provides a high-level overview of deep learning and use cases of deep learning in learning complex machine learning problems like image, audio and language Recognition/ Classification.


2. Embed Deep –Learning Techniques into Predictive Modelling – Using SAP Predictive Analytics for Complex Modelling [Link]


This papers discusses how Deep Learning algorithms can be brought into SAP Predictive Analytics and use it for complex modelling


3. Using Predictive Maintenance to Approach Zero Downtime – How Predictive Analytics Makes This possible [ Link]


The focus of this thought leadership paper is to discuss various predictive maintenance scenarios in various fields like transportation, manufacturing and production, Utilities, Medical Equipment’s, Data Centres and Cloud infrastructure.


4. Addressing Predictive Maintenance with SAP Predictive Analytics [Link]


This article discusses how SAP Predictive Analytics product portfolio helps realize several predictive maintenance scenarios. It also brings out the importance of Model Manager for dealing with deployment and maintenance of large number of models.


5. Ensemble Learning Overview [Link]

This blog provides a high level overview of Ensemble learning and the inherent differences between them. It discusses Bagging , Boosting and Random Forest.



You can find more information about SAP Predictive Analytics at  .

Assigned Tags

      1 Comment
      You must be Logged on to comment or reply to a post.
      Author's profile photo Former Member
      Former Member

      Nicely done 🙂 thanks. Was really informative.

      Waiting for your next blog.. If possible can you include some sample datasets also while explaining the scenarios and the algorithm, could try it out then.