Personal assistant is one way of working "smarter"
Machine learning is teaching from past learnings, learning and understanding
Prerequisite is having the data
Automate manual jobs, leave focus times for employees, increased satisfaction
Train data, apply model to database, capture feedback from users, and retrain model
Powered by HANA Cloud platform; machine learning service on HCP
Customers, partners build their own solutions
Use cases shown at SAPPHIRENOW
Priority 1 includes invoice matching and social media
Social media customer, working with C4C, taking sentiment from FB/Twitter posts, what is the sentiment from the posts and auto-classify – negative, consultive, and action recommender – dispatch messages to customer service agents
yaaS is Hybris as a service, a simple product recommender
CV matching is for job recruiting, mentioned in Hasso’s keynote; machine learning screens CV’s and matches with job posting, comes gender-free, doesn’t care about age
Call for customer co-innovation
Co-innovation framework; no guarantee of a product
Question & Answer
Q: How can we engage the Innovation Centre to bring customers to you and perhaps see if there is a project which is off interest?
A: covered on the slides
Q:Are there cookbooks for scenarios like “Invoice Matching”?
A: will develop with co-innovation customers
Q: Is there any plan for an open.sap.com course on these topics?
Q: How can we engage with you if we think we have interesting customer projects for Co-Innovation
A: contact the speakers of the webcast
Q: Can we get more details on Machine Learning platform
A: HCP; more information; details on YouTube – co-innovation
Q: Most of at least the co-innovation projects are customer specific and one off, how to scale this approach?
A: New ideas for potential machine learning – look at horizontal LoB agile use cases, customer engagement, and industry verticals such as banking is second wave, cross industry; readiness to executive
Q: How does The Innovation Center prioritize the projects that you want to focus on?
A: vertical, horizontal, attractiveness, readiness to execute
Q: will there be Machine Learning content at TechEd in Vegas?