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How organizations can make the most of machine learning

Nastel Technologies®
May 11, 2019
How organizations can make the most of machine learning

At SAP SAPPHIRE NOW 2019, TechRepublic Associate Staff Writer Macy Bayern sits down with Markus Noga, senior vice president of machine learning at SAP to discuss which companies should use machine learning, and best practices for execution. The following is an edited transcript of the interview.

Macy Bayern: In what ways are organizations most using machine learning currently?

Markus Noga: There’s three main ways that organizations use machine learning today, and we like to group them into the areas of automation, conversation, and intelligence. If you think about automation, things like factory robots come to mind, and the same repetitive tasks that robots have automated in factories, automation technology for software can automate in desktop environments. This category of software is called robotic process automation. SAP happens to be launching its robotic process automation solution called SAP Intelligent RPA this week. And we enable users to transcend the bounds of repetitive clicks and actions in systems, in order to focus on higher value tasks with IRPA.

The second big category is about the conversation. Not the conversation that you and I are having here, but repetitive large scale conversations around customer service, around procurement inquiries, around parts or order inquiries, where a small number of domains account for a large conversation volume. And by putting in the chat box and conversational agents, we enable the humans to focus on the challenging, the difficult to serve, or the value-adding parts of the conversation and letting bots take care of the routine elements and the repetitive tasks of this. SAP has a great product for this app called SAP Conversational AI, and it’s out for a free trial at SAP with a community of more than 60,000 active developers already.

Last but not least, people are using AI to bring actual intelligence into business processes. This is about training little machine learning modules that interface with the data, that interface with the decisions, and that keep learning in the business process. This kind of model brings the biggest value whenever decisions are at stake and whenever you need to decide anything from which banner ad to serve, all the way to whether to offer a line of credit to this customer. You want the best model, and that can help supplement the decisions humans are taking in the process with AI.

This article originally appeared on To read the full article, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here.

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


The Nastel i2M Platform provides:


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