XTraw AI: Machine Learning and AI Applications

In this podcast 8 season 2, we will be having a conversation with 2 of our data science experts who worked with enterprise customers in the machine learning space. We will discuss a few examples and also the guidelines and principles in doing machine learning in the enterprise customer space.

A brief teaser into the topics focused on machine learning and SAP apps in the year of tWenty22 aka 2022. Stay tuned and connected on the upcoming podcast sessions in 2022.

In this podcast 7 season 2, we will be having a conversation with our product experts Robert McGrath and Antoine Chabert from SAP Analytics cloud to discuss the smart features that are available for doing machine learning in the context of SAP S/4HANA. The conversation mainly focuses on the smart assist and smart predict features in SAP Analytics Cloud with some examples of how SAP S/4HANA and SAP SuccessFactors can leverage them. 

In this podcast 6 of season 2, we have a detailed conversation of how machine learning and predictive analytics enhances the planning functionality with SAP S/4HANA. This is an expert chat with Mark Burke who is the guru of Planning with SAP. Mark provides his thoughts about how planning functionality is enhanced with machine learning and discusses briefly the various examples such as the workforce management, sales & operational planning, financial planning & analysis. In this podcast, we also discuss briefly the various SAP planning solutions leveraged in the context of SAP S/4HANA.

In this podcast 5 in the season 2, we will be having a conversation with Sreedhar Muthuramalingam, the chief product expert, about how analytics is leveraged in the context of machine learning. We also discuss the various analytics tools and solutions at a high level on how they can be utilized for machine learning with SAP S/4HANA, SAP Business Technology Platform and SAP Analytics Cloud.

In this Podcast 4 in the Season 2, we will be having a conversation with 2 experts from the AI business services (Roberto Falk) and ISLM (Manikandan Rajasekar) to understand the behind scenes technical concepts of how AI business services are leveraged in the context of SAP S/4HANA. The conversation with the experts also provides you some technical know-how on these AI business services utilizing the ISLM framework for SAP S/4HANA.

In this Podcast 3 in the Season 2, we will be having a conversation with 2 experts from the AI business services (Jana Wuerth) and SAP S/4HANA (Yannick Peterschmitt) to understand how the AI business services are consumed with SAP S/4HANA. The conversation with the experts also provides you some thoughts on how you can go about leveraging some of those AI business services with SAP S/4HANA.

In this Podcast 2 in the Season 2, we will be chatting with another SAP expert, Marion Schlotte from the ISLM product management team to do a deep-dive into the core framework of ISLM that is used as a foundation in the life cycle management of machine learning with SAP S/4HANA. In this in-depth conversation we discuss on how ISLM is leveraged for both the embedded and side-by-side models while enabling machine learning with SAP S/4HANA.

In this Season 2 podcast series, we will chat with SAP Experts from the various technology and business teams to understand the various aspects of machine learning with SAP S/4HANA. In this Podcast 1 in Season 2, we will be chatting with Priti Dhingra, from the ISLM product management in the context of machine learning with SAP S/4HANA. The main focus of this podcast is on the overview of ISLM technology and how it is used in the context of embedding machine learning into SAP S/4HANA business processes.

In this podcast 10, we focus on the business implementation of the machine learning scenarios with SAP S/4HANA in the Produce Line of Business. We discuss at a high level in the manufacturing and supply chain domain, the different machine learning use cases that are already available with SAP S/4HANA. We also briefly discuss the machine learning technologies used in the context of these use cases. This ends the season 1.