Hybrid Computing, and thus Hybrid Analytics are concepts which are undergoing accelerated mutations, with the introduction of Edge and Fog Computing, in the wake of new mobility and IoT communication protocols, technologies and practices being phased in the Industry on a daily basis, 5G being its latest illustration. Our objective will be to shed some light on the various impacts, both positive and challenging, that these transformations impose on Cloud Analytics. This session will first address what these changes spell out for Cloud Analytics and in particular, what are the new considerations, key assets and enabling paradigms being introduced, both in terms of functional architectures and underlying infrastructures supporting the ingestion, distributed treatment and produced insights, in the cloud, in the fog, and at the edge, along with the unlocked potentials but also the pitfalls associated to them. As a part in these considerations, the session will address the intrinsic security, information privacy and data protection concerns, and the specific hybrid specificities which allow for new ways to compartment privacy and protect anonymity while maintaining the same descriptive and predictive capabilities. Unfortunately, we'll see that these new hybrid architectures can also harbor new combinations of vulnerabilities.

One of my favorite topics to discuss with other marketers is how both the art and science of marketing are changing. While the design and creativity of marketing is very exciting to me -- new interactive ways of creating and producing content, in this article I want to focus on the science of marketing -- the techniques used behind the scenes to improve marketing results for organizations. Specifically, I would like to focus on how machine learning intersects with the marketing function. Machine learning, as a technique, involves predictive and prescriptive techniques of data analysis to identify patterns and detect behaviors. The massive data volumes that are accessible to marketers -- whether Instagram or Twitter activity data, vehicle or device data, home automation data, or even spending and transaction data -- is data that can be collected and used for marketing purposes.

In this blog I aim to provide an introduction to TensorFlow and the SAP HANA integration, give you an understanding of the landscape and outline the process for using External Machine Learning with HANA. There's plenty of hype around Machine Learning, Deep Learning and of course Artificial Intelligence (AI), but understanding the benefits in an enterprise context can be more challenging. Being able to integrate the latest and greatest deep learning models into your enterprise via a high performance in-memory platform could provide a competitive advantage or perhaps just keep up with the competition? With HANA 2.0 SP2 onwards we have the ability to call TensorFlow (TF) models or graphs as they are known. HANA now includes a method to call External Machine Learning (EML) models via a remote source.

In this blog I aim to provide an introduction to TensorFlow and the SAP HANA integration, give you an understanding of the landscape and outline the process for using External Machine Learning with HANA. There's plenty of hype around Machine Learning, Deep Learning and of course Artificial Intelligence (AI), but understanding the benefits in an enterprise context can be more challenging. Being able to integrate the latest and greatest deep learning models into your enterprise via a high performance in-memory platform could provide a competitive advantage or perhaps just keep up with the competition? With HANA 2.0 SP2 onwards we have the ability to call TensorFlow (TF) models or graphs as they are known. HANA now includes a method to call External Machine Learning (EML) models via a remote source.

A couple of weeks ago, I spent a few hours at GE Digital's headquarters in San Ramon, CA. It was a great overview by several executives of how GE is using their Predix platform to create software to design, build, operate, and manage the entire asset lifecycle for the Industrial IoT. A big part of this transformation for GE involves hiring tons of software developers, acquisitions, and partnerships. One of those partnerships is with Silicon Valley based FogHorn Systems (GE Ventures, Dell Ventures, March Capital and a few others are investors). FogHorn is a developer of "edge intelligence" software for industrial and commercial IoT applications.

Today we're going to talk about bringing machine learning to your iOS apps. This is a topic that was really big in WWDC 2017, which was a little bit unexpected – I thought there would just be a couple updates, but I'm sure you've been hearing about machine learning already a lot this week. I'm an iOS developer at SoundCloud in Berlin and I have a background in math and CS. I studied a little bit of machine learning in college but nothing too substantial that was practical, so I had to relearn pretty much everything when I got back into ML recently. So what is machine learning? Probably all of you know what it is at this point, but just in case, in a nutshell it's enabling machines to learn like babies. Instead of explicitly telling the machine what you want it to do, the machine should infer this over time based on different ways of modeling. Arthur Samuel, who was an AI pioneer, defined it as the field of study that gives computers the ability to learn without being explicitly programmed. A very simple machine learning problem would be image classification.

Dr. Danny B. Lange is Head of Machine Learning at Uber where he leads an effort to build the world's most versatile Machine Learning platform to support Uber's rapid growth. With the help of this branch of Artificial Intelligence including Deep Learning, Uber can provide an even better service to its customers. Previously, Danny was the General Manager of Amazon Machine Learning. Managing Big Data has become a major competitive advantage for many organizations and hence maintaining a proper analytics platform is vital for an organization's survival. This conference provides insights and potential solutions to address Big Data issues from well known experts and thought leaders through panel sessions and open Q&A sessions.

Microsoft is in an extraordinary situation right now. Unlike most of its competitors, Microsoft's business is incredibly diversified as Redmond transitions from its heyday of the PC era into the mobile first, cloud first paradigm. Microsoft's flexibility allows it to not be wed to any single revenue master in the same way that Apple (the iPhone) and Google (advertising) are with their primary income drivers. Microsoft has three primary business units--devices, cloud and productivity--that are all growing and all tied together like the most expensive Venn diagram of all time. About half of all of Microsoft's business in the last quarter ( 12.7 billion) was from its "More Personal Computing" reporting group which includes Windows, Bing and its own hardware like the Surface tablets and Xbox. Microsoft's Office-based "Productivity And Business" segment made 6.6 billion in revenue while its "Intelligent Cloud" made 6.34 billion.