enterprise


A machine learning and AI guide for enterprises in the cloud

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AI technologies, including machine learning, give enterprises more insight into their data, streamline IT management tasks and provide a number of other benefits. Machine learning achieves these benefits by finding patterns in enterprise data sets and predicting possible outcomes -- all without the need for human intervention, which frees up admins for other tasks. Machine learning and AI services from major public cloud providers, such as Amazon Web Services (AWS), Azure and Google, make these technologies more accessible to enterprises. But before jumping in, organizations need to ensure that they have the necessary IT skill sets, carefully evaluate their service options and then implement them effectively. Use this guide to tackle some of those challenges and to get started with machine learning and AI cloud services in your organization.


data-integration-is-one-thing-the-cloud-makes-worse.html

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One, enterprises have too many decisions to make. Two, it's difficult to find success with complex data integration. Those are the two main excuses I hear these days, as enterprises move to the cloud. Whatever the justification, the lack of attention to data integration is beginning to cause some real damage. Enterprises have so much coming at them that they don't think about every approach and technology that they need to think about.


Enterprises Challenged By The Many Guises Of AI

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Artificial intelligence and machine learning, which found solid footing among the hyperscalers and is now expanding into the HPC community, are at the top of the list of new technologies that enterprises want to embrace for all kinds of reasons. But it all boils down to the same problem: Sorting through the increasing amounts of data coming into their environments and finding patterns that will help them to run their businesses more efficiently, to make better businesses decisions, and ultimately to make more money. Enterprises are increasingly experimenting with the various frameworks and tools that are on the market and available as open source software, in both small scale experiments run by a growing number of data scientists who have the expertise to find the valuable information the growing lakes of data and in full blown production deployments that are, conceptually, every bit as sophisticated as what the hyperscalers are deploying. The top cloud service providers and hyperscalers have for several years embrace data-driven AI and machine learning techniques and built their own internal frameworks and platforms that enable them to quickly take advantage of them. But as the technologies begin to cascade into more mainstream enterprises, the complexity of software and systems are throwing roadblocks in front of initiatives aimed at leveraging AI and machine learning for the good of the business.


Enterprise Machine Learning in a Nutshell (Repeat)

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Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world's most relevant enterprise data is part of SAP's system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.


The Impact of Artificial Intelligence in 2018: Seven Predictions

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The AI debate shifts from "is it good or evil" to "is it ever going to be good enough": If 2017 was the year where the warnings from Elon Musk and Stephen Hawking about the potential evil from AI clashed with predictions from Mark Zuckerberg and Bill Gates on its potential good, 2018 will be the year when the debate shifts to its practical utility. Much like other technologies that were lauded for their world-changing potential and then fizzled as the fog of the hype cleared, early adopters will find themselves disappointed by AI's obvious limits. The broader public--familiar with Alexa, Siri, and Google Home--will be similarly disillusioned as the experts acknowledge that there is only so much that AI will be able to do, and for really complex problems, a new paradigm will be needed. Despite the hype, AI has demonstrated value in industries across the board - from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making.


The Impact of Artificial Intelligence in 2018: Seven Predictions

#artificialintelligence

The AI debate shifts from "is it good or evil" to "is it ever going to be good enough": If 2017 was the year where the warnings from Elon Musk and Stephen Hawking about the potential evil from AI clashed with predictions from Mark Zuckerberg and Bill Gates on its potential good, 2018 will be the year when the debate shifts to its practical utility. Much like other technologies that were lauded for their world-changing potential and then fizzled as the fog of the hype cleared, early adopters will find themselves disappointed by AI's obvious limits. The broader public--familiar with Alexa, Siri, and Google Home--will be similarly disillusioned as the experts acknowledge that there is only so much that AI will be able to do, and for really complex problems, a new paradigm will be needed. Despite the hype, AI has demonstrated value in industries across the board - from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making.


[slides] #DNS and #DigitalTransformation @ExpoDX #IoT #AI #ML #DX #SaaS

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In his session at 21st Cloud Expo, Carl J. Levine, Senior Technical Evangelist for NS1, will objectively discuss how DNS is used to solve Digital Transformation challenges in large SaaS applications, CDNs, AdTech platforms, and other demanding use cases. Speaker Bio Carl J. Levine is the Senior Technical Evangelist for NS1. A veteran of the Internet Infrastructure space, he has over a decade of experience with startups, networking protocols and Internet infrastructure, combined with the unique ability to iterate use cases, bring understanding to those seeking to explore complicated technical concepts and increase revenue across diverse sales channels. CloudExpo DXWorldEXPO have announced the conference tracks for Cloud Expo 2018, introducing DXWorldEXPO. DXWordEXPO, colocated with Cloud Expo will be held June 5-7, 2018, at the Javits Center in New York City, and November 6-8, 2018, at the Santa Clara Convention Center, Santa Clara, CA.


Bringing Machine Learning (TensorFlow) to the enterprise with SAP HANA

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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.


The Importance of Creating a Culture of Data - THINK Blog

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Using and exploiting artificial intelligence (AI), is a goal for many enterprises around the world. Of course, before you can begin working with the cognitive technology, a number steps have to be taken. For starters, AI requires machine learning and machine learning, requires analytics. And to work with analytics effectively, you need a simple, elegant data, or, information architecture (IA). In other words, there is no AI without IA.


Deloitte TMT Predictions: Machine Learning Deployments Will Continue to Drive Growth - DATAVERSITY

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Among the findings pertaining to the enterprise, this year's report indicates that business organizations will likely double their use of machine learning technology by the end of 2018. TMT Predictions highlights five key areas that Deloitte believes will unlock more intensive use of machine learning in the enterprise by making it easier, cheaper and faster. The most important key area is the growth in new semiconductor chips that will increase the use of machine learning, enabling applications to use less power, and at the same time become more responsive, flexible and capable."