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    1. How Is Apple Using Machine Learning? | @ThingsExpo #AI #ML #DL #IoT #M2M

      How Is Apple Using Machine Learning? | @ThingsExpo #AI #ML #DL #IoT #M2M

      What DX Means to Retailers By William Schmarzo A recent BusinessWeek article titled “America’s Retailers Are Closing Stores Faster Than Ever” summarizes the epidemic that retailers are facing today (see Figure 1). Retailers are closing stores at a record path and the driving force behind the acceleration in store closings is Amazon, who now accounts for over 50% of all on-line retail sales (see Figure 2).

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      Mentions: Amazon Apple America
    2. Key to Data Monetization | @CloudExpo #IoT #BigData #Analytics #AI #DX #DigitalTransformation

      Key to Data Monetization | @CloudExpo #IoT #BigData #Analytics #AI #DX #DigitalTransformation

      Analytic Profiles: Key to Data Monetization By William Schmarzo Many organizations are associating data monetization with selling their data. But selling data is not a trivial task, especially for organizations whose primary business relies on its data. Organizations new to selling data need to be concerned with privacy and Personally Identifiable Information (PII), data quality and accuracy, data transmission reliability, pricing, packaging, marketing, sales, support, etc.

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    3. High-Level Plan for #DigitalTransformation | @CloudExpo #AI #ML #DX #IoT

      High-Level Plan for #DigitalTransformation | @CloudExpo #AI #ML #DX #IoT

      AI Is About Machine Reasoning By Rene Buest Machine Learning needs tons of data. But what are you going to do when the data only exist in the heads of your employees? Machine Learning, Deep Learning, Cognitive Computing, Robotic Process Automation (RPA), Natural Language Processing (NLP), Machine Perception, Predictive APIs, Image Recognition, Speech Recognition, Virtual Agent, Intelligent Assistant, Personal Advisor, Chatbot, Semantic Search. Did I miss anything? I am sure I did.

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    4. AI Is About Machine Reasoning | @CloudExpo @ReneBuest #AI #ML #DX #ArtificialIntelligence

      AI Is About Machine Reasoning | @CloudExpo @ReneBuest #AI #ML #DX #ArtificialIntelligence

      [video] IoT Panel: How to Handle All This Data By Pat Romanski In this power panel at @ThingsExpo, Jul. 7, 2017 06:15 PM EDT Reads: 1,983 Low-Code/No-Code Is Disruptive By Jason Bloomberg In the No-Code corner are the ‘citizen developers’ – business users who can build functional but generally limited apps without having to write a line of code.

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    5. Container Considerations on Your #DevOps Journey | @DevOpsSummit #AI #Docker #Serverless

      Container Considerations on Your #DevOps Journey | @DevOpsSummit #AI #Docker #Serverless

      [slides] The Governance of IoT Data By Liz McMillan IoT solutions exploit operational data generated by Internet-connected smart “things” for the purpose of gaining operational insight and producing “better outcomes” (for example, create new business models, eliminate unscheduled maintenance, etc.).

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    6. Value of Unlocking Legacy Applications | @CloudExpo #Cloud #Analytics

      Value of Unlocking Legacy Applications | @CloudExpo #Cloud #Analytics

      Natural Language Processing By William Schmarzo “Apophenia is the propensity to see patterns in random data.” We encounter it all the time in the real world. Examples include gamblers who see patterns in how the cards are being dealt or investors who imagine patterns in the movement of certain stocks, or basketball fans who believe that their favorite player has the “hot hand.” But apophenia has no place in the world of data science, especially when data science is trying to help us make better decisions about critical things such as the quality of healthcare, where to allocate police resources ...

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      Mentions: India Amazon Google
    7. Natural Language Processing | @BigDataExpo #BigData #Analytics #DataScience

      Natural Language Processing | @BigDataExpo #BigData #Analytics #DataScience

      Natural Language Processing By William Schmarzo “Apophenia is the propensity to see patterns in random data.” We encounter it all the time in the real world. Examples include gamblers who see patterns in how the cards are being dealt or investors who imagine patterns in the movement of certain stocks, or basketball fans who believe that their favorite player has the “hot hand.” But apophenia has no place in the world of data science, especially when data science is trying to help us make better decisions about critical things such as the quality of healthcare, where to allocate police resources ...

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    1-11 of 11
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