Whether it’s the latest in deep learning or in real-time search and analysis, Ryft brings together comprehensive analytics for smarter insights-faster.
Joined October 2014
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As data volumes and velocity grow, organizations are looking to identify "right-time" opportunities in "real-time." Read more about how organizations can overcome some of the biggest barriers to gaining insights from their data in real-time on the blog: ow.ly/CliJ30j42Vx
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What happens when you need to analyze a massive amount of data? Currently, you have to index that data to analyze it, and that index can turn one terabyte of data into five terabytes due to the size of the index. Or, you could stop indexing. ow.ly/ulKE30j42Rp
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A massive industry has been built around “cleaning” your data. But what if you didn’t HAVE to clean your data to get fast, accurate insights from it? Read more in the second post of our V's of #DataAnalytics series on the blog: ow.ly/2ouw30j42I6
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Data engineers spend hours preparing data for a warehouse or database. But what if we didn’t have to process our data at all? Learn more about how we are rethinking the #dataingestion pipeline on the blog: ow.ly/1fxx30j42zl
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When it comes to #machinelearning, your framework shouldn’t force a decision on analytics technologies, and your analytics technology shouldn’t force a decision on your framework. Read more on the blog: ow.ly/W9Vl30j42dI
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Data is messy—no way around it. Data variety has been the cause of many headaches of many #datascientists and #businessanalysts. Read more about how to harness a variety of data to get faster insights in the first post in our V's of #dataanalytics series: ow.ly/PgY630j4234
Innovations from #machinelearning and #artificialintelligence are fascinating, and they naturally lead us to wonder what breakthroughs we will create in our own organizations. The question that usually follows is, “Where to even start?” ow.ly/LlQy30iIQ5a
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When it comes to data analytics and machine learning initiatives, organizations need to makes sure they are using the right tool for the right job to improve efficiency and ensure success. ow.ly/emWQ30iIQ06
The recent hype around #machinelearning has created an abundance of articles about the basics of how computers learn. While many of these concepts are not new, it’s useful to understand the historical basics of #AI before we dive deeper. ow.ly/vuwy30iIPV9
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So, a new data analytics technology comes to market, or a new type of analysis is needed, and now you're tasked with updating the analytics ecosystem. How do you ensure you get the better, faster insights needed? Here are 5 questions you need to ask: ow.ly/JtWe30irKwK
Public safety organizations are working through ways to achieve greater insights with an increasing focus on using the Cloud to do so. So what can enterprises learn and take from public sector analytics programs? Quite a bit, actually. ow.ly/2fCx30iqclw
Machine Learning clustering/categorization algorithms require large numbers of complex comparison operations to initially sort the unstructured data. The computing status quo can't keep pace. Learn more about what's needed to achieve the insights you need: ow.ly/SC7O30iqcLD
Organizations are racing to take advantage of explosive data growth, but are they missing out on a treasure trove of dark, unstructured data? Learn how you can take advantage of it: ow.ly/aS3H30iqc3g #dataanalytics #hpda
When thinking about mission-critical activities, roles and responsibilities, titles with finance, sales, and IT often come to mind. Why is it that #data doesn’t immediately come to mind as well? ow.ly/LWfV30ihQtK #dataanalytics
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Everything generates #data; We generate it from our phone calls, our texts, tweets, emails, our Amazon wish list. Read more about the problems associated with collecting all of the data and how to overcome them on our blog. ow.ly/VfVk30ieFFg
Last week we talked about how #machinelearning could impact #healthcare personalization on the blog. Check out this article from @HITAnalytics on how #deeplearning can be used to help predict seizures. ow.ly/pcyI30i9mZ2
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RYFT retweeted
31 Jan 2018
.@Deloitte Global predicts bright future for #FPGAs in the #MachineLearning market with sales of “at least” 200K FPGAs in 2018. Xilinx is ready. Are you? bit.ly/2rQwiEW #DeloittePredicts
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Industrial IoT (IIoT) means lots of data-even more than many projected. @InformationAge has a great breakdown on why smart manufacturing needs smart, efficient edge solutions to get the most from that data (and other industries should take notice) | ow.ly/MpaW30i7vVh
As the demand for #machinelearning grows across nearly all industries—the list of applications where #FPGAs can and will deliver fast, efficient machine learning is nearly limitless. ow.ly/UFqC30i62te