big data


Machine learning on edge devices solves lack of data scientists

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The current approach to AI and machine learning is great for big companies that can afford to hire data scientists. But questions remain as to how smaller companies, which often lack the hiring budgets to bring in high-priced data scientists, can tap into the potential of AI. One potential solution may lie in doing machine learning on edge devices. Gadi Singer, vice president of the Artificial Intelligence Products Group and general manager of architecture at Intel, said in an interview at the O'Reilly AI Conference in New York that even one or two data scientists are enough to manage AI integration at most enterprises. But will the labor force supply adequate amounts of trained data scientists to cover all enterprises' AI ambitions?


Fight your space: Multi-tenant big data clusters

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When you're running a modern data cluster, which are becoming increasingly commonplace and essential to businesses, you inevitably discover headaches. Typically a wide variety of workloads run on a single cluster, which can make it a nightmare to manage and operate - similar to managing traffic in a busy city. There's a real pain for the operations folks out there who have to manage Spark, Hive, impala and Kafka applications running on the same cluster where they have to worry about each app's resource requirements, the time distribution of the cluster workloads, the priority levels of each app or user, and then make sure everything runs like a predictable well-oiled machine. Anyone working in data ops will have a strong point of view here since you'll have no doubt spent countless hours, day in and day out, studying the behaviour of giant production clusters in the discovery of insights into how to improve performance, predictability and stability. Whether it is a thousand node Hadoop cluster running batch jobs, or a five hundred node Spark cluster running AI, ML or some type of advanced, real-time, analytics.


IBM Brings AI and Advanced Analytics to the Industrial World

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IBM (NYSE: IBM) today announced a new portfolio of Internet of Things (IoT) solutions that team artificial intelligence (AI) and advanced analytics to help asset intensive organizations, such as the Metropolitan Atlanta Rapid Transit Authority (MARTA), to improve maintenance strategies. The solution is designed to help organizations to lower costs and reduce the risk of failure from physical assets such as vehicles, manufacturing robots, turbines, mining equipment, elevators, and electrical transformers. IBM Maximo Asset Performance Management (APM) solutions collect data from physical assets in near real-time and provide insights on current operating conditions, predict potential issues, identify problems and offer repair recommendations. Organizations in asset-intensive industries like energy and utilities, chemicals, oil and gas, manufacturing, and transportation, can have thousands of assets that are critical to operations. These assets are increasingly producing enormous amounts of data on their operating conditions.


What Is AIOps? - ITChronicles

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You've no doubt heard the term being bandied around – but what is it, and what can it do for your organization? Let's begin with a definition from Gartner, the research firm which coined the term a couple of years ago, and has since been responsible for popularizing both the phrase and the concept. "AIOps platforms combine big data and machine learning functionality to support all primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity of data generated by IT. The platform enables the concurrent use of multiple data sources, data collection methods, and analytical and presentation technologies." Translation: AIOps is essentially an umbrella term to describe the use of machine learning and big data analytics technologies to automate the identification and subsequent resolution of common IT issues.


Level of Astuteness in Current AI Systems

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The growth in how much big data is considered to cracking solutions have grown drastically after the '90s. Their role in resolving issues by predicting situations at the earliest is coming handy because of continuous advancements in this field of work. Back then, storage was a massive problem along with belief over technology (LOL). Storage isn't a damn issue while also life without technology is highly impossible. On a serious note, though gathering data and processing them in large quantities had been happening for a longer period now, the data are put to good use only recently.


Free Python Data Science coding Book series

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To provide some context, I posted about the idea of learning coding for machine learning / deep learning in a weekend We have had considerable success with this – and now we are planning the next stage. Created and managed by Ajit Jaokar, Dan Howarth, Ayse Mutlu - London UK. we welcome other community moderators. I highly recommend Chris Albon book – however the book itself is not free. Don't forget to join the group for the free books


Veracity of Big Data - Programmer Books

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Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V's of big data, including veracity, and study the problem from various angles. The solutions discussed are drawn from diverse areas of engineering and math, including machine learning, statistics, formal methods, and the Blockchain technology. Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language.


Global Artificial Intelligence Conference on April 23rd to April 25th in Seattle GlobalBigDataConference

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Global Big Data Conference's vendor agnostic Global Artificial Intelligence(AI) Conference is held on April 23rd, April 24th, & April 25th 2019 on all industry verticals(Finance, Retail/E-Commerce/M-Commerce, Healthcare/Pharma/BioTech, Energy, Education, Insurance, Manufacturing, Telco, Auto, Hi-Tech, Media, Agriculture, Chemical, Government, Transportation etc..). It will be the largest vendor agnostic conference in AI space. The Conference allows practitioners to discuss AI through effective use of various techniques. Large amount of data created by various mobile platforms, social media interactions, e-commerce transactions, and IoT provide an opportunity for businesses to effectively tailor their services by effective use of AI. Proper use of Artificial Intelligence can be a major competitive advantage for any business considering vast amount of data being generated.


Newcastle smart city moves forward with standardised data platform

ZDNet

The City of Newcastle has signed up to a single smart cities Internet of Things (IoT) enterprise platform from the National Narrowband Network Company (NNNCo), the company has announced. "The city standardised on the middleware platform as it prepares to roll out and scale multiple smart city applications," NNNCo said. "The deal between NNNCo and Newcastle City Council includes an agreement to run thousands of IoT devices through the platform for multiple city use cases." As part of the Newcastle City Intelligent Platform implementation, NNNCo will also provide its N-tick device certification program across all devices being deployed across the city. NNNCo CEO Rob Zagarella called the use of one platform and device certification program for an entire city a "breakthrough in the IoT market".


Scaling Agile, adopting AI: How Intel is making IT a strategic part of the business

ZDNet

Intel on Wednesday released its 2018-2019 IT performance report, giving an inside look at how the semiconductor business is transforming its IT operations to serve as a strategic part of the overall business. The report details the maturity of Intel's DevOps strategy and its ongoing efforts to scale Agile and DevOps practices. It also shows the progress Intel has made bringing machine learning into its operations. A large part of driving change within IT involves creating the right culture, Intel's chief data officer Aziz Safa told ZDNet. "Twenty years ago, we would not make a major change in the enterprise for years," he said.