cloud


Increasing Efficiency and Uptime with Predictive Maintenance

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In many manufacturing plants today, monitoring is a highly manual process. FOURDOTONE Teknoloji analyzes data from sensors to enable manufacturers to respond immediately to problems, and predict when machines are likely to fail. Downtime can be expensive, and in a tightly coupled manufacturing line a problem with one machine can have an impact on the entire factory. For many factories, avoiding downtime is a matter of luck rather than science: machine inspections are infrequent, and only capture what's visible to the eye. Data is gathered from the machines and analyzed in the factory, enabling an immediate response to emergencies or imminent problems.


[video] #Blockchain Keynote at @CloudExpo #IoT #ML #AI #Bitcoin #FinTech

@machinelearnbot

In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination - the Blockchain.


Machine Learning as a service: The way ahead for digital transformation - ETtech

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By Mrinal Sinha, Sapient Consulting The phenomenal growth of cloud-based offerings such as Platform as a service (PaaS), Infrastructure as a service (IaaS) and Software as a service (SaaS) has resulted into bigger competition in the market, with the new addition being Machine Learning as a Service (MLaaS). Machine Learning has emerged as one of the fastest evolving technologies today. One of the most critical factors for Machine Learning implementation is to have huge sets of data and to have machine learning (ML) experts or Data scientists who can identify a pattern in data, hiring whom can be difficult and expensive. Moreover, selecting a machine-learning algorithm is a process of trial and error. It is also a trade-off between specific characteristics of the algorithms, such as speed of training, memory usage, predictive accuracy on new data etc.


Machine Learning as a service: The way ahead for digital transformation - ETtech

#artificialintelligence

By Mrinal Sinha, Sapient Consulting The phenomenal growth of cloud-based offerings such as Platform as a service (PaaS), Infrastructure as a service (IaaS) and Software as a service (SaaS) has resulted into bigger competition in the market, with the new addition being Machine Learning as a Service (MLaaS). Machine Learning has emerged as one of the fastest evolving technologies today. One of the most critical factors for Machine Learning implementation is to have huge sets of data and to have machine learning (ML) experts or Data scientists who can identify a pattern in data, hiring whom can be difficult and expensive. Moreover, selecting a machine-learning algorithm is a process of trial and error. It is also a trade-off between specific characteristics of the algorithms, such as speed of training, memory usage, predictive accuracy on new data etc.


Machine Learning

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Bootcamp Description Everything we have learnt in software development is undergoing change. Thousands of job go unfilled at various companies as they can't find qualified engineers who know machine learning. TiE's one day bootcamp will set you on the right path. This is one investment in your career you can't afford to miss. Whether you are starting out or want to manage a team of machine learning experts, here is your chance to take just one day from your schedule and walk with real hands on experience with machine learning concepts.


Applying Machine Learning to SEC Filings to find Anomalous Companies

@machinelearnbot

Contemporary machine learning algorithms are well-suited to the complex, high-dimensional data associated with accounting records. In this short note we apply a simple unsupervised algorithm to find anomalous companies -- those with accounting metrics that don't match the statistical patterns implied by the bulk of the companies. To do this we leverage the SEC structured financial statements data set, a regularly updated collection of the machine-readable numeric core of the financial disclosures regularly filed to the SEC through its EDGAR system. We use the reported company assets as a normalizing factor; while size is of course a variable of interest, we are looking for less obvious, scale-independent patterns and anomalies. Note that axis and values in the graph above are in many ways arbitrary; it's simply a reasonable effort at representing in three dimensions the relative distances between points in the six-dimensional data space for the company fillings.


AWS Puts More Muscle Behind Machine Learning And Database

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Amazon Web Services essentially sparked the public cloud race a dozen years ago when it first launched the Elastic Compute Cloud (EC2) service and then in short order the Simple Storage Service (S3), giving enterprises access to the large amount compute and storage resources that its giant retail business leaned on. Since that time, AWS has grown rapidly in the number of services it offers, the number of customers it serves, the amount of money it brings in and the number of competitors – including Microsoft, IBM, Google, Alibaba, and Oracle – looking to chip away at Amazon's dominant 44 percent market share. Given the growth and the number of top-tier vendors taking aim, many in the industry have wondered when AWS would start to tail off, would start to see revenue or customer growth start to slow or see that those competitors are getting larger in the rear-view mirrors. That day will most likely come, but it hasn't happened yet. As we reported in The Next Platform, AWS now over a million organizations that are running part or all of their businesses on the AWS cloud.


Securing Velocity @DevOpsSummit #CloudNative #Serverless #AI #DevOps

@machinelearnbot

Because CICD saves developers a huge amount of time. CD is an especially great option for projects that require multiple and frequent contributions to be integrated. But... securing CICD best practices is an emerging, essential, yet little understood practice for DevOps teams and their Cloud Service Providers. The only way to get CICD to work in a highly secure environment takes collaboration, patience and persistence. Building CICD in the cloud requires rigorous architectural and coordination work to minimize the volatility of the cloud environment and leverage the security features of the cloud to the benefit of the CICD pipeline.


FICO Delivers Artificial Intelligence in the Cloud

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FICO Analytics Workbench is an analytic development tool that now supports a wide range of commonly used AI and ML model executions and proprietary analytic models for use cases such as fraud and anomaly detection. With the FICO Analytics Workbench, users have access to data exploration, visual data wrangling, decision strategy design, and machine learning. Analytics Workbench offers an explainable AI toolkit that enables organization to validate and interpret models, as well as individual model executions. It also includes pathways for exploring and modeling data, analytic notebook and its array of popular data science languages.


Make Way for Machine Learning: How Seattle Is Becoming a Major Hub for Artificial Intelligence

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This article appears in print in the April 2018 issue. Artificial intelligence (AI) promises to revolutionize virtually every sector of the economy --from automobile manufacturing to health care delivery to building maintenance. And the Seattle region's prominent role in cloud computing -- a key driver in AI's widespread use -- means the technology is becoming as prominent here as damp shoes. Indisputably, AI has quietly and inexorably made its way into our daily lives. "We see it in a lot of the things that require speech recognition," says Oren Etzioni, CEO of the Paul Allen Institute for Artificial Intelligence.