As a result, all major cloud providers are either offering or promising to offer Kubernetes options that run on-premises and in multiple clouds. While Kubernetes is making the cloud more open, cloud providers are trying to become "stickier" with more vertical integration. From database-as-a-service (DBaaS) to AI/ML services, the cloud providers are offering options that make it easier and faster to code. Organizations should not take a "one size fits all" approach to the cloud. For applications and environments that can scale quickly, Kubernetes may be the right option. For stable applications, leveraging DBaaS and built-in AI/ML could be the perfect solution. For infrastructure services, SaaS offerings may be the optimal approach. The number of options will increase, so create basic business guidelines for your teams.
How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.
And the shift hasn't gone unnoticed by the Big Three cloud providers. AWS and others offer subscription-based remote data storage and online tools, and researchers say they can be an affordable alternative to setting up and maintaining their own hardware. The cloud's added computing power can also make it easier for researchers to run machine-learning algorithms designed to identify patterns and extract insights from vast amounts of climate data, for instance, on ocean temperatures and rainfall patterns, as well as decades' worth of satellite imagery. "The data sets are getting larger and larger," said Werner Vogels, chief technology officer of Amazon.com Inc. "So machine learning starts to play a more important role to look for patterns in the data."
Most of us already know that adopting new cloud applications can boost a business's productivity by enabling organizations to be more agile and ready to change course in our fast-moving and connected digital world. But the rapid adoption of cloud apps and services also brings with it profound security threats, including visibility and control challenges that aren't present in traditional on-premises environments. At the same time, the cloud - because of its interconnected, flexible and adaptable nature - can also provide new possibilities for addressing cloud security problems. By leveraging the power of the cloud with a data science and machine learning cloud-based solution, security and risk professionals can solve many of the traditional security challenges found in popular apps like Office 365, Google Drive, Salesforce and Box. In her session at 19th Cloud Expo, Deena Thomchick, Senior Director of Cloud Security at Symantec, detailed how cloud-based data science, machine learning, computational analysis and intelligent algorithms can work together to help to deliver truly intelligent and responsive security and compliance for the cloud.
Cloud computing has made a lot of technology more accessible, and artificial intelligence and its underlying technologies are no exception. If you want more organizations to be able to use your technology, then make it possible for them to use it on one of the big public cloud providers -- Microsoft Azure, Google Cloud Platform, and Amazon Web Services (AWS). Indeed, many organizations are now using the AI services that are available and have been built on those public cloud platforms -- AWS Rekognition, for instance. In an effort to broaden the distribution of its flagship artificial intelligence technology, IBM this week announced that it is making IBM Watson portable across all these public cloud services. The company unveiled the strategy this week at the IBM Think 2019 event in San Francisco.
Prior to working at H2O, he worked as a Quality Assurance Software Engineer, developing software automation testing. Nicholas holds a degree in Mechanical Engineering, and has experience working with customers across multiple industries, identifying common problems, and designing robust, automated solutions.
BlueData, provider of the leading Big-Data-as-a-Service (BDaaS) software platform, today announced the new summer release for BlueData EPIC . This release builds upon BlueData's innovations in running large-scale distributed analytics and machine learning (ML) workloads on Docker containers, with new functionality to deliver even greater agility and cost savings for enterprise Big Data and AI initiatives. Last spring, BlueData introduced support for hybrid cloud environments – leveraging the inherent infrastructure portability and flexibility of Docker containers. This past fall, BlueData delivered a major new release that added deep learning (DL), GPU acceleration, and multi-cloud support to the container-based BlueData EPIC platform. And last month, BlueData announced a new turnkey solution to accelerate AI and ML / DL deployments in the enterprise.
Recently I read somewhere this statement – As we end 2017 and look ahead to 2018, topics that are top of mind for data professionals are the growing range of data management mandates, including the EU's new General Data Protection Regulation that is directed at personal data and privacy, the growing role of artificial intelligence (AI) and machine learning in enterprise applications, the need for better security in light of the onslaught of hacking cases, and the ability to leverage the expanding Internet of Things.
Oracle's new Autonomous Database Cloud will be cheaper, faster and, with the addition of the Oracle Cyber Security System, safer than anything from Amazon Web Services (AWS). At least that's the assertion Oracle Executive Chairman and CTO Larry Ellison wants everyone to remember from last week's Oracle Open World 2017 (OOW17) event in San Francisco. Whether Oracle's claims are fair and accurate comparisons remains to be seen, as the first release of the Oracle Autonomous Database, through a Data Warehouse Cloud Service, won't be available until December. Count on it being at least a few more months before independent reviewers can do independent tests against rival cloud services. It should be about that same time -- six months from now -- that several other data-related announcements from OOW17 will actually be available.
In this episode of the ARCHITECHT Show, Elastic founder and CEO Shay Banon talks about the evolution of Elasticsearch -- from an open source side project (the first iteration was a recipe-search app for his wife) to popular big data tool to the core of a company worth nearly a billion dollars. He also shares his thoughts and strategies on the growth of Elastic, which, somewhat under the radar, has expanded to include multiple products and employ hundreds of people around the world. In this episode of the ARCHITECHT AI Show, Derrick Harris speaks with Jeremy Howard and Rachel Thomas of Fast.ai, Among other things, Howard and Thomas discuss the promise of deep learning and early student successes (including Hot Dog, Not Hot Dog app from Silicon Valley), as well as the threat of job losses from AI and how seriously we should take Elon Musk's AI warnings. AIMatter is based in Belarus and built an app, called Fabby, that lets users add effects to their selfies.