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C5050-384 - IBM Cloud Platform Application Development v2 - Dump Information

Vendor : IBM
Exam Code : C5050-384
Exam Name : IBM Cloud Platform Application Development v2
Questions and Answers : 56 Q & A
Updated On : November 12, 2018
PDF Download Mirror : C5050-384 Brain Dump
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C5050-384 Questions and Answers

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C5050-384 IBM Cloud Platform Application Development v2

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C5050-384 exam Dumps Source : IBM Cloud Platform Application Development v2

Test Code : C5050-384
Test Name : IBM Cloud Platform Application Development v2
Vendor Name : IBM
Q&A : 56 Real Questions

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totally obtainable later this year on IBM Cloud and IBM Cloud deepest, AI OpenScale is an open platform that enables organizations to operate and automate AI at scale, anyplace it resides -- with transparent, explainable effect.

The platform solution makes it less complicated for data scientists, software builders, IT and AI operations teams, and company manner house owners to collaborate in deploying, managing, monitoring and updating AI assets in production. This empowers corporations to confidently combine computing device researching and deep discovering capabilities into their functions, and scale seamlessly because the demand for AI grows.

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IBM publicizes Cloud-based mostly neighborhood Platform for Cyber safety functions | killexams.com Real Questions and Pass4sure dumps

IBM security hook up with allow Open specifications-based mostly Collaboration on AI Platform Backed by more than a Dozen groups

CAMBRIDGE, Mass., Oct. 15, 2018 /PRNewswire/ -- IBM (NYSE: IBM) these days announced a brand new cloud-based neighborhood platform for cyber safety applications. IBM safety join is the primary safety cloud platform developed on open federated technologies, with AI at its core, to analyze safety information across prior to now unconnected equipment and environments. 

IBM organization emblem. (PRNewsFoto/IBM supplier) (PRNewsFoto/) (PRNewsfoto/IBM)

more

An IBM analysis of consumers' environments discovered that on commonplace, cybersecurity groups are the usage of over 80 diverse security items from forty diverse companies. Our analysis additionally shows below 20 p.c of the aspects in these on-premise equipment are used and may no longer supply the results valued clientele are expecting due to integration and complexity challenges.  

through integrating protection information from IBM protection products with an ecosystem of security providers, customers, and enterprise partners, IBM security join is designed to help enhance effectivity and collaboration as groups look after against cybercrime. IBM security connect will enable clients to apply laptop researching and AI, together with Watson for Cyber protection, for evaluation to aid them determine threats or hazards and enhance the efficacy and efficiency of risk detection and response. users can design and installation new personalized and complete options to handle safety consequences, reminiscent of SOC Operations workflows or Digital trust. 

IBM safety join will prolong the wealthy set of capabilities from IBM's protection items, by the use of potent integration capabilities to connect records, applications and tools from an ecosystem of vendors, made viable with the aid of the cloud.   

"The boom of cybersecurity technology and information mixed with a turning out to be competencies shortage is growing an sudden degree of complexity for protection groups," referred to Marc van Zadelhoff, widespread supervisor, IBM safety. "Leveraging the power of the cloud, we are able to now assemble equipment, facts and people with out costly customization and integration initiatives. information federation through IBM security join helps supply security professionals multiplied protection visibility and efficiency without the bother of migrating records or overly complicated product integrations."

a new, Open strategy to Cloud protection

IBM protection connect will help handle one of the greatest safety challenges today by means of open requirements, that could support pave the way towards collaborative innovation. as it is developed on open necessities, it may possibly support corporations construct exciting microservices, advance new security functions, combine present security options, and leverage statistics from open shared functions. 

Key services encompass: Open protection information integration services for sharing and normalizing threat intelligence, federated records browsing throughout on-premise and cloud information repositories and safety solutions, and real-time sharing of security signals/hobbies and insights that will also be leveraged by means of any app or answer integrated with the platform. 

IBM security connect will turn into the domestic of IBM's current protection App trade and all IBM security functions developed on a platform powered by IBM Cloud, however entirely compatible with different cloud providers.  IBM security will also champion the creation of new open standards in burgeoning areas such as the sharing of response playbooks and analytics patterns and should actively invest in constructing new open supply tasks that align with these efforts.

Story Continues

IBM safety's commitment to openness additionally skill that many present open security and protocol specifications are leveraged all the way through the platform, akin to STIX™ (Structured possibility tips eXpression) and TAXII™ (trusted automated exchange of Indicator suggestions). The IBM security mission STIX-Shifter, which is publicly available on GitHub, incorporates an open source library which permits software to connect with items that condominium data repositories using STIX Patterning, and return outcomes as STIX Observations.  using these open necessities for connecting to any data source mixed with IBM safety's already potent safety analytics and incident response capabilities helps consumers to benefit broader visibility and become aware of threats and hazards that had been in any other case missed because of disconnected data throughout complicated hybrid environments. 

IBM protection connect's initial set of applications and capabilities will permit clients to instantly connect to dissimilar safety items or facts repositories to automatically federate facts for the purpose of prioritizing and responding to threats. This progressive method of federating facts permits purchasers to depart their information where it's, as adverse to building costly statistics lakes, that may complicate or outright evade protection statistics analysis.

IBM security connect will also be an integral part of IBM safety capabilities offerings each as a purchaser and a contributor of recent innovation; including advancements in voice-enabled AI, computing device studying for security threat scoring, world hazard analytics, orchestration playbooks and mobile-enabled MSS purposes.  IBM security services will leverage the open vigour of IBM protection connect with boost deep and beneficial integrations throughout its associate ecosystem to deliver stronger cost for its international clients.

Addressing Complexity in Cybersecurity Operations

Designed to carry "neighborhood-pushed" security options, IBM protection join will operate as an open platform with an open construction community.  As a part of trendy announcement, a significant variety of know-how companions and world device integrators together with Cisco, Capgemini, Carbon Black, determine point, CrowdStrike, EY, ForeScout, Forcepoint, Fortinet, McAfee, Qualys, Smarttech, Symantec, Tenable, trend Micro, and VMware have committed to integrating with IBM security connect so as to help deliver more suitable information sharing across safety companies for our joint shoppers.  many of these vendors will also contribute to building integrated purposes on IBM security connect.  IBM already has tons of of pre-built apps built through IBM and partners attainable by means of the IBM safety App exchange for integrating on the product stage. as soon as obtainable, these extra integrations might be built into IBM security join inside months of launch, widening the reach of the platform to support control compliance and handle threats.

as an instance, some of the first options being established by clients will center of attention on possibility Operations Workflow. This solution is designed to empower security analysts to proactively establish, investigate, and respond to their most critical threats from a single, cloud-primarily based answer. probability Operations Workflow integrates seamlessly via open connectors to QRadar (each on-premise or cloud), as well as different SIEMs and endpoint solutions. by using leveraging open SDKs, different safety statistics ponds or lakes equivalent to Hadoop, and element security items will also be supported to supply a federated view and workflow for security analysts throughout previously unintegrated and siloed products.    

inbuilt expertise and talents

With the smartly-documented talents problem the protection industry is dealing with, IBM safety connect will additionally characteristic digitized knowledge from IBM's four,000+ global protection practitioners to supply choicest practices and assistance on how to implement protection and possibility administration concepts. The pre-integrated apps allow users to create easy-to-use regular workflows throughout multiple applications, so teams can focal point on solving security concerns as an alternative of struggling to integrate dozens of security products. furthermore, IBM security has dedicated 50 developers towards construction of the group, the place security practitioners can collaborate and share integrations.

IBM safety connect is designed to make gaining access to IBM X-drive safety features talents even simpler for consumers in any respect degrees; including choices and talents in X-force red safety testing, Managed safety services and Incident Response functions.

IBM safety join is predicted to be obtainable in 1Q 2019.

About IBM security 

IBM safety presents probably the most superior and built-in portfolios of business protection items and capabilities. The portfolio, supported by means of world-famous IBM X-force® research, permits groups to quite simply control possibility and defend in opposition t rising threats. IBM operates probably the most world's broadest security research, development and birth corporations, screens 60 billion security events per day in more than a hundred thirty international locations, and has been granted greater than 8,000 security patents global. For more suggestions, please determine www.ibm.com/protection, comply with IBMSecurity on Twitter or discuss with the IBM security Intelligence weblog. 

CONTACT: Dillon Townsel dillon.townsel@ibm.com 512-571-3455

View customary content material to down load multimedia:http://www.prnewswire.com/information-releases/ibm-proclaims-cloud-based-community-platform-for-cyber-security-purposes-300730681.html


tons of of leading world organizations set up IBM Cloud private | killexams.com Real Questions and Pass4sure dumps

TMCNet: Hundreds of Leading Global Enterprises Deploy IBM Cloud Private

tons of of leading international corporations install IBM Cloud inner most

to satisfy transforming into demand, IBM advances on-prem private cloud with new Watson AI capabilities

ARMONK, N.Y., Oct. 11, 2018 /CNW/ -- IBM (NYSE: IBM) has introduced that in lower than twelve months since the liberate of IBM Cloud deepest – an open source technology that brings cloud capabilities to organizations working on-premises IT systems – lots of of leading enterprises international have turned to the platform to assist modernize their operations. They include New Zealand Police, China's Fuyao community, Japan's Aflac assurance, Turkey's credit score bureau Kredi Kayit Bürosu and Brazil's fidelity national suggestions features.

IBM Corporation logo. (PRNewsFoto/IBM Corporation) (PRNewsFoto/) (PRNewsfoto/IBM)

building on this momentum, IBM is asserting a slate of latest superior facets for the on-premises private cloud platform, including the mixing of effective AI capabilities comparable to IBM Watson Assistant and IBM Watson Speech-to-textual content, as well as help for further public clouds, together with the IBM Cloud. The updates give consumers with even more alternative and adaptability for their IT journeys, and, for the first time, carry the vigour of IBM's Watson AI in the back of the company firewall.

IBM is releasing particulars of over a dozen organizations which might be driving transformation with IBM Cloud deepest, including:

  • New Zealand Police (NZP) is the usage of the latest applied sciences to seriously change its operations and raise its capacity to handle crime and preserve the community protected. NZP is exploring how IBM Cloud inner most and Kubernetes containers can help to modernize its present techniques as well as instantly launch new capabilities equivalent to a cell based mostly communications equipment for its officers.
  • Fuyao group is China's biggest and most superior manufacturer of automobile glass presenting Audi, Bentley, universal Motors, Toyota and different excellent car makers. to fulfill transforming into demand, Fuyao has launched into a smart Manufacturing initiative and is the use of IBM Cloud private to assist integrate the administration of enterprise applications throughout its world manufacturing and enterprise operations, guaranteeing the free flow of suggestions across the company.
  • Aflac coverage, Aflac insurance, a leading provider of scientific and cancer assurance providing insurance policy to 1 in 4 households in Japan, has signed an agreement with IBM to assist simplify and automate its IT infrastructure. With IBM Cloud inner most, Aflac plans to increase the efficiency of its operations and velocity up the building of new items and features.
  • Kredi Kayit Bürosu (KKB) provides the country wide cloud infrastructure for Turkey's finance trade. last 12 months KKB dependent a new statistics core in Ankara and is now using IBM cloud inner most to help consolidate and localize the banking programs of over a hundred and fifty members together with banks and other monetary features agencies. With IBM Cloud deepest, KKB is capable of power innovation throughout its fiscal capabilities ecosystem whereas assembly the excessive requirements for statistics protection and privateness.
  • Brazil's fidelity national assistance capabilities (FIS), a global leader in charge processing options for the financial trade, has adopted IBM Cloud deepest to assist streamline credit card chargebacks for its Brazilian operations. IBM Cloud inner most is assisting FIS to orchestrate the a number of IT systems involved in reconciling and checking of transaction chargebacks whereas meeting the stringent regulatory requirements of the trade and helping to keep away from fraud.
  • "The cloud has evolved in a extremely short time from being a way to cut prices to a platform for enterprise transformation and innovation," spoke of Robin Hernandez, Director, IBM deepest Cloud offering management. "Our IBM Cloud private affords valued clientele a transition to cloud it's designed to allow them to maintain their on-premises programs on which they nonetheless depend for many mission-critical functions and sensitive information units."

    putting AI inner the EnterpriseToday IBM is advancing IBM Cloud inner most even further, saying primary enhancements to the platform, ranging from AI capabilities to additional public cloud assist. beginning today, IBM Watson Speech-to-textual content is now obtainable with the brand new IBM Cloud private edition 3.1, making it the first time the tool is obtainable in an on-premises atmosphere. With Watson Speech-to-text, users can immediately transcribe and analyze audio from seven distinctive languages.

    moreover, IBM Watson Assistant is now available on IBM Cloud deepest. The Watson Assistant platform helps builders and non-technical users to create conversational AI options, from fundamental chatbots to advanced business grade options for customer carrier and more.

    IBM also announced these days that IBM Cloud inner most is now purchasable on the IBM Public Cloud, to support clients to with ease circulation their enterprise workloads from on-premises methods to the public cloud to be able to create hybrid cloud environments.

    customer quote"IBM Cloud inner most is assisting us to set up an conclusion-to-end digital platform so one can underpin a smarter, extra agile, and greater bendy company operating constitution," stated Mr. Lebing Xia, vp and CIO of the Fuyao community. "And with the aid of enabling us to hook up with other cloud-based capabilities like AI, Blockchain and IoT, we should be able to get much more business cost from the cloud."

    Analyst quoteAccording to IDC's international Quarterly 2Q18 Cloud IT Infrastructure Tracker, September 2018, total international spending on cloud IT infrastructure is expected to total $62.2 billion in 2018 with 12 months-over-year increase of 31.1%. while public cloud datacenters will account for the majority of this spending, on-premises deepest clouds will account for very nearly 20% of the overall cloud IT infrastructure market, transforming into at 19.3% yr over year in 2018. "firms have more than a few enterprise and IT needs a few of which are neatly served through public cloud offerings and a few which are not," pointed out Natalya Yezhkova, research Director, IDC. "On-premises private cloud is an impressive choice for based groups that are seeking for a gradual strategy for remodeling their organization whereas protecting control of their IT infrastructure."

    About IBM CloudWith $18.5B in annual cloud profits, IBM is the world leader in business cloud with a platform designed to meet the evolving needs of company and society. relocating past productiveness and price advancements, the IBM Cloud is tuned for the AI and data demands that are using real differentiation in modern commercial enterprise. IBM's inner most, public and hybrid choices deliver the international scale agencies should guide innovation throughout industries.

    developed on open supply frameworks Docker containers and Cloud Foundry, and managed with the Kubernetes container structure, IBM Cloud deepest makes it possible for corporations to enhance cloud-native functions and also modernize on-premises functions in an effort to installation them in either deepest cloud settings or the public cloud of their option.

    Media contacts:Jonathan BattyIBM Media family members (Europe)jjbatty@ibm.com

    Michael ZimmermanIBM Media members of the family (US)mrzimmerman@us.ibm.com

    Notes for journalists and editorsExamples of different main international organisations reworking with IBM Cloud deepest:

    CNH Industrial, ItalyCNH Industrial, a frontrunner within the capital items sector operating in the agricultural and building gadget, commercial motors, area of expertise motors and powertrain segments, has signed a multi-year cloud settlement with IBM. through the deal it will use IBM Cloud deepest and Watson synthetic Intelligence to seriously change its business approaches - from manufacturing to give chain, earnings & marketing and economic services. IBM Cloud deepest will aid CNH Industrial to optimize its management of commercial enterprise purposes whereas still benefitting from the safety of handle of its personal on-premises infrastructure. The agility and flexibility of ICP has the long-term advantage of producing improved efficiencies in utility construction and will permit quicker time to market for brand spanking new innovative client capabilities.

    Klirinski Interbankarski Sistemi ad Skopje (KIBS), Macedonia Klirinski Interbankarski Sistemi advert Skopje (KIBS), a Macedonia-based payment gadget operator owned by way of 12 banks is working with IBM to seriously change payment clearing tactics and help quicker the mixing of Macedonia's banking equipment with the european. With IBM Cloud inner most KIBS might be capable of consolidate and seriously change their existing substances into a "digital manufacturing unit" in a position to creating and operating up to date cloud-native applications on the fly. it is hoped that this transformation will reduce time to market for brand spanking new digital products from one-and-a-half years to simply a couple of months, while optimising working prices and helping KIBS' compliance activities concerning stringent banking laws.

    Sentient.io, SingaporeSentient.io, considered one of Singapore's leading artificial Intelligence businesses, is working with IBM to deliver on its mission to enhance well-known lifestyles with the aid of making human and desktop interactions more herbal and pervasive.Sentient.io has chosen the IBM Cloud deepest as a platform to host Singapore's first AI-as-a-provider (AaaS) platform to allow its customers to seriously change and automate their organizations with AI capabilities reminiscent of notion, understanding, self-gaining knowledge of and difficulty-solving. using IBM Cloud deepest, Sentient.io will help executive and enterprise purchasers to satisfy strict records protection and privateness requirements, modernize current applications by using relocating them to the cloud, and comfortably manipulate the lifecycle of their digital assets.

    Ilmarinen, FinlandIlmarinen, the greatest private insurer in Finland chargeable for the pensions of over 1.1 million finish residents and more than EUR forty five billion in investment assets, has shifted functions to IBM Cloud private to enable it to strengthen new digital capabilities whereas maximizing its existing IT infrastructure. by inserting its software construction pipeline onto IBM Cloud deepest, Ilmarinen is capable of enormously velocity up construction cycles and cut back infrastructure expenses, cutting the full software free up time from days to minutes. here is above all essential with increased drive on the world's pension systems because of an aging population.

    Techniker Krankenkasse (TK), GermanyGermany's largest statutory health insurance fund Techniker Krankenkasse (TK) is the use of IBM Cloud deepest for a brand new service called TK-protected which gives clients with their medical health insurance statistics by the use of their smartphone - in spite of time and placement. IBM is already providing information as-a-provider to TK-secure's relaxed facts vault which contains the encrypted health records of TK's insurees across Germany. by way of an open service platform, TK-safe clients can improvement from new value-addedservices for his or her own health care and are in a position to share suggestions with their physicians, hospitals and service suppliers.

    Mangalore institution, IndiaMangalore university, considered one of India's proper public training associations, will give new-age courses in rising technologies through its affiliated faculties and is leveraging the energy of IBM Cloud private to deliver a common platform that can also be accessed remotely by way of any student. IBM Cloud private will run on the tuition's IBM power servers as part of a complete solution which also includes QRadar, MaaS360 and Cognos utility. The solution may be integrated with the aid of Nihon conversation solutions, an education birth companion of IBM, which is accountable for the start of advanced lessons on cybersecurity and massive information Analytics and provides high-efficiency systems for network and safety purposes.

    SK Holdings C&C, Ltd., South KoreaSK Holdings C&C, a leading IT features firm and IBM business accomplice in South Korea turned to IBM Cloud deepest to right away and easily package functions as containers and re-aspect them with IBM microservices. additionally, the business is leveraging the software to assist valued clientele modernize their legacy functions and provide a at ease platform from which to run enterprise crucial software across each public and private clouds, in a true multi-cloud structure.

    Five9 Vietnam Corp., VietnamFive9 Vietnam is a fast-becoming IT and cloud provider provider that gives its shoppers with every little thing from cellular and analytics application construction, to deepest and public cloud capabilities. The company has been using IBM Cloud private on account that April to design, strengthen, and equipment functions as containers for handy deployment on the general public cloud. From there, their clients can tap into IBM microservices, similar to analytics, AI and blockchain, to extend their performance even additional.

    Cision View long-established content to down load multimedia:http://www.prnewswire.com/news-releases/tons of-of-leading-international-corporations-deploy-ibm-cloud-inner most-300729130.html

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    C5050-384 IBM Cloud Platform Application Development v2

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    SoftLayer IaaS v2.1 Manager Now Available for the Ensim Automation Suite | killexams.com real questions and Pass4sure dumps

    Enables Service Providers to Expand Their IaaS Offering Portfolio With a Complete Quote to Cash Process

    SAN JOSE, CA--(Marketwired - Mar 15, 2016) - Ensim Corporation, a leading provider of an end-to-end platform for monetizing any offering with marketplace, subscription management, service catalog, provisioning, usage collection, and billing for service providers, MSPs, and enterprises, today announced the availability of a major update to their integrated solution with IBM Cloud / SoftLayer. The integration with the Ensim Automation Suite enables complete customer life-cycle management with full usage collection, rating, payment, and billing.

    The Ensim Automation Suite enables the complete quote to cash process, from the initial order configuration, quote, approval process, notifications, deployment, and usage tracking, to billing and payment for the full array of SoftLayer offerings. Providers can bundle with 100s of other XaaS applications that are pre-integrated with the Ensim Automation Suite for monthly, yearly, or flexible subscription and payment plans. Single sign-on directly via the Ensim portal to the SoftLayer administrative console ensures ease of management, while automated usage collection ensures accounts are always in sync and accurate. Delegated administration portals enable simplified and automated user and administrator self-service at all levels (customer, agent, reseller, service provider).

    "The development of Ensim Service Manager for IBM Cloud / SoftLayer enables providers to expand their IaaS offering portfolio and bundling a variety of add-on services, while effectively maintaining business objectives and enforcing policy requirements," said David Wippich, CEO, Ensim. "Ensim is proud to offer a comprehensive platform that enables providers to offer, bundle and differentiate the SoftLayer offerings from a single portal."

    Ensim Automation Suite and Ensim SoftLayer IaaS Manager v2.1 Key Features:

  • Cloud Marketplace with Catalog, Quoting, Ordering
  • Channel enablement for Resellers and Agents
  • Integration to all SoftLayer Datacenter locations
  • Virtual machine management: Create, Start, Stop, Clone, Edit
  • Create and Assign Administrators
  • Bi-directional sync of User accounts
  • Detailed usage collection and reports with parameter details
  • Accurate Invoicing and Cost of Goods reconciliation
  • "Service Providers looking to offer IaaS now have the option of deploying an on-premise virtual machine management platform (Microsoft, VMware or OpenStack) or can leverage SoftLayer's cloud based IaaS platform to meet the demands of their customers," said William Fellows, Research Vice President of 451 Research. "The Ensim Automation Suite offers Private, Public and Hybrid IaaS management from a single portal and provides robust end-to-end ordering, provisioning, and billing all through a single pane of glass."

    About Ensim Ensim provides an end-to-end platform for marketplace, storefront, subscription management, service catalog, ticketing, provisioning, usage collection, and billing management with self-service portals for users, organizations, and channels. Monetize and manage any offering such as business applications, infrastructure, cloud brokerage, as well as SaaS, IaaS and ITaaS. The Ensim Automation Suite is ready-to-launch as an on-premise or cloud based solution; can integrate with any existing back office environment; and is available in both multi-tenant and dedicated versions to meet service provider and enterprise requirements. For additional information, visit http://www.ensim.com

    About SoftLayer SoftLayer, an IBM Company, provides cloud infrastructure as a service from a growing number of data centers and network points of presence around the world. Customers range from Web startups to global enterprises. Products and services include bare metal and virtual servers, networking, turnkey big data solutions, private cloud solutions, and more. Unique advantages include the industry's first Network-Within-a-Network topology for true out-of-band access, and an easy-to-use customer portal and robust API for full remote-access of all product and service management options. SoftLayer was founded in 2005 and is headquartered in Dallas, Texas. SoftLayer was acquired by IBM in July, 2013. For additional information, visit http://www.softlayer.com


    How to Choose a Video AI Platform and Evaluate its Results | killexams.com real questions and Pass4sure dumps

    How to Choose a Video AI Platform and Evaluate its Results

    Meet the big four players in the video artificial intelligence space, then learn how they can speed up time-consuming tasks like generating metadata or creating transcriptions.

    Learn more about the companies mentioned in this article in the Sourcebook:

    Why is artificial intelligence (AI) a buzzword right now? The more video content we create, the larger our media libraries get. We only have a limited amount of humans on our staff to ingest that media, process it, and add metadata tags to it.

    Our organizations need help making this content more searchable so we can get some more ROI on our content. Let’s say you work for a surveillance firm, and you have 24/7 feeds running on 10 cameras or 100 cameras. You may not be able to get enough human help to search that media, but you could get AI help.

    On the audio side, there’s great demand for transcription, and there are closed-caption mandates and translation needs as well. We don’t all have translators on staff, but if we have AI translation in our system, even an imperfect translation will help a lot.

    The Big Four

    There are four key players in video AI: Amazon Web Services (AWS), Google Cloud, IBM Watson Media, and Microsoft Azure’s Video Indexer. Each has numerous services.

    The four core services that AWS has for AI are Rekognition, for computer vision and adding deep learning-based visual search and image classification; Comprehend, which tries to understand sentiment and what’s going on within the language; Transcribe, which converts audio to text files; and Translate.

    The Google Cloud platform features Video Intelligence, Natural Language API, Speech-to-Text, and the Translation API.

    IBM Watson Media is AI for media workflow and video processing, but the big difference between IBM’s platform and the others is that it requires breaking the video into individual segments. The three products created under the banner of Watson Media are Video Recommendations, Captioning (speech-to-text), and Video Enrichment (computer vision).

    The fourth is Microsoft Azure, aka Video Indexer. To access Microsoft’s cognitive and indexing features, go to videoindexer.ai, where there’s a cool user interface you can log into and upload videos to, and start getting AI-generated metadata right away for free.

    This article will look at these platforms and services in terms of four main features: face/object/scene recognition, sentiment analysis, surveillance, and transcription/translation. When choosing a platform, keep in mind that it’s all about the use case. All four platforms return JSON (JavaScript Object Notation) data, so there’s nothing stopping you from using all of them if you want to. You can see a demo of a custom app that my company, RealEyes Media, built on top of AWS and Google Cloud, in a clip from a presentation I did at Streaming Media East 2018.

    How to Get Started

    If you don’t have developers in-house at your organization, a lot of these vendors offer demo versions of their platforms that you can play around with (Figure 1). The free version of Microsoft Azure Video Indexer, for example, is quite robust. You can actually take widgets and embed them into your own systems. Of the four platforms we discuss in this article, Video Indexer is the most complete product you can access without having to go through contract negotiations.

    Demo versions of leading video AI platforms, clockwise from left: Google Cloud, IBM Watson Media, Microsoft Azure Video Indexer, AWS

    Google Cloud and AWS offer demos on their pages, but they don’t really expose the services to the degree that you can embed them into your own system. In this context, “embed” essentially means writing some code to get an iFrame to load on a page inside of your site. The online demo versions of Google Cloud and AWS aren’t really conducive to that, but you can upload your own videos, wait for the apps to process them, and see how well the service will work with your videos.

    IBM sells a couple of Watson products, such as Video Enrichment (top right in Figure 1). They are API-first. Theoretically, you can buy this paid product and communicate between your system and IBM’s to pull metadata into your system.

    Generating metadata is one of the big use cases for AI. Let’s say you’ve archived multiple seasons of a TV show, and you have a media asset manager (MAM) that houses all this metadata about all of those stored assets. Wouldn’t it be great to have the computer generate some of that metadata for you, so that in addition to the title, season, and episode name, you’d have things like “car crash,” “leopard,” and “little baby girl” (or whatever terms apply to your content) that you can actually use to search and discover your content much more easily? The first thing your developers are going to want to know about is, “What’s the learning curve? What software development kit (SDK) is available for each of these platforms?”

    AWS has a client SDK, which basically acts as an accelerator so your developers don’t have to write all of the code from scratch. They can build their code on top of the boilerplate AWS provides, with SDKs for Android, iOS, Java, .NET, etc. My team of developers has played around with this SDK, and they have found it comprehensive.

    One important step in choosing a video AI platform is to let your developers test whatever demo version is available. And listen to their feedback, because your team members are going to be more productive if they don’t have to spend as much time trying to grok how to work with the software.

    Like AWS, the Google Cloud platform has a good number of client SDKs: .NET, Node.js, Go, Java, etc. But from my developers’ perspectives, Google’s API documentation is quite verbose, and it takes a lot of clicks to get to what you need—much more so than the AWS developer documentation, in their opinion. For instance, if you just want to know how to send a video up to the Google service, rather than seeing the signature of the payload that you want to upload, you have to wade through three or four paragraphs on every single data point within that signature.

    IBM Watson Media's API reference lives behind a paywall. When you want to use it, you tell the IBM Watson Media people, “This is my use case. I want to play around with your system, because I think I’m going to buy it.” Then you sign some contracts. In my case, as an IBM partner, they were kind enough to share the API documentation with me. It looked pretty straightforward.

    Azure has client SDKs, but to interact with the Video Indexer you will just use its API without an SDK. If you need the extra lift that an SDK can provide, then Video Indexer might not be the right solution for you. Amazon and Google have things like Java, Android, iOS, Ruby, and so on, as you can see in Figure 2. Your developers might be able to build something faster on those platforms. The Video Indexer documentation is outstanding. It’s well-laid-out, and you can inline-test it as long as you have an active account.

    API Learning Curve/SDK rundown for AWS, Google Cloud, IBM Cloud/Watson Media, and Microsoft Azure Video Indexer

    Testing the Platforms

    I’ve tested lots of different kinds of footage, but because so much data comes back, I’ve narrowed the focus in my testing to emphasize specific use cases. When it comes to translation and transcription, video surveillance, or metadata and objects, which platforms work best in those cases?

    One thing to keep in mind with AI is that AI is only as smart as the data you’ve trained it with. In the case of IBM Watson, if you go to the website and look at some of the case studies, you’ll see that the company did a project for the US Open tennis tournament which was instrumental in training its AI. If you’re in the sports industry, IBM Watson might have more accurate results for you when it comes to object detection or seeing when a ball is being hit across the court. On the other hand, if you do news footage, Google might serve you better, because users live stream their news over YouTube.

    It’s important to consider these different kinds of use cases when assessing how well a particular platform will work for you, because ultimately, the data that’s being processed by all these machine-learning solutions is what’s training the AI to be smarter. It’s similar to any process of accumulating knowledge. I come from a developer background, but I’m in the streaming media industry. If you ask me about which camera is the best camera, I won’t have a good answer, because I don’t have that data in my background.

    That’s a way to think about AI: You’re going to be sending it specific data, and it’s going to learn off that data. The type of data that it learns off of is going to make it more accurate in one way or another.

    Interpreting the Data

    Figure 3 shows an example of the kind of data you get back with these systems. Generally, AI solutions work like this: They have an API that allows your developers to hit an end point and pass some data in. It’s similar to you going into a browser, typing “google.com,” searching for “ducks,” and hitting Enter. A developer uses an API to programmatically hit an end point like google.com. It might be something like “myaiservice.com/v2/api/visionservices/detectObjects” and you would pass it a data payload that the API expects.

    Sample data from a processed video payload on an AI platform. Note that, since this screenshot was taken in May 2018, Microsoft Azure Video Indexer has started providing confidence values in its response JSON (click to enlarge).

    Again, it’s like when you go to Google and type in a search string and then hit Enter. A developer will send up a payload that says, “My video is here. Please process it.” Then you wait for the processing to happen, because it’s not instantaneous. Once the processing is done, you’ll have data that you can pull down, comparable to search results in Google.

    All of these services use JSON, the data exchange format shown in Figure 3. This is great, because it enables you to get data from multiple services normalized and used in a custom application. When you’re working with one format, it makes it a lot easier to parse through the immense amount of data you get back.

    In the example shown in Figure 3, I sent up a trailer of American Psycho. You can see the data it returned in AWS on the far left. It says, “I found a person at timestamp 18196 milliseconds, and I’m 97.8222 percent sure.” That’s the data you get back with object detection in AWS.

    With Azure Video Indexer (second column from left), it says, “I found a person in the time range 00:00:21.438 to 00:00:22.14.” In the Google column (third from left), you can see that it identified a “human” “entity” in multiple instances, with time signatures in seconds and nanos, and confidence values in the 85 percent–96 percent range.

    Keep in mind that the computer doesn’t actually “see” a person; it’s learning from all of the data that it’s processed historically and making its best judgment based on that, so knowing the confidence value is extremely important. If your primary interest is identifying objects and you want it to be accurate, you should definitely pay attention to the confidence values being reported.

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    Comparing Kubernetes to Pivotal Cloud Foundry — A Developer’s Perspective | killexams.com real questions and Pass4sure dumps

    All this means that with PCF, your build artifact is your native deployment artifact, while in Kubernetes your build artifact is a docker image. With Kubernetes, you need to define the template for this docker image yourself in a Dockerfile, while in PCF you get this template automatically from a buildpack.

    Management Console Cloud Foundry

    PCF separates the web dashboard to two separate target audiences.

  • Ops Manager is targeted at the IT professional that is responsible for setting up the virtual machines or hardware that will be used to create the PCF cluster.
  • Apps Manager is targeted at the developer that is responsible for pushing application code to testing or production environments. The developer is completely unaware of the underlying infrastructure that runs its PCF cluster. All he can really see is the quotas assigned to his organization, such as memory limits.
  • Kubernetes

    Kubernetes takes a different approach. You get one dashboard to manage everything. Here’s a typical Kubernetes dashboard:

    As you can see from the left-hand side, there is a lot of data to process here. You have access to persistent volumes, daemons, definition of roles, replication controllers etc. It’s hard to focus on what are the developer’s needs and what are the IT needs. Some might tell you this is the same person in a Devops culture, and that’s a fair point. Still, in reality-it is a more confusing paradigm compared to a simple application manager.

    Command Line Interface Cloud Foundry

    Cloud foundry uses a command line interface called cf. It is a cli that lets you control all aspects of the developer interaction. Following in the footsteps of simplicity that you might have already noticed, the idea is to take an opinionated view to practically everything.

    For example, if you are in a folder that contains a spring boot jar file called myapp.jar, you can deploy this application to PCF with the following command:

    cf push myapp -p myapp.jar

    That’s it! That’s all you need. PCF will lookup the current working directory and find the jar executable. It will then download the java buildpack, create a container, calculate the required memory settings, delpoy it to the currently logged-in org and space in PCF, and set a route based on the application name:

    wabelhlp0655019:test odedia$ cf push myapp -p myapp.jar Updating app myapp in org OdedShopen / space production as user… OK Uploading myapp… Uploading app files from: /var/folders/_9/wrmt9t3915lczl7rf5spppl597l2l9/T/unzipped-app271943002 Uploading 977.4K, 148 files Done uploading OK Starting app myapp in org OdedShopen / space production as user…Downloading pcc_php_buildpack…Downloading binary_buildpack…Downloading python_buildpack…Downloading staticfile_buildpack…Downloading java_buildpack…Downloaded binary_buildpack (61.6K)Downloading ruby_buildpack…Downloaded ruby_buildpackDownloading nodejs_buildpack…Downloaded pcc_php_buildpack (951.7K)Downloading go_buildpack…Downloaded staticfile_buildpack (7.7M)Downloading ibm-websphere-liberty-buildpack…Downloaded nodejs_buildpack (111.6M)Downloaded ibm-websphere-liberty-buildpack (178.4M)Downloaded java_buildpack (224.8M)Downloading php_buildpack…Downloading dotnet_core_buildpack…Downloaded python_buildpack (341.6M)Downloaded go_buildpack (415.1M)Downloaded php_buildpack (341.7M)Downloaded dotnet_core_buildpack (919.8M)Creating containerSuccessfully created containerDownloading app package…Downloaded app package (40.7M)Staging…— — -> Java Buildpack Version: v3.18 |https://github.com/cloudfoundry/java-buildpack.git#841ecb2— — -> Downloading Open Jdk JRE 1.8.0_131 from https://java-buildpack.cloudfoundry.org/openjdk/trusty/x86_64/openjdk-1.8.0_131.tar.gz (found in cache)Expanding Open Jdk JRE to .java-buildpack/open_jdk_jre (1.1s)— — -> Downloading Open JDK Like Memory Calculator 2.0.2_RELEASE from https://java-buildpack.cloudfoundry.org/memory-calculator/trusty/x86_64/memory-calculator-2.0.2_RELEASE.tar.gz (found in cache)Memory Settings: -Xmx681574K -XX:MaxMetaspaceSize=104857K -Xss349K -Xms681574K -XX:MetaspaceSize=104857K — — -> Downloading Container Security Provider 1.5.0_RELEASE from https://java-buildpack.cloudfoundry.org/container-security-provider/container-security-provider-1.5.0_RELEASE.jar (found in cache) — — -> Downloading Spring Auto Reconfiguration 1.11.0_RELEASE from https://java-buildpack.cloudfoundry.org/auto-reconfiguration/auto-reconfiguration-1.11.0_RELEASE.jar (found in cache) Exit status 0Uploading droplet, build artifacts cache…Uploading build artifacts cache…Uploading droplet…Staging completeUploaded build artifacts cache (109B)Uploaded droplet (86.2M)Uploading completeDestroying containerSuccessfully destroyed container0 of 1 instances running, 1 starting0 of 1 instances running, 1 starting0 of 1 instances running, 1 starting0 of 1 instances running, 1 starting1 of 1 instances running

    Although you can start with barely any intervention, this doesn’t mean you give up any control. You have a lot of customizations available in PCF. You can define your own routes, set the number of instances, max memory and disk space, environment variables etc. All of this can be done in the cf cli or by having a manifest.yml file available as a parameter to the cf push command. A typical manifest.yml file can be as simple as the following:

    applications:- name: my-appmemory: 512Minstances: 2env:PARAM1: PARAM1VALUEPARAM2: PARAM2VALUE

    The main takeaway is this: with PCF, provide the information you know, and the platform will imply the rest. Cloud Foundry’s haiku is:

    Here’s my code

    Run it on the cloud for me.

    I don’t care how.

    Kubernetes

    In kubernetes, you interact with the kubectl cli. The commands are not complicated at all, but there is still a higher learning curve from what I’ve experienced so far.

    For starters, a basic assumption is that you have a private docker registry available and configured (unless you only plan to deploy images available on public registries such as docker hub). Once you have that registry up and running, you will need to push your Docker image to that registry.

    Now that the registry contains your image, you can initiate commands to kubectl to deploy the image. Kubernetes documentaiotn gives the example of starting up an nginx server:

    # start the pod running nginx$ kubectl run --image=nginx nginx-app --port=80 --env="DOMAIN=cluster"deployment "nginx-app" created

    The above command only spins up a kubernetes pod and runs the container.

    A pod is an abstraction that groups one or more containers to the same network ip and storage. It’s actually the smallest deployable unit available in Kubernetes. You can’t access a docker container directly, you only access its pod. Usually, a pod would contain a single docker container, but you can run more. For example, an application container might want to have some monitoring dameon container in the same pod.

    In order to make the container accessible to other pods in the Kubernetes cluster, you need to wrap the pod with a service:

    # expose a port through with a service$ kubectl expose deployment nginx-app --port=80 --name=nginx-httpservice "nginx-http" exposed

    Your container is now accessible inside the kubernetes cluster, but it is still not exposed to the outside world. For that, you need to wrap your service with an ingress.

    Note: Ingress is still considered a beta feature!

    I could not find a simple command to expose an ingress at this point (please correct me if I’m wrong!). It appears that you must create an ingress descriptor file first, for example:

    apiVersion: extensions/v1beta1kind: Ingressmetadata:name: test-ingressannotations:ingress.kubernetes.io/rewrite-target: /spec:rules:- http:paths:- path: /testpathbackend:serviceName: testservicePort: 80

    Once that file is available, you can create the ingress by issuing a command

    kubectl create -f my-ingress.yaml

    Note that unlike the single manifest.yml in PCF, the deployment yml files in Kubernetes are separated — there is one for pod creation, one for service creation and as you saw above — one for ingress creation. A typical descriptor file is not entirely overwhelming but I wouldn’t call it the most user friendly either. For example, here’s a descriptor file for nginx deployment:

    apiVersion: apps/v1 # for versions before 1.9.0 use apps/v1beta2kind: Deploymentmetadata:name: nginx-deploymentlabels:app: nginxspec:replicas: 3selector:matchLabels:app: nginxtemplate:metadata:labels:app: nginxspec:containers:- name: nginximage: nginx:1.7.9ports:- containerPort: 80

    All this to say — with kubernetes, you need to be specific. Don’t expect deployments to be implied. If I had to create a haiku for Kubernetes, it’ll be probably something like this:

    Here’s my code

    I’ll tell you exactly how you should run it on the cloud for me

    And don’t you dare make any assumptions on the delployment without my written consent!

    Zero Downtime Deployments

    Both platforms support the ability to deploy applications with zero downtime, however this is one area where Kubernetes wins in my opinion, since it provides a built-in mechanism for zero downtime deployments with rollback.

    Cloud Foundry

    With Pivotal Cloud Foundry, there’s no built-in mechanism to support a rolling update, you’re basically expected to do some cf cli trickery to perform the update with zero downtime. The concept is called blue-green deployment. If I had to explain it in step-by-step guide, it’ll probably be something like this:

  • Starting point: you have myApp in production, and you want to deploy a new version of this app — v2.
  • Deploy v2 under a new application name, for example — myApp-v2
  • The new app will have its own initial route — myApp-v2.mysite.com
  • Perform testing and verification on the new app.
  • Map an additional route to the myApp-v2 application, using the same route as the original application. For example:
  • cf map-route myApp-v2 mysite.com —hostname myApp
  • Now requests to your application are load balanced between v1 and v2. Based of the number of instances available to each version, you can perform A/B testing. For example — if you have 4 instances of v1 and 1 instance of v2, 20% of your clients will be routed to the new codebase.
  • If you identify issues at any point — simply remove v2. No harm done.
  • Once you are satisfied, scale the number of available instances of v2, and reduce or completely delete the instances of v1.
  • Remove the myApp-v2.mysite.com route from v2 of your application. You have now fully migrated to the new codebase with zero downtime, including sanity testing phase and potentially A/B testing phase.
  • Note: The cf cli supports plugin extensions. Some of them provide automated blue-green deployments, such as blue-green-deploy, autopilot and zdd. I personally found blue-green-deploy to be very easy and intuitive, especially due to its support for automated smoke tests as part of the deployment.

    Kubernetes

    kubectl has a built-in support for rolling updates. You basically pass a new docker image for a given deployment, for example:

    kubectl set image deployments/kubernetes-bootcamp kubernetes-bootcamp=jon/kubernetes-bootcamp:v2

    The command above tells kubernetes to perform a rolling update between all pods of the kubernetes-bootcamp deployment from its current image to the new v2 image. During this rollout, your application remains available.

    Even more impressive — you can always revert back to the previous version by issuing the undo command:

    kubectl rollout undo deployments/kubernetes-bootcamp External Load Balancing

    As we saw previously, both PCF and Kubernetes provide load balancing for your application instances/pods. Once a route or an ingress is added, your application is exposed to the outside world.

    If we’ll take an external view of the levels of abstraction that are needed to reach your application, we can describe them as follows:

    Kubernetes ingress → service → pod → container Cloud Foundry route → container Internal Load Balancing (Service Discovery) Cloud Foundry

    PCF supports two methods of load balancing inside the cluster:

  • Route-based load balancing, in the traditional server-side configuration. This is similar to external load balancing mentioned above, however you can specify certain domains to only be accessible from within PCF, thereby making them internal.
  • Client side load balancing by using Spring Cloud Services. This set of services offers features from the Spring Cloud frameworks that are based on Netflix OSS. For service discovery, Spring Cloud Services uses Netflix Eureka.
  • Eureka runs as its own service in the PCF environment. Other applications register themselves with Eureka, thereby publishing themselves to the rest of the cluster. Eureka server maintains a heartbeat health check of all registered clients to keep an up-to-date list of healthy instances.

    Registered clients can connect to Eureka and ask for available endpoints based on a service id (the value of spring.application.name in case of Spring Boot applications). Eureka would return a list of all available endpoints, and that’s it. It is up to the client to actually access one of these endpoints. That is usually done using frameworks like Ribbon or Feign for client-side load balancing, but that is an implementation detail of the application, and not related to PCF itself.

    Client-side load balancing can theoretically scale better since each client keeps a cache of all available endpoints, and can continue working even if Eureka is temporarily down.

    If your application already uses Spring Cloud and the Netflix OSS stack, PCF fits your needs like a glove.

    Kubernetes

    Kubernetes uses DNS resolution to identify other services within the cluster. Inside the same namespace, you can lookup another service by its name. In another namespace, you can lookup the service’s name followed by a dot and then the other namespace.

    The major benefit that Kubernetes’ load balancing offers is not requiring any special client libraries. Eureka is mostly targeted at java-based applications (although solutions exist for other languages). With Kubernetes, you can make load-balanced http calls to any Kubernetes service that exposes pods, regardless of the implementation of the client or the server. The load-balancing domain name is simply the name of the service that exposes the pods. For example:

  • You have an application called my-app in namespace zone1
  • It exposes a GET /myApi REST endpoint
  • There are 10 pods of this container in the cluster
  • You created a service called my-service that exposes this application to the cluster
  • From any other pod inside the namespace, you can call:
  • GET https://my-service/myApi
  • From any other pod in any other namespace in the cluster, you can call:
  • GET https://my-service.zone1/myApi

    And the API would load-balance over the available instances. It doesn’t matter if your client is written in Java, PHP, Ruby, .NET or any other technology.

    Marketplace

    PCF offers a services marketplace. It provides a ridiculously simple way to bind your application to a service. The term service in PCF is not the same as a service in kubernetes. A PCF service binds your application to things like a database, a monitoring tool, a message broker etc. Some services that are available out of the box are:

  • Spring Cloud Services, which provides access to Eureka, a Config Server and a Hystrix Dashboard.
  • RabbitMQ message broker.
  • MySQL.
  • Third party vendors can implement their own services as well. Some of the vendor offerings include Datastax for Cassandra and Redislabs for Redis in-memory database. Here’s a screenshot of available services on Pivotal Web Services:

    IBM Bluemix is another Cloud Foundry provider that offers its own services such as IBM Watson for AI and machine learning applications.

    Every service has different plans available based on your SLA needs, such as a small database for development or a highly-available database for a production environment.

    Last but not least, you have the option to define user-provided services. These allow you to bind your application to an existing service that you already have, such as an Oracle database or an Apache Kafka message broker. A user provided service is simply a group of key-value pairs that you can then inject into your application as environment variables. This offloads any specific configuration such as URLs, usernames or passwords to the environment itself — services are bound to a given PCF space.

    Kubernetes

    Kubernetes does not offer a marketplace out of the box. There is a service catalog extension that allows for a similar service catalog, however it is still in beta.

    Note that since it can run any docker container — Dockerhub can be considered as a kubernetes marketplace in a way. You can basically run anything that can run in a container.

    Kubernetes does have a concept similar to user-provided services. Any configuration or environment variables can exist in ConfigMaps, which allow you to externalise configuration artifacts away your container, thus making it more portable.

    Configuration

    Speaking of configuration — One of the features of the Spring Cloud Services service is Spring Cloud Config. It is another service that is targeted specifically for Spring Boot applications. The config service serves configuration artifacts from a git repository of your choosing, and allows for zero-downtime configuration changes. If your Spring beans are annotated with @RefreshScope, they can be reloaded with updated configuration by issuing a POST /refresh API call to your application. The property files that are available as configuration sources are loaded based on a pre-defined loading order, which provides some sort of an inheritance-based mechanism to how the configuration is loaded. It’s a great solution, but again assumes that your applications are based on the Spring Boot stack. If you’re already using spring boot with a config server today — PCF fits like a glove.

    In Kubernetes, you can still run a config server as a container, but that would probably become an unneeded operational overhead since you already have built-in support for ConfigMaps. Use the native solution for the platform you go with.

    Storage Volumes

    A big differentiator of Kubernetes is the ability to attach a storage volume to your container. Kubernetes uses etcd as a means to manage storage volumes, and you can attach such a volume to any of your containers. This means you get a reliable storage solution, which lets you run storage-based containers like a database or a file server.

    In PCF, your application is fully stateless. PCF follows the 12-factor apps model and one of these models assumes that your application has no state. You should theoretically take the same application that runs today in your on-prem data center, move it to AWS, and provided there is adequate connectivity, it should just work. Any storage-based solution should be offloaded to either a PCF service, or to a storage solution outside the PCF cluster itself. This may be regarded as an advantage or a disadvantage depending on your application and architecture. For stateless application runtimes such as web servers, it is always a good idea to decouple it from any internal storage facility.

    Onboarding Kubernetes

    Getting started with Kubernetes was not easy. As mentioned above, you can’t just start with a 5-minutes quick start guide, there are just too many things you need to know and too many assumptions about what you already have (docker registry and a git repository are often taken for granted).

    Just taking a look at the excellent Kubernetes Basics interactive tutorial shows the level of knowledge required on the platform. For a basic on-boarding, there are 6 steps, each one of them containing quite a few commands and terminologies you need to understand. Trying to follow the tutorial on a local minikube vm instead of the pre-configured online cluster is quite difficult.

    Cloud Foundry

    Getting started with PCF is easy. Your developers already know how to develop their spring boot / nodejs / php / ruby / .NET application. They already know what its artifacts are. They probably already have some jenkins pipeline in place. They just want to run the same thing in a cloud environment.

    If we’ll take a look at PCF’s “Getting Started with Pivotal Cloud Foundry”, it’s almost comical how little is required to get something up and running. When you need more complex interaction, it’s all available for you, either in the cf cli, as part of a manifest.yml, or in the web console, but this doesn’t prevent you from getting started quickly.

    If you mainly develop server-based applications in java or nodejs, PCF gets you to the cloud simply, quickly and more elegantly.

    Vision Kubernetes

    Kubernetes is truly a great open source platform. Kudos to Google for giving up control and letting the community do its thing. That’s probably the number one reason why Kubernetes has taken off so quickly while other solutions like Docker swarm are falling behind. Other vendors also offer solutions that offer a more PaaS-like experience on top of Kubernetes, such as RedHat OpenShift.

    But with such a diverse and thriving eco-system, the path forward can be one of many different directions. It really does feel like a Google product in a way — maybe it will remain supported by Google for years, maybe it will change with barely any backwards compatibility, or maybe they’ll kill it and move to the next big thing (Does anyone remember Google Buzz, Google Wave or Google Reader?). Any AngularJS developer who’s trying to move to Angular 5 can tell you that backwards compatiblity is not a top priority.

    Cloud Foundry

    Cloud Foundry is also a thriving open source platform, but it is pretty clear who sets the tone here. It is Pivotal, with additional contributions from IBM. Yes, it’s open source, but the enterprise play here is Pivotal Cloud Foundry, which provides added value like the services marketplace, Ops Manager etc. And on that side, it’s a limited democracy. This is a product that is meant to serve enterprise customers, and the feature set would first and foremost answer those needs.

    If Kubernetes is Google, then PCF is Apple.

    A little more of a walled garden, more controlled, better design/experience layer, and a commitment for delivering a great product. I feel like the platform is more focused, and focus is critical in my line of work.

    PKS

    The real surprise of the recently announced PCF 2.0 was that all I’ve been talking about throughout this article is now just one part of a larger offering. The aplication runtime (everything that is referred to as PCF in this article) is now called Pivotal Application Service (PAS). There is also a new serverless solution called Pivotal Function Service (PFS), and lastly — a new Kubernetes runtime called Pivotal Container Service (PKS). This means that Pivotal Cloud Foundry now gives you the best of both worlds: A great application runtime for fast onboarding of cloud-native applications, as well as a great container runtime when you need to develop generic low-level containers.

    Conclusion

    In this article I tried to share my personal experiences of working with both platforms. Although I am a bit biased towards PCF, it is for a good reason — it has served me well. I approached Kubernetes with an open mind, and found it to be a very versatile platform, but also one that requires a steeper learning curve. Maybe I got spoiled by living in the Spring eco-system for too long :). With the latest announcement of PKS, it appears that Pivotal Cloud Foundry is set to offer the best integrated PaaS — one that lets you run cloud native applications as quickly and simply as possible, while also exposing the best generic container runtime when that is needed. I can see this becoming very useful in many scenarios. For example, Apache Kafka is one of the best message brokers available today, but this message broker still doesn’t have a PCF service available, so it has to run externally on virtual machines. Now with PCF 2.0, I can run Apache Kafka in docker containers inside PCF itself.

    The main conclusion is that this is definetly not a this or that discussion. Since both the application runtime and the container runtime now live side by side in the same product, the future seems promising for both.

    Thank you for reading, and happy coding!

    Oded Shopen

    Visit my homepage: http://odedia.org

    Follow me on twitter: https://twitter.com/odedia

    Follow me on LinkedIn: https://www.linkedin.com/in/odedia/

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    IBM C5050-384 Exam (IBM Cloud Platform Application Development v2) Detailed Information



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