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Google Drops Bid, IBM Protests DoD Cloud compress | killexams.com actual Questions and Pass4sure dumps

Panchenko Vladimir/Shutterstock

The jockeying over a massive U.S. armed forces cloud compress reached a fever pitch this week as one stylish contender dropped out while a different filed a prison protest aimed toward keeping off a sole-supply compress award for cloud features worth billions of bucks.

confronted with an inside riot over the viable utilize of its cloud and AI technologies in battle, Google totality of sudden withdrew this week from competing for the protection department’s large Joint commercial enterprise protection Infrastructure contract. The winner-take-all JEDI software can live expense as tons a $10 billion over 10 years.

one after the other, IBM launched a preemptive strike days earlier than the time confine for submitting bids through becoming a member of Oracle in protesting Pentagon plans to award a sole-supply cloud contract. “JEDI’s primary flaw lies in mandating a separate cloud ambiance for as much as 10 years,” IBM asserted in a blog publish explaining its protest.

“certain necessities within the [DoD request for proposals] both mirror one supplier’s interior procedures or unnecessarily mandate that limpid capabilities live in vicinity by using the bid submission time confine versus when the toil would really begin,” IBM claimed. Amazon net capabilities (NASDAQ: AMZN), which provides cloud features to U.S. intelligence companies, is extensively considered the front-runner for the JEDI contract.

The IBM (NYSE: IBM) protest filed this week with the U.S. govt Accountability office is fragment of an ongoing criminal exertion by cloud carriers to radically change JEDI into a multi-dealer compress award. Oracle (NYSE: ORCL) correctly challenged a $950 million DoD cloud compress awarded ultimate yr to AWS companion REAN Cloud.

while Google also lacked the federal certifications necessary to maneuver classified DoD statistics, common opposition to defense compel toil among Google engineers brought on the traffic to drop out of the JEDI competition.

Google (NASDAQ: GOOGL), which had also been lobbying the Pentagon for a multi-supplier approach, tested prior this week it could no longer compete for the DoD cloud contract. The resolution follows a company decision not to resume a compress for a defense compel AI exertion referred to as project Maven designed  to speed up DoD’s integration of massive facts and computing device getting to know into its intelligence operations.

business involvement in the AI project sparked sharp protests within the search big, prompting Google to challenge a group of AI principles precluding utilize of its AI know-how in weapons.

“We aren't bidding on the JEDI compress because first, they couldn’t live assured that it might align with their AI concepts," a Google spokesman referred to in a statement first suggested via Bloomberg. "And second, they decided that there were parts of the compress that were out of scope with their present executive certifications.”

together with AWS, IBM and Oracle, Microsoft (NASDAQ: MSFT) is among the cloud carriers competing for the JEDI contract. Microsoft introduced previous this week that new cloud areas in a position to managing sensitive and secret U.S. records could live obtainable early subsequent yr. That certification is required for coping with categorised information.

ultimate bids for the JEDI compress are due Oct. 12. regardless of the preemptive protests and the prospect of greater after a compress is ultimately awarded, DoD officers advised Congress in may additionally they method to stick to a single-source award to expedite the cloud rollout. among the causes were averting multiple compress awards that might require particular person task orders.

“That pace may evade DoD from impulsively offering new capabilities and better effectiveness to the warfighter that enterprise-stage cloud computing can permit,” John Gibson II, the Pentagon’s chief administration officer, advised lawmakers.

linked

in regards to the creator: George Leopold

George Leopold has written about science and expertise for more than 30 years, specializing in electronics and aerospace expertise. He previously served as government editor of digital Engineering instances. Leopold is the creator of "Calculated chance: The Supersonic life and instances of Gus Grissom" (Purdue tuition Press, 2016).


Apple Highlights Relationship With IBM In enterprise website Makeover | killexams.com actual Questions and Pass4sure dumps

Apple launched a redesigned edition of its Mac in traffic website this week as its worship affair with IBM continued to develop. because the identify implies, the web page showcases Apple’s commercial enterprise acumen and the new design specializes in Apple’s strengths in company.

The balanced site highlights the kinds of things you could possibly expect in an Apple marketing piece fondness the symbiotic relationship between Apple application and hardware and the style Macs, iPhone and iPads play nicely together.

in addition, Apple calls attention to its accessibility equipment, the Swift programming language, which it lately open sourced and OSX protection aspects, among other points that might appeal to enterprise patrons.

Mac in  traffic website

Mac in company web page. Courtesy of Apple.

however also emphasized its relationship with IBM, particularly IBM’s suffer making a gargantuan transition to Macs this 12 months. in response to both the web site and Fletcher Previn, who's vice chairman for office as a provider within the IBM CIO workplace, switching to Macs resulted in an ordinary discount rates of $270 per laptop over a related workstation. Previn referred to the precise dollar rate reductions ranged from $240 to $273.

“We currently occupy 50,000 MacBooks deployed and method to deploy a total of a hundred and fifty,000 to 200,000 devices throughout the firm. considering that the rollout begun in June, 2015 [we have] been including 1900 Macs per week within IBM,” he mentioned.

The discount rates are broadly speaking due to lessen supports fees from fewer calls to IBM’s interior profit desk. only five percent of Mac users occupy referred to as for aid considering the fact that the application began, compared to 40 % for laptop clients, Previn advised TechCrunch.

On ordinary, forty percent of new-hires are requesting Apple machines when they Come on board, however he stories they've considered generational alterations when it involves using Apple items. “somebody who has labored at IBM for 25 years is plenty less prone to examine for a Mac than a new faculty rent, as an instance,” Previn pointed out.

truly, IBM has created a enterprise referred to as ‘Mac at Work’ helping other groups build the transition to Apple items. “IBM is now capable of present clients the identical program [we implemented internally] to without rigor and securely combine Macs with their traffic programs and purposes,” Previn defined.

The IBM worship persevered with a link at the backside of the main web page to a sunder landing page focusing on the IBM-Apple cellular app partnership. This fragment highlights the distinctive industries and jobs covered with the aid of the apps that the two businesses occupy created together.

App from IBM-Apple mobile partnership on iPad, iPhone and Apple Watch.

An app from the IBM-Apple mobile partnership. image courtesy of Apple.

The partnership, which has been ongoing for about 18 months just reached the one hundred app milestone this week. each company brings its exciting strengths to the partnership with Apple bringing its keen design feel and IBM its deep commercial enterprise figuring out. Working together the two agencies have produced a sequence of commercial enterprise apps that engage skills of cell programming ideas and create absolutely new techniques of working.

The purpose of the site is to parade totality of this off with some fairly pictures of delighted Mac users at work.

Apple did not respond to a request for remark on this story.


Apple’s partnership with IBM strikes forward, starting with AppleCare for traffic | killexams.com actual Questions and Pass4sure dumps

  • submit
  • prior this yr, Apple entered into an agreement with IBM targeted at traffic shoppers, and on Wednesday, the partnership showed the primary signals of life when Apple posted a web page on its web site describing its “AppleCare for commercial enterprise” software.

    AppleCare for commercial enterprise feels fondness a pretty splendid security net for businesses deploying [company]Apple[/company] products: apart from round-the-clock telephone assist, every enterprise subscribing to AppleCare for enterprise will glean a “personal liaison” to tackle its account, who can reply to pressing considerations within an hour. organizations can register for onsite hardware service for both two or three years supplied by means of IBM global technology services.

    commercial

    shoppers can additionally glean free replacements for “as much as 10 p.c” of coated iOS gadgets — so if one out of 10 of your employees has fumble fingers and cracks their screen, companies can glean these iPhones and iPads replaced without additional charge.

    There isn’t public pricing purchasable — that’s the variety of factor your IT manager needs to consult with an Apple or [company]IBM[/company] earnings consultant.

    considering the fact that the preparatory announcement, there hasn’t been another public word concerning the partnership, however IBM is working on enterprise-concentrated iOS apps, known as “IBM MobileFirst,” which may quiet live announced earlier than the conclusion of the 12 months.

    screen-shot-2014-11-05-at-8-15-21-pm


    Killexams.com 000-041 Dumps and actual Questions

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    Machine Learning and Reactive Programming: Looking Ahead to Reactive acme | killexams.com actual questions and Pass4sure dumps

    We sat down with Steven Astorino, IBM VP of Development, Hybrid Cloud, z Analytics, and Canada Lab Director, to chat about his toil in machine learning and his upcoming talk at Reactive acme in Montreal.

    So, first of all, could you talk about your background? What initially drew you to machine learning and data science?

    Sure. One aspect of my job really revolves around data science and machine learning. I manage IBM products fondness Data Science Experience, Watson Studio, SPSS decision Optimization, Watson Explorer—that's totality on the ML/data science fragment of the house AI. And then I also occupy DB2 on the Mainframe, for example, some of the most ripen products in the world.

    My background is in computer science. I was a programmer early in my career. Analytics has always been benevolent of immediate to me, in terms of having a actual understanding of things and being able to drive insights out of information and data, whether it's on a personal flat or whether it's enterprises and so forth. I've worked in different industries as well.

    But most recently in this role, actually going back a few years now, they were looking at the next generation of what they should live building. And IBM had a large involvement with BARC and BARC Technology Center, contributing to open source and being able to Do analytics and machine learning. Through there, they really started looking at what tooling could live made available. So I'm not confident if you're aware, but everything that they Do now at IBM is really about design thinking and design first methodology — understanding who the personas are, what their job is, interviewing those individuals, and really trying to create a meet for purpose suffer for them to live able to Do their job much easier.

    At that time, they were going back to what persona they should live looking at. "Data scientist" was one of the key personas they felt needed to live focus on. That's where products fondness Data Science suffer emerged into providing an IDE-type environment for data scientists. It's evolved into much more than that, as they added the IBM Research technology to it, and automated things on the machine learning aspect of operationalizing and productizing models. They can shove into production, monitor, retrain, and so forth.

    That's how this came about. Obviously, there's a actual necessity for this. There are a lot of companies out there, including ourselves, that focus directly on the algorithms. So they Do that as well. They occupy a core set of algorithms that are very key to machine learning and data science, but also around the the tooling and the automation, providing an end-to-end life cycle, being able to inject that into data, cleanly the data, build your models, and then prioritize and Do that iterative process.

    What would you articulate are the biggest challenges around operationalizing machine learning for the "real world?"

    I assume operationalizing machine learning can live a fairly simple task. What complicates thing is, if you perceive at large enterprise companies, leveraging the tools to automate some of the traffic processes they occupy set in place. To integrate this type of technology into those traffic processes is one of the biggest challenges.

    Having the data scienctists empowered to build models, then, is another. Whether it's the machine learning engineer or the IT organization, they can engage these models, toil with the line of business, and then shove them into production. It's more of a culture shift than anything else. In fact, the tools they provide involve the concept of collaboration specifically to enable and automate them so that it's easier to glean into production.

    Another challenge for enterprises is being able to really, truly explain. Let's articulate you're a large bank or insurance company. You've integrated the traffic processes now, and totality of a sudden, you find yourself in a situation where you're getting audited. How Do you intricate what decision a model took, and why? Coming to a lofty flat of circumstantial understanding of why a model made that recommendation, and being able to intricate in an audit situation — that's another really gargantuan one. There are some tools out there, and we're actively working on some ourselves, but it's one that hasn't been resolved yet. Especially at the enterprise level, in a monetary situation, it's going to engage some time to accept some of these latest technologies.

    So we're talking about making things more transparent.

    Yeah, and it's not necessarily just transparency, but having things documented in a simple way. These algorithms are super complex, right? How Do they intricate to a human how a decision was derived, based on maybe gigabytes of data, historical data with different labels, and different variations in the algorithm? That's really where it gets a bit tricky and complicated.

    That sort of ties into a really fascinating conversation happening prerogative now around jaundice in AI. How Do they address the issue of jaundice in a reactive environment?

    As humans, they mind to build more mistakes based on their own biases, and also their own environment around us. Whereas a model with machine learning will not engage some of those variables into account that would give you a apt mathematical respond on whether to sanction or reject an insurance claim, as an example.

    From that standpoint, there are pros and cons. On one hand, you could say, "Look, the algorithm and the instrument will Do a better job than a human would." But, on the other hand, unless an algorithm is trained to detect things a human being would live able to detect, they occupy more sense than the machine would. So I assume this is going to live an ongoing exercise for us to Do better, both ourselves and the technology, to build confident that some of things will Come to fruition.

    Your keynote at Reactive Summit—who should attend that, and why?

    The audience should live anyone who has already had appointment with AI, machine learning, and data sciences, and also the traditional IT individuals who are now AI DevOps or machine learning engineers. Anyone in the IT industry really, who is an IT architect, or who is in suffuse of managing systems and bringing these machine learning models into the production environment, I assume would profit from this.

    If I perceive at a lofty level, machine learning continues to better by automating the tasks that humans would do. We're leveraging machine learning to glean those automated and to build better predictions on the different industries. It will profit us eventually create better services, either for us, for their clients, or for healthcare. There's a lot in totality kinds of industries that we're focusing on, but to me, it's about how we're making better recommendations to resolve bigger problems.

    Cool. Is there anything else I didn't mention that you wanted to cover?

    During the keynote, I will also finger ground on the differences between machine learning, deep learning, data science, and so forth. There's a lot of confusion in the market still, and I assume it's valuable that they totality glean clarity in the industry. Then I'll vanish into some of the deep learning aspects, and the end-to-end lifecycle of machine learning with a reactive trademark as well.

    Okay, thank you. I really esteem your time.

    All right. Thank you.


    : Inside IBM’s data virtualization, data everywhere strategy | killexams.com actual questions and Pass4sure dumps

    Data virtualization has been one of IBM Corp.’s primary concerns since Red Hat Inc.’s OpenShift open-source container application platform gained steam across the industry. Essentially, the combination of OpenShift and IBM’s Cloud Private enterprise-grade private cloud platform resulted in gargantuan Blue’s gargantuan stir to the hybrid cloud.

    In doing so, IBM was able to involve data virtualization through the Cloud Private for Data platform, consequently producing a tenacious relationship between data science and synthetic intelligence, according to Daniel Hernandez (pictured), vice president of IBM Analytics.

    Hernandez spoke with Dave Vellante (@dvellante), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during IBM Change the Game — Winning With AI event in NYC. They discussed how IBM is offering a single-integrated platform through its Cloud Private for Data platform, as well as how that switch to the hybrid cloud has impacted client data and IBM in general. (* Disclosure below.)

    [Editor’s note: The following answers occupy been condensed for clarity.]

    Give us the update on momentum in your business.

    Hernandez: So when they terminal talked they were just introducing something called IBM Cloud Private for Data. The basic notion is anybody that wants to Do data science, data engineering, or building apps with data anywhere, we’re going to give them a separate integrated platform to glean that done. It’s going to live the most efficient, best passage to Do those jobs.

    We had [Hortonworks CEO] Rob Beerden on their program, and he talked a lot about the IBM, Red Hat and Hortonworks relationship. Certainly, they talked about it on their earnings convoke and there seems to live a limpid momentum in the marketplace. But give us your perspective on that announcement. What exactly is it totality about?

    Hernandez: You vanish back to June terminal year; they entered into a relationship with Hortonworks where the basic premise was customers suffuse about data and any data-driven initiative was going to require data science. The other factor of that was we’re going to bring their data science and machine learning tools and runtimes to where the data is, including Hadoop. That’s been a resounding success. The next step up is how Do they proliferate that separate integrated stack everywhere, including private cloud or preferred clouds fondness OpenShift.

    So there were two elements of the announcement. They did the hybrid cloud architecture initiative, which is taking the Hadoop data stack and bringing it to containers and Kubernetes. And the other was we’re going to bring that entire stack onto OpenShift. So, on IBM’s side, with IBM Cloud Private for Data, they are driving certification of that entire stack on OpenShift so any customer that’s betting on OpenShift as their cloud infrastructure can profit from that and the separate integrated data stack.

    Everybody’s talking about containers, Kubernetes and multicloud. Those are the feverish trends. I postulate you’ve seen the identical thing?

    Hernandez: 100 percent. If data is imperative for you, you better rush your data analytic stack wherever you necessity to, and that means multicloud by definition. So you’ve got a choice. You can say, I can port that workload to every sunder programming model and data stack or you can occupy a data stack everywhere, including multiclouds and Open Shift in this case.

    The cloud, whether it’s on-prem or in the public cloud, expands now to the edge; you’ve also got this concept of data virtualization. What’s it totality about?

    Hernandez: Data virtualization has been going on for a long time. Its basic intent is to profit you access data through whatever tools, no matter where the data is.

    Traditional approaches to data virtualization are pretty limiting. So they toil relatively well when you’ve got miniature data sets. But when you’ve got highly fragmented data, which is the case in virtually every enterprise that exists, a lot of the undermined technology for data virtualization breaks down.  

    We’ve been incubating technology under this project codenamed Queryplex. It was pretty limpid that this is a game-changing method for data virtualization that allows you to drive the benefits of accessing your data wherever it is, pushing down queries where the data is and getting benefits of that through a highly fragmented data landscape. And so what we’ve done is engage that extremely innovated next-generation data virtualization technology, involve it in their data platform, called IBM Cloud Private for Data, and made it a censorious feature inside of that.

    So what’s the secret sauce of Queryplex and data virtualization? How does it totality work? What’s the tech behind it?

    Hernandez: Technically, instead of data coming and getting funneled through one node. If you ever assume of your data as benevolent of a graph of computational data nodes, what Queryplex does is engage advantage of that computational mesh to Do queries and analytics. It distributes out that workload. A low computing aggregate, it’s probably going to live higher than whatever you can do into that separate node.

    And how Do customers access these services? How long does it take? They glean this capability as fragment of what?

    Hernandez: It would perceive fondness a touchstone query interface to them. So this is totality magic behind the scenes — IBM’s Cloud Private for Data. It’s going to live a feature, so this project Queryplex is introduced as next-generation data virtualization technology, which just becomes a fragment of IBM Cloud Private for Data.

    Can they talk about the traffic repercussion of Queryplex and data virtualization?

    Hernandez: Better economics. You don’t occupy to Do ETL in this particular case. So data at relaxation [is] getting consumed because of this online technology. Two, performance. Because of the passage this works, you’re actually going to glean faster response times. [And] three, you’re going to live able to query more data simply because this technology allows you to access totality your data in a fragmented passage without having to consolidate it.

    Now [that] the conversation has moved to AI, your thoughts about where that innovation is coming from and what the potential is for clients?

    Hernandez: You necessity data; you necessity algorithms; you necessity to compute. And bringing those together are exactly the combination that you necessity to implement on any AI system. You already occupy data and computational grids here. You’ve got algorithms bringing them together and solving some problem that matters to a customer; [it] is the natural next step.

    OK, let’s talk about Stack Overflow. Why Stack Overflow; you’re targeting developers? 

    Hernandez: So instead of having a sunder community for AI that’s focused on AI machine developers, why not bring the synthetic intelligence community to where the developers already are, which is Stack Overflow. So, if you vanish to AI.stackexchange.com, it’s going to live the spot for you to vanish to glean totality your answers to any question around synthetic intelligence and, of course, IBM is going to live there in the community helping out.

    Watch the complete video interview below, and live confident to check out more of SiliconANGLE’s and theCUBE’s coverage of IBM Change the Game — Winning With AI event. (* Disclosure: TheCUBE is a paid media colleague for the IBM Change the Game — Winning With AI event. Neither IBM, the event sponsor, nor other sponsors occupy editorial control over content on theCUBE or SiliconANGLE.)

    Photo: SiliconANGLE Since you’re here …

    … We’d fondness to declare you about their mission and how you can profit us fulfill it. SiliconANGLE Media Inc.’s traffic model is based on the intrinsic value of the content, not advertising. Unlike many online publications, they don’t occupy a paywall or rush banner advertising, because they want to hold their journalism open, without influence or the necessity to chase traffic.  

    The journalism, reporting and commentary on SiliconANGLE — along with live, unscripted video from their Silicon Valley studio and globe-trotting video teams at theCUBE — engage a lot of hard work, time and money. Keeping the quality lofty requires the uphold of sponsors who are aligned with their vision of ad-free journalism content.

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    IBM and NVIDIA Collaborate to Expand Open Source Machine Learning Tools for Data Scientists | killexams.com actual questions and Pass4sure dumps

    RAPIDS, new GPU-accelerated libraries to live made available in PowerAI on IBM POWER9, IBM Cloud, and IBM Watson Studio

    ARMONK, N.Y., Oct. 10, 2018 /PRNewswire/ -- IBM (NYSE: IBM) today announced that it plans to incorporate the new RAPIDS™ open source software into its enterprise-grade data science platform for on-premises, hybrid, and multicloud environments. With IBM's vast portfolio of deep learning and machine learning solutions, it is best positioned to bring this open-source technology to data scientists regardless of their preferred deployment model.

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

    More

    "IBM has a long collaboration with NVIDIA that has shown demonstrable performance increases leveraging IBM technology, fondness the IBM POWER9 processor, in combination with NVIDIA GPUs," said Bob Picciano, Senior Vice President of IBM Cognitive Systems. "We perceive to continue to aggressively shove the performance boundaries of AI for their clients as they bring RAPIDS into the IBM portfolio."

    RAPIDS will profit bring GPU acceleration capabilities to IBM offerings that engage advantage of open source machine learning software including Apache Arrow, Pandas and scikit-learn. Immediate, wide ecosystem uphold for RAPIDS comes from key open-source contributors including Anaconda, BlazingDB, Graphistry, NERSC, PyData, INRIA, and Ursa Labs.

    IBM is planning to bring RAPIDS to key areas across on-premises, public, hybrid, and multicloud environments, includingi:

  • PowerAI on IBM POWER9, to leverage RAPIDS to expand the options available to data scientists with new open source machine learning and analytics libraries. Accelerated workloads occupy been proven to glean a direct profit from the special engineering that NVIDIA and IBM occupy done around POWER9, including integration of NVIDIA NVLink® and NVIDIA Tesla® Tensor Core GPUs. PowerAI is IBM's software layer that optimizes how today's data science and AI workloads rush on heterogeneous computing systems, and their goal is for this improved performance trajectory for GPU accelerated workloads on POWER9 to continue with RAPIDS.
  • IBM Watson Studio and Watson Machine Learning, to engage advantage of the power of NVIDIA GPUs so that data scientists and AI developers can build, deploy, and rush faster models than CPU-only deployments for their AI applications in a multicloud environment with IBM Cloud Private for Data and IBM Cloud.
  • IBM Cloud, to users who elect machines equipped with GPUs will live able to apply the accelerated machine learning and analytics libraries in RAPIDS for their cloud applications and tap the benefits of machine learning.
  • "IBM and NVIDIA's immediate collaboration over the years has helped leading enterprises and organizations around the world tackle some of the world's largest problems," said Ian Buck, vice president and common manager of Accelerated Computing at NVIDIA. "Now, with IBM taking advantage of RAPIDS open-source libraries announced today by NVIDIA, GPU accelerated machine learning is coming to data scientists, helping them anatomize gargantuan data for insights faster than ever possible before."

    Machine learning is a form of AI that enables a system to learn from data rather than through specific programming. Enterprises across multiple industries fondness retail, finance, and telecommunications, are either actively using machine learning or exploring machine learning for the potential value it offers to companies trying to leverage gargantuan data to profit them better understand the subtle changes in behavior, preferences, or customer satisfaction.

    Earlier this year, IBM set a record in a tera-scale machine learning benchmark, beating the previous record holder by 46x. Using an IBM Research-developed machine learning algorithm called IBM Snap Machine Learning (Snap ML) running on IBM Power Systems AC922 servers with NVIDIA Tesla V100 Tensor Core GPUs, IBM researchers trained a logistic regression classifier in 91.5 seconds using an online advertising dataset released by Criteo Labs with over 4 billion training examples.

    Media Contact

    Sam PonedalExternal Relations, IBM Power Systems, IBMsponeda@us.ibm.com; (916) 217-0145

    Kristin BrysonNVIDIA PR Directorkbryson@nvidia.com; (203) 241-9190

    i Statements regarding IBM's future direction and intent are matter to change or withdrawal without notice and depict goals and objectives only.

    Certain statements in this press release including, but not limited to, statements as to:  the benefits and repercussion of IBM incorporating RAPIDS into its data science platform; and enterprises across multiple industries actively using or exploring machine learning are forward-looking statements that are matter to risks and uncertainties that could antecedent results to live materially different than expectations. valuable factors that could antecedent actual results to vary materially include: globaleconomic conditions; their reliance on third parties to manufacture, assemble, package and test their products; the repercussion of technological evolution and competition; evolution of new products and technologies or enhancements to their existing product and technologies; marketacceptance of their products or their partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of their products or technologies when integrated into systems;as well as other factors circumstantial from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its form 10-Q for the fiscal period ended July 29, 2018. Copies of reports filed with the SEC are posted on the company's website and are availablefrom NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any duty to update these forward-looking statements to reflect future events or circumstances.

    Story Continues



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    Yang Terkini

    Kategori

    • leaf
    • flower
    • pushpin-2

    Tray Bibit

    Kami menyediakan beragam tray yang dapat digunakan dalam pembibitan mulai dari proses persemaian tahap awal hingga tahap akhir.

    Tray kami telah dipergunakan oleh banyak nursery lokal maupun internasional untuk berbagai komoditas seperti tomat, paprika, sayuran, tebu, kelapa sawit, tembakau, bahkan untuk kebutuhan perhutanan seperti kayu eucalyptus.

    Dengan lebih dari 20 model tray bibit dan akan terus berkembang, kami adalah mitra terbaik untuk tray bibit (pot tray) di Indonesia saat ini.

    Pot Tanaman Hias

    Pot tanaman hias kami tersedia dalam berbagai ukuran dan telah digunakan selama lebih dari 10 tahun oleh pengembang tanaman hias terbesar di Australia hingga saat ini.

    Desain pot kami mengutamakan daya tahan terhadap cuaca yang ekstrim dan sistem aerasi / drainase yang paling efisien namun dengan harga yang sangat kompetitif.

    Aksesoris

    Kami juga menyediakan berbagai aksesoris pendukung operasional nursery yang efisien.

    Berangkat dari kebutuhan nursery modern internasional yang telah menjadi klien kami semenjak tahun 2000 hingga sekarang, kami telah mengembangkan berbagai aksesoris pendukung yang tepat guna dan saat ini kami sediakan bagi pembibit tanah air yang membutuhkan efisiensi tinggi dalam kontrol hasil penanaman.