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Vendor designation : IBM
: 30 true Questions

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There comes a degree where respectable concepts don't seem to subsist enough; potential, proven solutions should prefer flight.

here's the position the position cloud computing finds itself nowadays, specially when the conversation turns towards immoderate performance computing (HPC).

The mammoth majority of companies these days are leveraging the agility, impregnate merits, and console provided by using the public cloud capabilities mannequin. HPC amenities are additionally exploring the advantages of cloud, however most likely now not for the selfsame motives. Whereas greater ordinary businesses are most regularly seeking to public cloud services providers for backup and catastrophe recovery solutions or the alternative to lessen storage expenses through parking seldom-accessed data sets on less expensive cloud storage, HPC amenities usually watch to subsist exploring the possibilities of accommodating spikes renowned with the aid of dynamically including and subtracting cloud-based mostly materials.

These everything sound enjoy very decent ideas, but making them work, effectively and reliably, has been a true problem. Cloud compute materials, unlike cloud storage, may moreover subsist extra costly than on-premises infrastructure. safety is everything the time a consideration. relocating mammoth HPC information units over networks may moreover subsist prohibitively sluggish and cumbersome. and moreover you deserve to maintain should maintain functions that may function acceptably on cloud-based mostly elements.

When off-site cloud materials are integrated with on-premises computing, the influence is referred to as a hybrid cloud structure. There is no one correct technique to construct an HPC hybrid cloud computing cluster. each website is different, and clients rush sunder workloads which maintain very distinctive aid consumption patterns. for instance, some profound gaining scholarship of practicing consumes huge quantities of information to provide a tiny model. Others are exceedingly CPU intensive, or GPU intensive. The hybrid cluster retort really carried out will of direction subsist driven by means of the workloads HPC users want to run, but two hybrid architectures regularly mount to the conducive when IT architects ply the challenges of constructing cloud waft for HPC – the “stretch cluster” configuration and the “multi-cluster” configuration. To accommodate spikes favourite, organizations operating typical HPC environments are increasingly hybrid clouds to give the further potential when it’s truly obligatory.  Stretch and multi-cluster hybrid cloud solutions can assist ply this challenge by using enabling dynamic entry to cloud resources.

A stretch cluster capacity that you just extend or augment your on-premises HPC compute cluster with further cloud-based compute nodes, when obligatory. This structure assumes that you've a compute cluster in another location – both on-premises or configured in a cloud. This structure is formally defined as a unique cluster stretched over a wide district network (WAN) in order that compute nodes on the other region or within the cloud talk with a grasp scheduling host on the originating region. even though lots more straightforward in feeling than a multi-cluster, this capability that everything verbal exchange and coordination with the grasp scheduler happens over the WAN, which can subsist a supply of added can impregnate or reduced reliability.

A multi-cluster hybrid cloud configuration is a more involved structure, since it adds a grasp scheduler running within the cloud. through together with this additional grasp scheduler within the cloud, the architecture eliminates lots of the communique from cloud compute nodes to the on-premises master. the two grasp schedulers as a substitute exchange project meta-data in a “job forwarding” model. in this model, users on-premises post utility workloads to a queue on-premises, which in whirl forwards that workload to the cloud for execution. Upon job completion, the scheduler running within the cloud synchronizes the job popularity with the on-premises environment and a notification is provided to the person.

The question turns into, what are the deployment, configuration, and optimization particulars for HPC stretch and multi-cluster hybrid cloud solutions? How conclude I in fact originate this thing fly? A fresh announcement from IBM provides the solutions. In a crucial step along the pathway to doable, legit, effortless-to-use hybrid cloud options, IBM has developed and released a deployment ebook that helps using IBM Spectrum LSF with Amazon net capabilities (AWS). This LSF cloud deployment e-book builds on an existing relationship with AWS. IBM is presenting expertise, services, and management capabilities on the route to provide IBM Spectrum LSF users quickly, bendy entry to AWS choices.

the modern deployment e-book gives suggestions for structure a wide range of customizable stretch and multi-cloud IBM Spectrum LSF cluster configurations that allow clients to more comfortably and without problems capitalize the agility, impregnate merits, and console provided by route of cloud computing.

IBM Spectrum LSF is a Part of a comprehensive Suite of solutions aiding natural HPC and immoderate throughput environments, as well as massive statistics, synthetic Intelligence, GPU, desktop gaining scholarship of, and containerized workloads, amongst many others. IBM Spectrum LSF, the core of the Suite, is an impressive workload administration platform for traumatic, dispensed HPC environments. It provides an entire set of intelligent, policy-pushed scheduling aspects that champion maximize utilization of compute infrastructure components while optimizing utility performance. LSF is the HPC workload management regular, with probably the most complete set of capabilities – from license scheduling and session scheduling to advanced analytics.

IBM Spectrum LSF Suite comes in three editions and contains a number of extra add-ons comparable to IBM Spectrum LSF useful resource Connector, which makes it workable for policy-pushed cloud bursting to everything essential cloud functions, including IBM Cloud, AWS, Google, and Azure. And now, IBM is providing variable expend licensing for IBM Spectrum LSF which faculty that LSF users can subsist in a position to rush the solution nearly anywhere, and pay simplest for what they use.

The cloud offers many merits, however constructing valuable hybrid cloud solutions isn’t so simple – before. With the leading-side functionality offered through IBM Spectrum LSF and the aid of the brand modern LSF deployment pilot for AWS, HPC amenities are a entire lot greater ready to originate cloud waft for them.

learn about main-area HPC and AI options at the IBM booth next week at SC18 (sales space #3433) in Dallas, Texas. Register here for technical briefings and consumer neighborhood sessions.

study greater about dynamic hybrid cloud with IBM Spectrum LSF on the IBM IT Infrastructure weblog – IBM Spectrum LSF Goes Multicloud.

a more in-depth materialize at the deployment e-book and helping materials for structure IBM Spectrum LSF hybrid-cloud configurations with Amazon internet services will moreover subsist discovered here.


P2020-007 IBM Optimization Technical Mastery Test v1

Study pilot Prepared by Killexams.com IBM Dumps Experts


Killexams.com P2020-007 Dumps and true Questions

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P2020-007 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : P2020-007
Test designation : IBM Optimization Technical Mastery Test v1
Vendor designation : IBM
: 30 true Questions

Nice to hear that Latest dumps of P2020-007 exam are available.
A few tremendous advice is that I exceeded P2020-007 check the day past... I thank entire killexams.Com institution. I certainly respect the unbelievable paintings that you everything do... Your schooling cloth is notable. Maintain doing arrogate paintings. I will actually expend your product for my next exam. Regards, Emma from the great apple


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Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 2 | killexams.com true questions and Pass4sure dumps

Ricardo Balduino and Tim Bohn

Early Flight, Creative Commons Introduction

As they described in Part 1 of this series, their objective is to attend forecast the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They expend historical flights data and historical weather data to originate predictions for upcoming flights.

Over the course of this four-part series, they expend different platforms to attend us with those predictions. Here in Part 2, they expend the IBM SPSS Modeler and APIs from The Weather Company.

Tools used in this expend case solution

IBM SPSS Modeler is designed to attend learn patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a range of advanced algorithms and analysis techniques, including text analytics, entity analytics, conclusion management and optimization to deliver insights in near real-time. For this expend case, they used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.

We moreover used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.

A proposed solution

In this blog, they propose one workable solution for this problem. It’s not meant to subsist the only or the best workable solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. They hearten the readers to try and attain up with different solutions, and provide us with your feedback for future blogs.

Business and data understanding

The first step of the iterative process includes understanding and gathering the data needed to train and test their model later.

Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so they ended up with 12 csv (comma separated value) files. They used IBM SPSS Modeler to merge everything the csv files into one set and to select the ten airports in their scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in design 1.

Figure 1 — gathering and preparing flights data in IBM SPSS Modeler

Weather data — From the Extensions Hub, they added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in design 2.

Figure 2 — gathering and preparing weather data in IBM SPSS Modeler

Combined flights and weather data — To each flight in the first data set, they added two modern columns: root and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in design 3 are placeholders for the diagrams in Figures 1 and 2 above.

Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation

We iteratively performed the following steps until the desired model qualities were reached:

· Prepare data

· fulfill modeling

· Evaluate the model

· Repeat

Figure 4 shows the first and second iterations of their process in IBM SPSS Modeler.

Figure 4 — iterations: prepare data, rush models, evaluate — and conclude it again First iteration

To start preparing the data, they used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took custody of null values). In order to better train the model later on, they filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)

Figure 5 — imbalanced data establish in their input data set

This is an gripping expend case, and often a hard one to solve, due to the imbalanced data it presents, as seen in design 5. By “imbalanced” they strike that there were far more non-cancelled flights in the historical data than cancelled ones. They will contend how they dealt with imbalanced data in the following iteration.

Next, they defined which features were required as inputs to the model (such as flight date, hour, day of the week, root and destination airport codes, and weather conditions), and which one was the target to subsist generated by the model (i.e. forecast the cancellation status). They then partitioned the data into training and testing sets, using an 85/15 ratio.

The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to rush multiple models at once and preview their outputs, such as the district under the ROC curve, as seen in design 6.

Figure 6 — models output provided by the Auto Classifier node

That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. They decided to expend the Random Trees model since the initial analysis showed it has the best district under the curve as compared to the other models in the list.

Second iteration

During the second iteration, they addressed the skewedness of the original data. For that purpose, they chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped their selected model drudgery more effectively.

Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE

In design 7, they notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

As mentioned earlier, they picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less prostrate to overfitting, which gives a higher likelihood of repeating the selfsame test results when you expend modern data, that is, data that was not Part of the original training and testing data sets. Another odds of this method — in particular for their expend case — is its faculty to ply imbalanced data.

Since in this expend case they are dealing with classification analysis, they used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in design 8. The table shows the precision achieved by the model during training.

Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights

In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was correct most of the time, but moreover made wrong predictions about 4–5% of the time (false negatives and counterfeit positives).

That was the precision given by the model using the training data set. This is moreover represented by the ROC curve on the left side of design 9. They can see, however, that the district under the curve for the training data set was better than the district under the curve for the testing data set (right side of design 9), which means that during testing, the model did not fulfill as well as during training (i.e. it presented a higher rate of errors, or higher rate of counterfeit negatives and counterfeit positives).

Figure 9 — ROC curves for the training and testing data sets

Nevertheless, they decided that the results were soundless conducive for the purposes of their discussion in this blog, and they stopped their iterations here. They hearten readers to further refine this model or even to expend other models that could decipher this expend case.

Deploying the model

Finally, they deployed the model as a relaxation API that developers can call from their applications. For that, they created a “deployment branch” in the SPSS stream. Then, they used the IBM Watson Machine Learning service available on IBM Bluemix here. They imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can moreover call The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and originate the prediction.

A typical scoring endpoint provided by the Watson Machine Learning service would glance enjoy the URL shown below.

https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>

By passing the expected JSON cadaver that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to subsist cancelled or not. This is seen in design 10, which shows a call being made to the scoring endpoint — and its response — using an HTTP requester tool available in a web browser.

Figure 10 — actual request URL, JSON body, and response from scoring endpoint

Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would subsist 88.8% likely to subsist cancelled, based on forecast weather conditions.

Conclusion

IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this expend case without writing a unique line of code. They were able to ensue an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.

Resources

The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can moreover find instructions on how to download IBM SPSS Modeler, glean a key for The Weather Channel APIs, and much more.


The Week In Review: Design | killexams.com true questions and Pass4sure dumps

M&AIoT-focused memory chipmaker Adesto Technologies acquired S3 Semiconductors, a provider of mixed-signal and RF ASICs and IP. Based in Ireland, S3 Semiconductors was founded in 1986. S3 Semiconductors will become a trade unit of Adesto and will continue to operate under its current model in the $35 million deal. S3 Semiconductor’s parent company, S3 Group, will continue as a sunder company focused on smart medical devices.

ToolsCadence debuted a modern design-for-reliability tool for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the faculty to purge die with manufacturing defects and resulting test escapes that antecedent province failures, as well as electro-thermal and aging analysis to avert thermal overstress and forecast wear-out.

Aldec made several updates to its Riviera-PRO verification platform, adding a UVM Register Generator for automated register model creation. The UVM Register Generator supports input of IP-XACT component files and CSV files. Other additions comprehend a Unit Linting function that runs ALINT-PRO in the background and champion for the latest extensions to VHDL.

XJTAG uncorked free DFT software for users of Mentor’s Xpedition product to extend the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT aide helps validate correct JTAG chain connectivity by finding and repairing errors.

IPArasan Chip Systems uncorked its MIPI I3C Host Controller Interface (I3C HCI) Master IP core compliant to the recently released MIPI I3C HCI Specification 1.0. The IP kit includes a Linux software stack compatible with the I3C HCI Master IP Core, an I3C Hardware progress Kit to enable prototyping and compliance testing of I3C products, and the I3C Slave IP Core.

Chips&Media launched its first computer vision IP, which implements a convolutional neural network capable of processing 4K resolution at 60 frames per second input in real-time for expostulate detection. The IP targets edge devices for automotive and surveillance applications.

Chipus taped out a set of its ultra-low power analog IP on SilTerra’s 0.18um I18L IoT Platform. The test chip includes a temperature sensor, RTC oscillators, 6MHz oscillators, and an ultra-low power bandgap reference.

StandardsThe Si2 Compact Model Coalition published the Open Model Interface, a C-language application programming interface that supports SPICE compact model extensions. OMI allows for simulation and analysis of physical effects such as self-heating and aging as well as design optimization. It is based on TMI2, the TSMC Model Interface, which was donated to Si2 by TSMC in 2014. So far, OMI supports BSIM, BSIM-CMG, HiSIM2, and BSIM-SOI.

The MIPI Alliance released MIPI Touch, a family of publicly available specifications for the design and implementation of handle applications, such as in smartphones. The application ties together several MIPI specifications for touch, sensor, and software integration: MIPI handle Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI handle Adaptation Layer for I3C (MIPI ALI3C) to translate handle commands for expend on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables handle commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a handle sensor.

DealsRambus and GigaDevice formed a joint venture, Reliance Memory, in China to focus on progress and commercialization of Resistive Ram (RRAM), a non-volatile memory that works by changing the resistance of materials when different voltages are applied. The joint venture has investment funding from THG Ventures, West peak Capital, Walden International, and Zhisland Capital.

Esperanto Technologies inked deals for both NetSpeed Systems’ network-on-chip IP and design environment and UltraSoC’s embedded analytics and debug IP for its Supercomputer-on-a-Chip for AI applications. The 7nm chip will integrate thousands of RISC-V compatible processors each with vector/tensor accelerators.

NetSpeed Systems and Northwest Logic are teaming up on a elevated throughput memory subsystem solution that uses NetSpeed’s interconnect IP and Northwest Logic’s HBM2 and GDDR6 memory controllers for customers designing SoCs for hyperscale and automotive applications. The companies will provide a common platform to extend NetSpeed’s dynamic-priority altitude approach to the memory controller so that system-level traffic-flow priorities are handled in an end-to-end solution.

NumbersIntel Capital placed investments in two firms: Movellus and SiFive. Movellus provides generator tools enabling automatic creation and implementation of PLL, DLL, and LDO digital IP and will expend the funds to accelerate market deployment and adoption of its technology. SiFive is a provider of RISC-V IP cores, SoCs, and progress boards, as well a RISC-V-focused IP catalog.

Rambus released financial results for first quarter 2018 with revenue of $46.4 million. On a GAAP basis, there was a loss per partake of $0.36; on a non-GAAP basis there was a loss per partake of $0.10. Rambus changed to a modern reporting gauge this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the selfsame quarter ultimate year. GAAP income per partake for the quarter was $0.05, up 66.7% from $0.03 in Q1 2017; non-GAAP income per partake was $0.21, up 23.5% from $0.17. The company posted record revenue for its memory and interface IP cores.

Jesse Allen   (all posts)Jesse Allen is the scholarship center administrator and a senior editor at Semiconductor Engineering.

Cisco refused to participate in NSS Labs report on SD-WAN | killexams.com true questions and Pass4sure dumps

Cisco refused to activate the Viptela software-defined WAN product NSS Labs bought for testing, leaving the research...

firm with a noticeable hole in its recent comparative report on SD-WAN vendors.

Cisco did not provide a judgement for refusing to activate the product NSS Labs had purchased for between $30,000 and $40,000, NSS Labs CEO Vikram Phatak said this week. "There was no judgement given other than, effectively, they didn't want to subsist tested (for the NSS Labs report)."

Cisco's action marked the first time a vendor had refused to whirl on a product NSS Labs had bought for evaluation, Phatak said. Cisco's Viptela team had initially told NSS Labs it would champion the test, which led the firm to buy the product.

"That's a first for us, candidly," Phatak said. "And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock because normally, they're pretty straightforward to drudgery with."

Cisco's response

After initially refusing to contend the matter, Cisco released a statement that said NSS Labs's proposed testing methodology "fell well short" of reflecting "the enterprise-grade nature of customer SD-WAN requirements."

"We shared true customer deployment requirements with them, but their feedback wasn't incorporated," Cisco said. "At that point, they decided not to participate in the test."

Not participating, however, usually doesn't strike the vendor will avert the test from going forward. Typically, when a company refuses to link a test, NSS Labs will conduct it alone.

"If someone says they don't want to subsist tested, they say, 'That's great, but if a product is conducive enough to subsist sold to the public, it's conducive enough to subsist tested,'" Phatak said. "We're going to buy it, and we'll report to the public."

NSS Labs wants a refund

NSS Labs wants Cisco to refund the money spent on Viptela. It is hoping it can glean the money back without going to court.

"I hope it doesn't attain to that," Phatak said. "We haven't talked to any lawyers. I'm assuming that we'll subsist able to maintain the conversation and glean their money back."

Typically, NSS Labs buys products, and the vendors whirl them on enjoy they would for any other customer.

That's a first for us, candidly. And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock. Vikram PhatakCEO, NSS Labs

NSS Labs renowned Cisco's refusal to activate the Viptela purchase in its SD-WAN Comparative Report, which was the company's first SD-WAN test. Not having Cisco in the evaluation left out one of the largest SD-WAN vendors and a major tech company.

In the first quarter, London-based IHS Markit listed Cisco as No. 4 in the SD-WAN market, just behind Silver Peak. VMware was first with a 19% share, followed by Aryaka with 18%.

The NSS Labs report, released this month, compared the products of nine vendors, including VMware's NSX SD-WAN, formerly VeloCloud. VMware is Cisco's largest competitor.

NSS Labs had moreover planned to comprehend Silver Peak in the comparison but renowned it was unable to obtain the product in time for testing.

Tech companies often cite recommended ratings in NSS Labs reports in marketing materials. In April, Cisco highlighted in a blog post the organization's "recommended" rating for the Cisco Advanced Malware Protection for Endpoints product.

Based on its recent SD-WAN tests, NSS Labs recommended products from VMware, Talari Networks and Fortinet and listed products from Citrix Systems, FatPipe Networks, Forcepoint and Versa Networks as "verified." Tech buyers should admiration recommended and verified products as candidates for purchase, according to NSS Labs.

The company issued "caution" ratings for Barracuda Networks and Cradlepoint, which means companies should not deploy their products without a comprehensive evaluation, NSS Labs said.



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MORGAN Studio

is specialized in Architectural visualization , Industrial visualization , 3D Modeling ,3D Animation , Entertainment and Visual Effects .