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IBM IBM Optimization Technical Mastery

IBM: a long Work-In-growth | killexams.com true Questions and Pass4sure dumps

No result found, are trying fresh key phrase!then again, searching from the technical charting perspective ... the SVP and CFO of IBM, pointed out within the income appellation that the company has been in the hunt for to enrich its "personnel optimization productiveness ...

IBM’s plot to convey machine learning Capabilities to statistics Scientists in every unique region | killexams.com true Questions and Pass4sure dumps

Hillary Hunter is an IBM Fellow.

Over at the IBM blog, IBM Fellow Hillary Hunter writes that the enterprise anticipates that the area’s volume of digital information will exceed 44 zettabytes, an awesome quantity. As firms start to realize the colossal, untapped skills of statistics, they necessity to locate a means to exploit it. Enter AI.

IBM has labored to construct the business’s most comprehensive facts science platform. built-in with NVIDIA GPUs and utility designed specially for AI and probably the most statistics-intensive workloads, IBM has infused AI into choices that consumers can entry even with their deployment mannequin. these days, they capture the next step in that journey in announcing the next evolution of their collaboration with NVIDIA. They plot to leverage their fresh information science toolkit, RAPIDS, throughout their portfolio so that their consumers can enhance the efficiency of computer studying and facts analytics.

Plans to advertise GPU-accelerated computing device learning encompass:

  • IBM POWER9 with PowerAI: to leverage RAPIDS to extend the options available to statistics scientists with fresh open supply desktop getting to know and analytics libraries. Accelerated workloads were confirmed to rate an instantaneous improvement from the exclusive engineering that NVIDIA and IBM gain achieved around POWER9, including integration of NVIDIA NVLink and NVIDIA Tesla GPUs. PowerAI is IBM’s software layer, which optimizes how these days’s statistics science and AI workloads sprint on these heterogeneous computing methods. Their purpose is for this improved performance trajectory for GPU-accelerated workloads on POWER9 to continue with RAPIDS.
  • IBM Watson Studio and IBM Watson desktop learning: to capture handicap of the power of NVIDIA GPUs so that facts scientists and AI developers can build, set up, and sprint quicker fashions than CPU-handiest deployments of their AI applications in a multicloud environment with IBM Cloud inner most for facts and IBM Cloud.
  • IBM Cloud: to users who elect machines equipped with GPUs with a view to drill accelerated computing device learning and analytics libraries in RAPIDS to their cloud purposes and tap into the merits of machine gaining knowledge of.
  • IBM and NVIDIA’s shut collaboration over the years has helped leading companies and corporations complete over tackle one of the crucial world’s biggest issues,” said Ian Buck, vice chairman and prevalent manager of Accelerated Computing at NVIDIA. “Now, with IBM taking abilities of RAPIDS open-supply libraries announced today via NVIDIA, GPU accelerated machine gaining knowledge of is coming to facts scientists, helping them anatomize massive records for insights faster than ever workable before. Recognizing the computing energy that AI would need, IBM changed into an early recommend of statistics-centric methods. This approach led us to deliver the GPU-fitted apex system, the realm’s most powerful supercomputer, and already researchers are seeing gigantic returns. previous within the year, they demonstrated the handicap for GPUs to accelerate up desktop discovering after they confirmed how GPU-accelerated desktop getting to know on IBM energy programs AC922 servers set a new accelerate checklist with a 46x progress over ancient effects.

    because of IBM’s dedication to bringing accelerated AI to clients throughout the technology spectrum, breathe they users of on-premises, public cloud, deepest cloud, or hybrid cloud environments, the enterprise is located to bring RAPIDS to users regardless of how they want to access them.

    Hillery Hunter is an IBM Fellow and CTO of Infrastructure in the IBM Hybrid Cloud business. just before this role, she served as Director of Accelerated Cognitive Infrastructure in IBM analysis, leading a crew doing go-stack (hardware via application) optimization of AI workloads, producing productiveness breakthroughs of 40x and more advantageous which gain been transferred into IBM product choices. Her technical pursuits gain always been interdisciplinary, spanning from silicon expertise through gadget utility, and she or he has served in technical and leadership roles in reminiscence technology, systems for AI, and different areas. She is a member of the IBM Academy of expertise.

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    E-commerce startup Fluid uses IBM’s Watson. Now IBM invests in Fluid | killexams.com true Questions and Pass4sure dumps

    Use IBM’s Watson know-how in an flattering manner and you’ll breathe rewarded.

    That’s definitely the manner issues look today when distinguished Blue announced it had made an investment in Fluid, an e-commerce utility commerce that has built-in Watson — which can study in line with a variety of ancient information and then answer questions about the waft — into its virtual browsing assistant application.

    IBM didn’t negate how a suited deal funds it has poured into Fluid, but they attain know simply how dedicated IBM is to backing corporations that spend its Watson expertise. prior this 12 months it announced $one hundred million in challenge funding would disappear to corporations operating “a fresh category of cognitive apps powered by using Watson.” outdated Watson-oriented investments from IBM encompass health-optimization enterprise Welltok.

    IBM spoke of in January that Welltok and Fluid would disappear to market this yr with Watson-enabled features, and now each of them gain rate a hold of funding from IBM. MD Buyline, a corporation with software for monitoring medical device, is additionally slated to hit the market quickly, IBM stated on the time. most likely when that happens, IBM will hurl some funding at MD Buyline, too.

    All these strikes could assist IBM develop into a platform for processing lots of diverse styles of information via a cloud. They may aid bring massive Blue closer to strategic dreams around running agencies from clouds in region of undifferentiated servers for facts facilities.

    The Watson investing also associates IBM with hip startups instead of slow-relocating enterprises. IBM’s SoftLayer and Cloudant acquisitions gain introduced game builders and other early-stage IT consumers to IBM. without retard backing corporations fancy Fluid after they’re open to displaying off what Watson can attain could lead on extra individuals to feel about IBM as now not only a mainframe seller but as an organization that enables next-technology experiences.

    Fluid chief govt Kent Deverell gives individuals a tang for such an adventure in a weblog submit today:

    think about buying technical gear, fancy a tent, on-line today. you possibly can visit a retailer or industry web page and ogle for tents by using narrowing down elements fancy dimension, vogue, and a pre-determined list of elements. evaluate that to the in-store journey of enticing with an associate and asserting, “i am taking my family camping in upstate fresh york in October and i necessity a tent. What should quiet I accept as true with?” Fluid’s Watson-enabled cellular app will permit buyers to result this selfsame question on-line in simple English to learn the impeccable tent in addition to surprising gadgets necessary to finished the adventure.

    through their partnership with IBM, they are taking patrons into a brand fresh era of computing. quickly, you’ll not classification the notice ‘backpack’ in a retail web page’s search container and spend hours poring via dozens of options, stories, and greater in finding the yardstick product. in its place, you’ll expect Watson what character of backpack is preempt for a 14-day hiking excursion in Patagonia and immediately acquire personalized, data-pushed responses to efficaciously ebook your choice.


    P2020-007 IBM Optimization Technical Mastery Test v1

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

    Test Code : P2020-007
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    IBM Optimization Technical Mastery Test v1

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

    Ricardo Balduino and Tim Bohn

    Early Flight, Creative Commons Introduction

    As they described in portion 1 of this series, their objective is to serve prognosticate the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They spend historical flights data and historical weather data to result predictions for upcoming flights.

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

    Tools used in this spend case solution

    IBM SPSS Modeler is designed to serve learn patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a ambit of advanced algorithms and analysis techniques, including text analytics, entity analytics, conclusion management and optimization to deliver insights in near real-time. For this spend 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 also 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 breathe 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 inspirit the readers to try and Come 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 complete 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 figure 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 figure 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 fresh columns: inception 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 figure 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

    · accomplish 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, sprint models, evaluate — and attain 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 supervision 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 institute in their input data set

    This is an pitiable spend case, and often a hard one to solve, due to the imbalanced data it presents, as seen in figure 5. By “imbalanced” they signify 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, inception and destination airport codes, and weather conditions), and which one was the target to breathe generated by the model (i.e. prognosticate 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 sprint multiple models at once and preview their outputs, such as the locality under the ROC curve, as seen in figure 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 spend the Random Trees model since the initial analysis showed it has the best locality 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 labor more effectively.

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

    In figure 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 prone to overfitting, which gives a higher likelihood of repeating the selfsame test results when you spend fresh data, that is, data that was not portion of the original training and testing data sets. Another handicap of this method — in particular for their spend case — is its capacity to ply imbalanced data.

    Since in this spend 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 figure 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 redress most of the time, but also made wrong predictions about 4–5% of the time (false negatives and mistaken positives).

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

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

    Nevertheless, they decided that the results were quiet suited for the purposes of their discussion in this blog, and they stopped their iterations here. They inspirit readers to further refine this model or even to spend other models that could resolve this spend case.

    Deploying the model

    Finally, they deployed the model as a repose API that developers can summon 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 also summon 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 result the prediction.

    A typical scoring endpoint provided by the Watson Machine Learning service would ogle fancy 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 breathe cancelled or not. This is seen in figure 10, which shows a summon 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 breathe 88.8% likely to breathe cancelled, based on forecast weather conditions.

    Conclusion

    IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this spend case without writing a unique line of code. They were able to succeed 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 also find instructions on how to download IBM SPSS Modeler, rate 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 commerce 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 separate company focused on smart medical devices.

    ToolsCadence debuted a fresh design-for-reliability tool for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the capacity to eradicate die with manufacturing defects and resulting test escapes that cause field failures, as well as electro-thermal and aging analysis to preclude thermal overstress and prognosticate 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 embrace a Unit Linting office 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 increase the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT assistant helps validate redress 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 kick 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 touch applications, such as in smartphones. The effort ties together several MIPI specifications for touch, sensor, and software integration: MIPI touch Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI touch Adaptation Layer for I3C (MIPI ALI3C) to translate touch commands for spend on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables touch commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a touch 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 apex 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 high 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 elevation 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 spend 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 fiscal results for first quarter 2018 with revenue of $46.4 million. On a GAAP basis, there was a loss per participate of $0.36; on a non-GAAP basis there was a loss per participate of $0.10. Rambus changed to a fresh reporting yardstick 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 participate for the quarter was $0.05, up 66.7% from $0.03 in Q1 2017; non-GAAP income per participate 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 knowledge center administrator and a senior editor at Semiconductor Engineering.

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    Google+ : https://plus.google.com/112153555852933435691/posts/8NTkTVfugU3?hl=en
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    zoho.com : https://docs.zoho.com/file/5bym243385cf5942341d9b284b06dad1b55ec






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