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P2020-007 - IBM Optimization Technical Mastery Test v1 - BrainDump Information

Vendor Name : IBM
Exam Code : P2020-007
Exam Name : IBM Optimization Technical Mastery Test v1
Questions and Answers : 30 Q & A
Updated On : January 18, 2019
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P2020-007 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : P2020-007
Test Name : IBM Optimization Technical Mastery Test v1
Vendor Name : IBM
Q&A : 30 Real Questions

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IBM IBM Optimization Technical Mastery

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

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

IBM’s Plan to convey machine learning Capabilities to statistics Scientists in every single place | killexams.com Real 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 need 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, we take the next step in that journey in announcing the next evolution of our collaboration with NVIDIA. We plan to leverage their new information science toolkit, RAPIDS, throughout our portfolio so that our 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 new open supply desktop getting to know and analytics libraries. Accelerated workloads were confirmed to get an instantaneous improvement from the exclusive engineering that NVIDIA and IBM have 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 run on these heterogeneous computing methods. Our 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 take advantage of the power of NVIDIA GPUs so that facts scientists and AI developers can build, set up, and run 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 choose machines equipped with GPUs with a view to practice 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 all over tackle one of the crucial world’s biggest issues,” said Ian Buck, vice chairman and normal 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 analyze massive records for insights faster than ever possible 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 Summit system, the realm’s most powerful supercomputer, and already researchers are seeing gigantic returns. previous within the year, we demonstrated the advantage for GPUs to speed up desktop discovering after we confirmed how GPU-accelerated desktop getting to know on IBM energy programs AC922 servers set a new speed checklist with a 46x development over old effects.

    because of IBM’s dedication to bringing accelerated AI to clients throughout the technology spectrum, be 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 have been transferred into IBM product choices. Her technical pursuits have 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 Real Questions and Pass4sure dumps

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

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

    IBM didn’t say how a good deal funds it has poured into Fluid, but we do know simply how dedicated IBM is to backing corporations that use its Watson expertise. prior this 12 months it announced $one hundred million in challenge funding would go to corporations operating “a new 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 go to market this yr with Watson-enabled features, and now each of them have get 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 throw 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 place 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 have introduced game builders and other early-stage IT consumers to IBM. without delay backing corporations like Fluid after they’re open to displaying off what Watson can do 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 taste for such an adventure in a weblog submit today:

    think about buying technical gear, like a tent, on-line today. you possibly can visit a retailer or industry web page and look for tents by using narrowing down elements like 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 new york in October and i need a tent. What should still I accept as true with?” Fluid’s Watson-enabled cellular app will permit buyers to make this same question on-line in simple English to discover the perfect tent in addition to surprising gadgets necessary to finished the adventure.

    through our partnership with IBM, we are taking patrons into a brand new 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 ideal product. in its place, you’ll ask Watson what type of backpack is appropriate for a 14-day hiking excursion in Patagonia and immediately acquire personalized, data-pushed responses to efficaciously ebook your choice.




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

    Test Code : P2020-007
    Test Name : IBM Optimization Technical Mastery Test v1
    Vendor Name : IBM
    Q&A : 30 Real Questions

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

    Ricardo Balduino and Tim Bohn

    Early Flight, Creative Commons Introduction

    As we described in Part 1 of this series, our objective is to help predict the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. We use historical flights data and historical weather data to make predictions for upcoming flights.

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

    Tools used in this use case solution

    IBM SPSS Modeler is designed to help discover 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, decision management and optimization to deliver insights in near real-time. For this use case, we 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, we propose one possible solution for this problem. It’s not meant to be the only or the best possible 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. We encourage 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 our 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 we ended up with 12 csv (comma separated value) files. We used IBM SPSS Modeler to merge all the csv files into one set and to select the ten airports in our 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, we 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, we added two new columns: ORIGIN 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

    · Perform modeling

    · Evaluate the model

    · Repeat

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

    Figure 4 — iterations: prepare data, run models, evaluate — and do it again First iteration

    To start preparing the data, we used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took care of null values). In order to better train the model later on, we 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 found in our input data set

    This is an interesting use case, and often a hard one to solve, due to the imbalanced data it presents, as seen in Figure 5. By “imbalanced” we mean that there were far more non-cancelled flights in the historical data than cancelled ones. We will discuss how we dealt with imbalanced data in the following iteration.

    Next, we defined which features were required as inputs to the model (such as flight date, hour, day of the week, origin and destination airport codes, and weather conditions), and which one was the target to be generated by the model (i.e. predict the cancellation status). We 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 run multiple models at once and preview their outputs, such as the area 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. We decided to use the Random Trees model since the initial analysis showed it has the best area under the curve as compared to the other models in the list.

    Second iteration

    During the second iteration, we addressed the skewedness of the original data. For that purpose, we 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 our selected model work more effectively.

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

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

    As mentioned earlier, we 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 same test results when you use new data, that is, data that was not part of the original training and testing data sets. Another advantage of this method — in particular for our use case — is its ability to handle imbalanced data.

    Since in this use case we are dealing with classification analysis, we 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 correct most of the time, but also made wrong predictions about 4–5% of the time (false negatives and false 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. We can see, however, that the area under the curve for the training data set was better than the area under the curve for the testing data set (right side of Figure 9), which means that during testing, the model did not perform as well as during training (i.e. it presented a higher rate of errors, or higher rate of false negatives and false positives).

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

    Nevertheless, we decided that the results were still good for the purposes of our discussion in this blog, and we stopped our iterations here. We encourage readers to further refine this model or even to use other models that could solve this use case.

    Deploying the model

    Finally, we deployed the model as a REST API that developers can call from their applications. For that, we created a “deployment branch” in the SPSS stream. Then, we used the IBM Watson Machine Learning service available on IBM Bluemix here. We imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can also 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 make the prediction.

    A typical scoring endpoint provided by the Watson Machine Learning service would look like 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 body 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 be cancelled or not. This is seen in Figure 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 be 88.8% likely to be cancelled, based on forecast weather conditions.

    Conclusion

    IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this use case without writing a single line of code. We were able to follow 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, get a key for The Weather Channel APIs, and much more.


    The Week In Review: Design | killexams.com real 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 business 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 new design-for-reliability tool for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the ability to eliminate die with manufacturing defects and resulting test escapes that cause field failures, as well as electro-thermal and aging analysis to prevent thermal overstress and predict 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 include a Unit Linting function that runs ALINT-PRO in the background and support 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 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 Development 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 object 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 use 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 development 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 Summit 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 use the funds to accelerate market deployment and adoption of its technology. SiFive is a provider of RISC-V IP cores, SoCs, and development 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 share of $0.36; on a non-GAAP basis there was a loss per share of $0.10. Rambus changed to a new reporting standard this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the same quarter last year. GAAP income per share for the quarter was $0.05, up 66.7% from $0.03 in Q1 2017; non-GAAP income per share 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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