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How to find an IT Project Manager for a Big Data Project

how to add an invitation for it project manager for giant data project this may be wiped out five easy steps once you're in your personal dashboard select add a replacement request in step 1 you'll be asked to settle on between a adviser project manager or specialist select one that suits you step 2 is for you to supply some information on the role during this case we selected technology and data

 do not forget about the work title to make a more precise search here we will also add a further language aside from english and paste the work description by clicking next we proceed to step 3. this is often the part where we'll be selecting most crucial skills and keywords which will definitely bring you closer to the it project manager you are looking for step 4 is all about time location and price you're required to stipulate data duration for once you want the project to be complete and even have an choice to select price or determine it with a candidate pressing next brings to the last step where we will add key questions either now or later right after selecting any of the 2 our platform will immediately start to load profiles that we discover are matching your search within the next 24 to 48 hours our skill talents research team will send you a brief list of the simplest matching profiles within the meantime you'll check the suggested profiles our ai platform has found you'll review their profiles and invite them to use .

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Boost Machine Learning Model Performance using VOTING ENSEMBLE !

we're gonna be understanding what are voting and symbols and what are their benefits and the way we gonna be using in our machine learning algorithms and what are these limitations voting examples voting ensembles is an assembled machine learning model that mixes the prediction from multiple other models so it's basically it's like we do a a sort of ensemble learning with different quite models running in parallel so it's a way which will be wont to improve the model 

performance ideally achieving better performance than any single model utilized in the assembly so there are literally two quite uh voting ensembles one is for regression and one is for classification so in regression voting and sample we generally take the output from the models and take the typical of these output models and that we do the predictions and in classification vertical symbols we do a majority vote from all the type of predictions from the models so in classification voting assemble we've two quite votings which is tough voting and soft voting so in hard voting what the model generally does is it predicted the category es with the most important sum of ports from the models so it takes the bulk vote from all the predictions from the model then gives the output result and in terms of sentimental voting it takes the probability from all quite model then it takes its sum of the possibilities for every and each classes then it takes the utmost of these probability and predict the classes so are often "> are often "> are often "> this is often often often often often often what a general voting symbol does and what does her voting and soft rotting is it's been delivered so this is how a special voting and symbols we will use in our building our classification models by using hard voting and software and that we can get the prediction with reference to to our required conditions so if you if you would like to urge the class tables at the top we will use hard voting or if you would like to urge the prediction done maybe in terms of probabilities then you'll use soft rooting so let's just undergo the implementation of this voting examples and allow us to see how this will be achieved so in vertical symbols and this will be applied to a soft floating and symbols classification voting and symbol model using hard voting so this all implementation can be through with reference to regression voting assemble also and which is especially soft voting also so this is a general implementation which you which of them can be further extended to the regression voting and sample also and variance from numpy and we're getting to be importing make classification function from sql data set and we're getting to be importing cross file scope we're getting to be importing repeated satisfy k4 and k nearest classifier so immediately we're getting to building a kns classifier and with uh and we're getting to be building a series of classifier k nearest classifier in our model and can run in parallel so we'll see in how like how we'll be doing on this stuff and we're getting to be finally importing voting classifier function from sql and symbols so this function will generally help us to try to to the parallelism what we are talking about and in terms of classifier and if we do the voting regression then we've to try to to the we've to see that we've to enhance the classes from the voting regression and eventually we're getting to be importing matplotlib for visualization of the various accuracies of our model so let's just run it so within the next cell what we're gonna be doing is we're gonna be making our own data set so by using uh make underscore classification function so this is how we generally roll in the hay so immediately i'm giving a 1000 samples and 20 features and it it'll prepare a custom data set on behalf of me for analysis so let's just roll in the hay so here's the most cell which describes how a voting model can be built so this is nothing but a voting classifier model series of models which you would like to coach and and it takes voting parameter as which type of voting you would like to use that case it's hard voting or soft software so this is what our voting classifier takes in so immediately we're gonna building this model uh estimator parameters.

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Best CPU for machine learning

machine learning and AI episode different considerations of cpus in machine learning workstations and this was actually a touch bit difficult to place together because there is a lot of use cases within the world of computing and uh you recognize essentially a cpu which may be great for gaming isn't necessarily great for machine learning different features of the hardware are getting to be better for various applications and even even within the sector of machine 

learning there is a lot of various use cases that are getting to dictate exactly what features you are going to require in your hardware and things like your cpu my audience with this video are people looking to create their own workstations to try to to things like compete in kaggle competitions and really those that don't need to be paying a fortune to cloud services like amazon it's for people that who want this personal workstation to try to to relatively small machine learning workloads the rationale i say relatively small is that this the spectrum of machine learning is extremely vast and wide the requirements for a corporation are getting to be very different thanks to the various volume and also their available budget in these cases those companies are likely to be moving towards something just like the cloud or have a reasonably serious on-prem data center solution considerations that a corporation like tesla has got to believe are aren't getting to really presumably be applied to our smaller personal workstation scale and scale scale is basically getting to make a huge difference in machine learning the various components of cpus and which components of cpus are important for machine learning at this personal scale that we're going exactly what quite cpu to shop for i actually want to only provide the maximum amount information as possible about different architecture features of cpus and therefore the way this is often often often getting to relate to machine learning performance in order that ready to "> you'll really make your own decision supported your particular use case also as what you are looking for during a cpu and your budget which being said if you are doing find yourself wanting specific advice for for your particular use case trying to try to to and hopefully i could offer something useful alright so a number of the most components of cpus that we're getting to mention today are cores threads and pcie pci express lanes we'll start with cores everyone understands cores pretty much more cores are getting to allow more parallelization of your workload and these are closely tied along side threading where threads are quite an idea where you are able to separate a physical cpu core into several different virtual cores usually two threads per physical cpu core and amd and intel have their own techniques for for doing this but they're both able to quite do that same threading uh process and the number of threads that you simply get are gonna tell you ways many software processes you'll really be running on the cpu at just one occasion typically you would like that as high as possible but there is a couple other considerations which will or might not outweigh that as i'll get into during a bit cores and threads are pretty much linked together there's another another one that gets talked about quite bit and that is pcie express lanes so these are getting to be the highways so to talk that transfer data between your cpu ram and your gpu ram there are two main compute intensive tasks the cpu goes to be involved in during machine learning and people are pre-processing and model training pre-processing first and regardless of what your use case is data preparation and and reading in data before you quite start with everything goes to be done almost entirely together with your your cpu if you're doing bulk pre-processing the speed of your processing core goes to be vital you are going to require a high clock speed also number of cores goes to be important python has some very nice libraries that are getting to allow you to spread your workload over uh several cpu cores to actually get that parallel effect so considerations with bulk processing are getting to be primarily your clock speed how briskly you are able to crank through computations followed pretty closely by number of cpu cores sometimes however you are not getting to want to be doing a bulk pre-processing you are going to require to be doing mini execution and this is really where your items that you are looking to process are getting to be processed in an asynchronous manner and you are not getting to be doing this all directly and this is oftentimes applied in deep learning models.

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How machine learning model works

how machine learning model is trained tested and deployed for real world problems within the chosen domain so in machine learning we give input also as output to the machine learning model if it's generally a supervised machine learning model supervised machine learning model means you're giving input also as output in terms of machine learning output means labeled data so in figure you would possibly be seeing there are two boxes on the left the upper box contains various images

 or various shapes downside green box shows the name or the label or the output for those images for instance the sixth side image is named hexagon hexagon four side is named square and thirdness triangle so to the machine learning model you're giving labeled data that's shape and therefore the name of shape is additionally there so it's called labeled data if you're giving only shapes and not their name or output or label it'll be called unlabeled data which is that the usual phenomenon once we mention unsupervised machine learning model so coming to the purpose you're giving labeled data that's shaped within the ir name to the machine learning model giving some commands to algorithm that this is often often often often square this is triangle and this is hexagonal so machine learning will learn okay if this if there's any square that's if if there's one image having four sides equal size it's called square okay if there are three sides it'll be a triangle so it understands it learn it memorize then model is train now you'll give test data to the trained machine learning model then if you're showing image of square that's in terms of machine language if you're showing a picture having four equal sides then it'll give the prediction or the output or the result that's it's a square or if you're giving a triangle that's a shape geometrical shape having three sides it'll provides a result that's this triangle so what's vital in machine learning training so what's important during training so during this case you ought to show various sorts of squares to the machine learning model during running what does that mean you ought to show different sizes of the square suppose you're giving 100 images of same square so could be possible during testing or during real world deployment if you're using the machine learning model which has been trained by you by using square of same size and in testing or during deployment you're showing machine learning model otherwise you are using the machine learning model for a square whose size has never been seen by your machine learning model either during training or testing then there are chances it's going to do some error it means your model is over fitting over fitted means what's there are two words under fitting overfitting what does one mean by under fitting under fitting means you've got not shown sufficient sort of structures or shapes of various sizes different colors to the machine learning model for instance if you're showing all the red square to the machine learning model during training and testing and through deployment if you're using green square it's going to not give proper result it means you've got trained your machine learning model just for highly exclusive particular sort of input which can definitely fail the model during testing or during deployment so this is overfitting that's to coach particular machine learning model for a specific feature or for a specific input without much variety without much variation or differences what's under fitting you've got shown less data for instance you're giving 100 squares and just one in just two hexagonals otherwise you are showing a 50 squares of rate size in two squares of blue color so you're not exposing your machine learning model to equal set of features or equal set of input file means it'll be a biased machine learning model unintentionally for instance you're showing only hexagonal of blue color and not red color so it's not a wonderfully trained model it'll be called a under fitted model if i mention some different interesting example suppose you've got trained a machine learning model which is understood for image recognition and through testing you've got shown all the pictures of black people during unlocking the phone and suddenly i mean you're showing you're training a machine learning model for image recognition so you're showing hundreds or thousands of photos of black people or those that aren't fair or white skill and in the same machine learning training model you're showing only two or three images of White race so definitely you've got you're training a machine learning model.

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How to Interpret Machine Learning Models with LIME

explore thanks to a way to  the way to explain and interpret machine learning models with lime lime stands for local interpretable model agnostic explanations and provides an excellent way to see what is going on on below the surface the theoretical part the acronym lime stands for local interpretable model agnostic explanations the project may be a ll about explaining what machine learning

 models do lime currently supports explanations for tabular models text classifiers and image classifiers during a nutshell lime is employed to elucidate predictions of your machine learning model the reasons should assist you to know why the model behaves the way it does if the model isn't behaving needless to say there's a good chance you probably did something wrong within the data preparation phase to put in lime execute the pip command how lime works in practice you'll not interpret a model before you train it so is that the "> that is that the initiative the wine quality data set is straightforward to coach on and comes with a bunch of interpretable features the way to load it into python all attributes are numeric and therefore the re are not any missing values so you'll cross data preparation from the list train test split is that the next step the column quality is that the target variable with possible values of excellent and bad set the random state parameter to 42 if you would like to urge an equivalent split model training is the only thing left to try to to random forest classifier from psychic learn will do the work and you will got to fit it on the training set you will get an 80 accurate classifier out of the box consistent with the score variable and that is all you would like to start out with model interpretation the model you initially need to import the lime library and make a tabular explainer object it expects a few of parameters the training data parameter expects the info assail which the model was trained it must be during a new pirate format the feature names parameter execs the column names for an equivalent training set these are required because the numpy array is passed within the first parameter next there is a category names parameter it expects an inventory of distinct classes from the target variable finally there's the mode parameter it expects a string representing the sort of problem you're solving and that is it you'll start interpreting a nasty wine comes in first the second row of the test set represents wine classified as bad you'll call the explain instance function of the explainer object to interpret the prediction this function expects a few of parameters the primary one is data row and it represents one observation from the info set it's the row that you would like to interpret the prediction the other is predict function it's a function wont to make predictions the predict probe from the model is a great option because it shows probabilities the way to write this in code the model is 81 confident this is often often a nasty wine the values of alcohol sulfates and total sulphur dioxide increase wine's chance to be classified as bad the volatile acidity is the just one that decreases it wine next you can find one at the fifth row of the test set let's repeat the procedure and take a glance at the interpretation the model is 100 confident it is a good wine and the top three predictors show it that's how lime works during a nutshell there are different visualizations available and you're not limited to interpreting only one instance but this is enough to urge you started things up next today you've learned what lime is the way to train an easy machine learning model and the way to interpret its predictions knowing why the model behaves the way it does is important for explaining it to non-tech people curious about machine learning due to algorithms like lime and shop recorder models are a thing of the past.

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How To Write a Cover Letter For a Machine Learning Engineer?

how to write down a canopy letter for a machine learning engineer position the foremost important writing tips for machine learning engineers firstly knowledgeable covering letter requires a proper fonda font size that's easy to read you'll pick calabree ariel or times new roman with a ten to 12 point font size furthermore stick with a 1 page document with around 300 to 400 words and divide your letter into four paragraphs secondly distinguish two styles american and british english covering letter s thirdly address your letter to someone and write an appropriate greeting for instance dear mrs smith or dear miss smith

 you'll use mrs because the abbreviation if the recipient may be a wife now if you do not know the reader's gender then i might advise you to write the complete name find any contact information their hiring manager fourthly remember your covering letter may be a supplement to your resume don't list your skills and knowledge such as you neutralize your resume but instead provide context with preferably numerical and statistical information for instance what have you ever ever achieved in terms of designing and developing machine learning and deep learning systems or what about your ability to code in python or java confirm to link these qualifications to the work requirements eventually concentrate to your covering letter ending choose for instance count regards past regards or sincerely you're sincerely as british and americans tend to reverse the order and write sincerely nowadays sincerely may be a common and unacceptable close for american covering letter s but you'll also write yours faithfully which may be used when the recipient isn't addressed by name like within the ir hiring manager it's a british usage and yours truly is that the american equivalent alright that's it for the foremost important writing tips next up writing the duvet letter example first you'd like to list your contact information on the left side in between white lines underneath we put the date then the hiring manager's name and position followed by the company's information as for desalination we write dear mr smith this is often how you structure an American English cover letter for british english you would like to place the contact information and date on the proper side then you would like to place the date first then the month and exclude the comma underneath we notice that the topic is included within the british english letter it's commonly utilized in the united kingdom but usually overlooked within the us for american usage the month is placed first followed by the date and you would like to insert a comma between the day and year we also include a dot after the abbreviation mr or ms for british english you'll leave it call at the primary paragraph also called the introduction you would like to point out your excitement for the position and company you apply for mention exactly where you found the vacancy for what position and therefore the refore the establishment you would wish to work on within the second paragraph you shortly introduce yourself followed by a motivation for the work opportunity attempt to answer questions like what triggers you to figure as a machine learning engineer or why does one want to figure at their company show that you simply simply are actually curious about specifically this job role at their company so match your qualifications to the work requirements don't list your skills and knowledge such as you neutralize your resume but instead provide context preferably with numerical and or statistical information so what have you achieved in numbers statistics or awards you have been granted a key here is to convince the reader that you actually obtain the required qualifications in the last paragraph you would like to precise your interest to further discuss your candidacy and the subject of interest ask your attached references and resume and include your telephone number in order that they can easily reach bent you do not forget to thank the reader for taking their time to review your cover letter and shut during a compelling way so kind or best regards followed by your name and surname all.

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Machine Learning Across the Digital Attack Surface

the sophistication of cyber threats is growing at an exponential rate and attackers are leveraging automation and advanced techniques to create deploy and execute targeted attacks subsequent generation of security requires a coordinated fabric approach that employs machine learning at multiple layers within the defenses to spot normal patterns and isolate targeted and patient zero attacks many vendors claim that they need some kind of machine learning and ai within the se 3d solutions i'm often asked how does fortinet uses machine 

learning and ai within the fortinet security fabric cybersecurity may be a game of offense and defense and closely resembles modern-day warfare defending the enterprise assets has never been a simple task sizeable concerns around data privacy daily workers securing remote offices and data stored within the cloud has always been on top of their minds branches process more sensitive data and transactions as enterprise continues to information and data flows in and out of the network continuously so as to guard the crown jewels fortinet security fabric leverages machine learning and ai to assist enterprise to strengthen the defense fortigate next-gen firewall is that the first and basic layer of security fabric defense against adversaries leveraging ai-based 4d guard security services and updates continuously to supply the simplest coverage forty analyzer is added for extra visibility reporting and incident response today's branches have their own digital attack services many are processing sensitive user data subject to regulatory requirements and even have their own set of teleworkers and other factors obviously 40k is that the first line of defense at the branch but it also facilitates business-critical sd-wan connectivity and simplifies its overall branch deployments with managed wireless and switch networks in the end it's all about securing and simplifying the branch deployments and streamlining business critical applications as security operations center becomes more mature they shift to investigating more sophisticated attacks one among the primary products software adopt is for the sandbox by doing that they are adding a machine learning component into the material 40 sandbox works with other 14 solutions to supply the power to research and stop advanced attacks like 4d mail for mail or 365 security and 40 web or web application attacks other additional machine learning components of the safety fabric involves user behavior analytics and endpoint malicious behavior as sock continues to mature they'll adopt guardians or mergers themselves against tools like mitre or kill chain they'll still use tools like deception to divert attacks from key assets place traps to proactively hunt discover and profile attackers increasing the value of attacks and also for the ai virtual security analyst to classify malware look for outbreaks and traces the source of infection all the solutions within the ai enabled fortinet security fabric works endlessly to supply a complete defense in cyber security and a comprehensive set of machine learning inspection points across the digital attack service.

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An Induction Motor Control System Based on Artificial Intelligence!

induction control system supported AI AI as an example of an induction motor with a frequency changer management is implemented employing a fuzzy control fuzzy output controllers introduction fuzzy controller is applied if there's insufficient knowledge of an impact object supported this experiment the expert form the idea of the principles of the fuji controllers an example is what upon the parameters of which water consumption movement of initiates of the whole system the utilization of physics controllers also can be just this by the non-linearities of 

the control object now i'm getting to mention an impact system the control object is an induction motor controlled by a frequency changer the system consists of an impact object and a pid controller covered by a feedbacks and a fuji adaptation blocks which we call fab of the parameters of the pid controller and therefore the refore the refore the refore the refore the source of the front signal and the structural diagram is shown below moreover control process consists of three basic steps which is that the first one is falsification of input variables second is defining fuzzy or producing blocks of rules and the third one is diffusification of output variable in fuzzy output systems a rule base to regulate the coefficient when compiling the control logic it's necessary to be guided by the subsequent consideration once we increase or decrease the worth of this parameter k p k i and k d it'll affect to the transient response and the stability of the system therefore we offer the subsequent pattern for calculating these parameters the greater absolutely the value of the mismatch and its derivative the best would be kp and the less for ki and kd and vivosa the rule for pid control coefficient are given within the tables on the proper hand side for subsequent step is optimization of coefficient once we use fab as source elimination we'd like to unravel the matter of setting the parameter of pid control to be read our acceptable well and to try to to so we'd like to incorporate the parameter of gain p gain i and gandhi for the results of our optimization we will see that we will read the decimal value of the transient response which is t but 0.3 and overshoot of 10 percent and from our result the t transient response is adequate to 0.9 second and therefore the overshoot of 10 percent from the modeling result the calculations were administered within the dynamic simulation environment sim intake the transient response of tendering the stimulation is descending on the left graph to simulate a change within the system parameter the damping coefficient of the control object will vary from 1 to 2 for five seconds it are often noticed that the output is stable with the overshoot of 10 percent and a decay time of 0.3 seconds static error in steady state is adequate to 2 as a justification for the appropriateness of the utilization fab underlying graph may be a graph of the ten ts process without the utilization of fab the pid controller coefficient have fake value obtained by the fab unit during the previous simulation by kp is adequate to 0.866 ki is adequate to 0.113 and kde is adequate to 0.113 from the graph obtained it are often seen that such a system is characterized by lower speed and greater overshoot of 20 next slide last we will conclude that employing a pid controller with adaptation of its parameter to a fab is far more efficient than employing a separate pd controller or a separately faster controller.

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10 Things That REALLY Gets Tech Professionals Promoted!

how does one get promoted as a tech professional if there was a secret resource ready to you'll use and apply to your career to urge promoted what would that mean to you in terms of the elevated status you get the gain you would possibly get from it and just overall satisfaction from the work you would possibly get that said we're gonna dive in i've put together a presentation around this there are 10 things and this is often often often often often no particular order you'll use this and slice and dice the way you would like but if you apply this stuff in your career

 and this is what i tell tons of the people i coach and mentor and advice these are the items i tell them to believe as they're quite working within the ir careers to make sure that they get promoted we're gonna dive in into the very first one into the very first one and this one may be a no-brainer if we're having a conversation like this today it really really is vital for you to know that's this whole idea of getting promoted what you would like alright there are some people within the ir career that they do not want to urge promoted they do not want to travel thereto next level they're comfortable where they're they get the responsibility that they get which is that's it alright then if you begin talking about getting promoted getting to management or taking more responsibility or doing differing types of roles which may not be for that person and if that's you that's okay nobody wants to be promoted not everybody's getting to get promoted within the first place so if you're comfortable where you're that's okay alright persist with what you're comfortable with but if you're somebody that desires to urge promoted you would like to travel thereto next level you would like to require on more responsibility you would like to climb up the ladder for whatever reason alright then this could be for you so it's extremely important first of all to know the expectation because we will mention all the ideas and tricks to use to urge promoted or what's the key sauce to urge promoted it doesn't apply to you if you do not want to urge promoted within the first place so thereupon said getting out of the way if you're going to stay watching this i'm assuming that you simply simply simply simply want to urge promoted so this is getting to bring us into the second point that we've here on the screen okay and this is a no brainer it's a bottom line you can't get promoted if you are not delivering on your current job or your current promises or your current role or your current responsibility whatever it is you're doing whether you're working as a dba as a manager as a you recognize a developer a qa tester a devops engineer it doesn't matter what you're doing once you were hired for that job you've got a group of responsibilities that you were hired to try to to or that there are expectations that you gotta deliver on those things alright that's what's currently paying you today and therefore the re is no way reasonably on planet earth that you're gonna get promoted to subsequent role if you can't show competence within your current role so as a bare minimum if you're battling your current job current job you would like to see that one off the list right this is a no brainer you can't skip this step alright so now assuming assuming that you are delivering on the guarantees and you are a stellar employee in your current job in your current role let's enter subsequent thing to start out elevating your status into a promotable asset into an employee that the manager and senior level are people are looking and saying we gotta move this person up the ranks we gotta elevate them up the ranks within the corporation or within the business alright and that really involves you having the ability to exit of your shell and help the people around you right and when something like this comes up especially for folks that are within the technology field and technology professionals it seems a touch bit sort of a misnomer it seems a touch bit like we're swinging from the left field here because technology professionals are wont to you recognize you recognize being the smart people in the room the people that everybody looks up to them and the it looks like that's where everybody should be not if you would like to urge promoted not if you would like to realize more responsibility beyond your task into a broader responsibility which may involve people in the organization and if if that's your expectation you've got to be willing and able to demonstrate that.

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Networking Overview and HCI Management

have a management network a storage network a vm network vlans if you wanted for isolation then we've the ipmi okay that's for out of land management now solid fire and element os it's an iscsi platform okay so that is the protocol that we use to attach uh to vmware and therefore the connection to the highest users also okay so we use iscsi in order that means we've to make um vmfs data stores for sound traffic okay it isn't nfs data stores those are vmfs data stores uh on vmware so let's go and see that the 2 sorts of configurations 

that we we support uh you'll accompany a six cable configuration uh you'll see we've two ports for management two ports for storage two more for the vm traffic then also we've one for the out of van management that is the ipmi and uh if we accompany this same configuration now let me let me return real quick here uh if we attend the proper side the 2 cable configuration meaning that we are getting to share two physical ports for management for storage and 4 vm we've to take care with the performance here uh that's something that we go and that we see uh on the hci training the connections to the compute node those are those we've on the top then we've the connection to the storage nodes also and you'll see once more both six cable and two cable configuration the configuration are often done by the mde okay once you attend the nde the netapp deployment engine you'll choose a topology uh maybe you do not see all the small print here but it says six cable two cable um then we've another topology for the storage node and for the compute node also okay so that's done by the nde again i will be able to offer you a couple of more details on mde so let's accompany the hci setup the first step we've to organize for the installation uh we've to settle on if we would like to use an existing vcenter or we would like to put in a replacement one we've to settle on the networking environment maybe two maybe six cables as we have seen then we will install we will rack and stack then we configure with the mde okay now the nde it is so easy can have hci up and running in about 45 minutes it reduces 400 inputs to fewer than 30. we will reuse data like maybe usernames and passwords and that we have predefined workflows and predefined steps as you'll see on the proper side it goes with prerequisites then in fact you've got to to simply accept the top user license we accompany the vsphere configuration credentials topology inventory network settings we do a fast review on the configuration then it shows you that it's now completed okay again around 45 minutes and it is so easy to configure using that netapp deployment engine for the management now that we for instance we've the physical connections now that we've the uh initial setup for the hci management we will use the element plugin for the middle okay it is also referred to as the vcp uh so we will manage the storage resources like data stores volumes qos the storage cluster components and also we will manage data protection the compute nodes and this is often the interesting part the compute nodes they seem as esxi hosts so you'll manage them in vsphere a bit like the other esxi host hci the netapp hci events and faults also are you'll see them through vsphere as system alarms also you'll attend your elementos ui now the management node that we have seen that it is also deployed by the deployment engine it's checking the compute nodes and it appears as a vm within the cluster after the deployment so you'll also attend the management note and you'll see a couple of more details through the management bill and hci sends performance and alert statistics to the netapp solidfire active iq service so you'll also get your solidfire active iq app then you'll cf. alarms details space savings a bit like the other netapp system through active iq so with nde uh we deploy the storage cluster um we will also deploy the vmware plugin for vcenter and it also as you'll see all the way down it deploys the m node that is the name that we use for the management node and as we have seen it's getting to be a vm.

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Learn to configure Azure Machine Learning Workspaces

azure machine learning workspace important an azure machine learning workspace is it gives you the power to manage compute instances manage experiments pipelines data sets register models work with deployment endpoints and associated resources first we'll attend azure machine learning studio from here i'll attend the add icon once I attend the add icon notice there's  that there's several tabs the fundamentals tabs is basically the foremost of the knowledge

 you will need to line up initially and you'll get really an extended way by just configuring that tab also though there is the networking tab which allows you to toggle between a public endpoint or a personal endpoint there is a advanced tab which allows you to possess your own level of encryption including during this case you'll select a high business impact workspace and this is able to limit the quantity of data that might be sent to microsoft for instance you were performing on maybe a medical project or a government project where there have been specific requirements you'd select this icon there also are tags and therefore the tags are very useful therein it might be how to watch costs in your organization for instance for instance this was the info science team i could undergo here and put a put a tag in and say you recognize resource one and this is able to be something that i could then later put into a price report attend the essential setup and what i'm going this is able to be the billing account and i am getting to select an existing resource group during this case i've already got one found out so i'll plow ahead and choose that next we'll attend the workspace details from here i can specify the name of the workspace here i'll call this udacity workspace then i might also undergo here and choose what region within the us i would like to use during this case i could leave the u.s east that's an honest region on behalf of me but there are several other regions that you simply can select including europe north america korea japan many different regions finally a crucial choice to remember is that for several of the advanced features you will need to pick enterprise and enterprise will offer you the power to run things just like the advanced automl features great now.

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How dangerous is SOCIAL ENGINEERING?

how dangerous is social engineering this guy just got lured into transferring money to someone whom he thought was his boss how did the hacker convince him to send him money well the solution to the present are going to be social engineering it it must be possible to recall the transfer come on a bank together with your clout i understand that we authorized the transfer but the the the entire transaction was fraudulent but the recipient was a bogus company the entire thing was made up you're telling me it's it's impossible to reverse the transfer social engineering is that the art of manipulating someone 

into abandoning tip like Social Security number access codes or valuables hacks supported social engineering are built around exploiting the human psychology to collect necessary background information like potential points of entry and weak security protocols needed to proceed with the attack these attacks cash in of human vulnerabilities like emotions trust or habit so as to convince individuals to try to to certain tasks like clicking a fraudulent link cyber criminals have learned that a carefully written email voicemail or text message can convince people to transfer money divulge tip reveal password grant access to critical resources or download a file that installs malware on the corporate network social engineering scams are often employed by hackers but social engineering isn't limited to information security it's something we all experience a day what makes social engineering dangerous is that it relies on human error instead of vulnerabilities in software and operating systems the attacker tends to motivate the user into compromising themselves instead of using brute force methods to breach your data this process can happen during a single email or over months during a series of social media chats it could even be a face-to-face interaction here are the five commonest sorts of social engineering techniques the primary sort of social engineering is baiting during this attack attackers leave malware infected floppy disks cd-roms or usb flash drives with the label of the cooperate organization in locations people will find them like bathrooms elevators sidewalks parking lots etc an unknowing employee may devour the bait out of curiosity and insert it into a piece or computer leading to automatic malware installation on the system giving attackers access to the victim's pc and maybe the company's internal network the other is phishing this system involves sending an email or a text message that appears to return from legitimate business a bank or mastercard company requesting verification of data and directing them to a fake site where their login credentials are going to be sent to the attacker the e-mail will appear to originate from an entity the user recognizes like a lover colleague or service provider this sort of attack is successful because people are less suspicious of names or people they trust the third is pretexting pretexting uses a deceptive identity because the pretext to interact a targeted victim during a manner that increases the prospect the victim will divulge information or perform actions which will compromise their system or network pre-texting also can be wont to impersonate co-workers police bank tax authorities clergy insurance investigators or the other individual into tricking a victim into abandoning information some users may believe the scam compliance surrender their personal and financial information the fourth is scareware scareware may be a sort of malware utilized in alerting the user to false threats or problems on their computing system this sort of attack usually manifests itself as malicious software that tricks users into purchasing fake antivirus protection and other potentially dangerous software the fifth is tailgating tailgating involves the passage of an unauthorized user either accidental or forced into restricted areas secured by unattended electronic access control for instance by reference card tailgating relies on human trust to offer the criminal physical access to a secure building or area social engineering attacks are particularly difficult to stop because they're designed to play on natural human characteristics like curiosity anger respect for authority and therefore the desire.

[kourdem]

The three facets of data science

so skills they're a number of the foremost in-demand competitive data science skills to need for an industry professional in our rapidly changing and data-driven economy which they can assist you to accelerate your current job role or redirect your career journey into all types of latest possibilities and sus academy for data science may be a virtual learning experience that equips data scientists with sas knowledge and skills they need ed to empower society organizations and research through data i recently had the chance to look at these courses and gain an in-depth perspective of how the training can augment data scientists

 within the ir work there are three different credential levels counting on your interest experience level and academic goals the primary one is data curation you'll be ready to go from being a knowledge science beginner to having an understanding of hadoop data management and different big data concepts so ready to "> you'll help companies to urge the insights and therefore the refore the value that they need from all of their actionable data and available data answer important questions and drive their goals forward the other may be a dvanced analytics you'll be ready to solve important business problems regardless of the domain by building and by comparing and by describing complicated models and applying your extended analytics skills in sophisticated analytics technologies like machine learning but also predictive analytics and the third one may be a i and machinery so you'll be able to approach business challenges from an analytics foundation and choose deploy and manage models so you'll help organizations to use ai to reinforce their operations and to innovate so data creation credentials are often combined with advanced analytics or ai and machine learning credentials so you'll become a licensed data scientist who is that this for you're an aspiring or a practicing data scientist who want to further your educational journey and gain practical new skill sets by using sas along side professional level credentials and did you recognize 70 of analytics jobs are in sas programming this is often consistent with recent research and you would like to eventually get hired by a corporation whether it's in finance pharmaceuticals or manufacturing or even healthcare or government or retail or technology we use various sas features to enhance their operations and advance innovation through technologies like cloud like ai machine learning and iot in current circumstances they have brought tons of uncertainty but certain job skills have remained resilient and integral to the acceleration of digital transformation and advancement for businesses and entities across any sort of domain so what does this boil right down to is how are you able to hone your data science capabilities with top skills needed to support today's data centric digital organizations then government agencies or research institutions in their initiatives to rework to innovate and to grow sales academy for data science is a digital learning program that mixes courses in data creation advanced analytics and ai and machine learning with hands-on virtual labs a lively community of peers and sas experts you get 30 days of this program for free of charge and starts sponsoring this so data scientists can gain a far better understanding of how sus can augment their work and what you'll learn you will learn to develop the talents you would like to become a more versatile data scientist or a business analyst using sas tools like why data creation is a critical data science skill and the way it's wont to answer key business questions and the way to use sus technology to wash data and to form it more usable you furthermore may train in machine learning in computer vision and forecasting and optimization to find out the way to prepare data and use and deploy machine learning models you will apply concepts with the entire access to the sas software and you will have the chance to utilize open source tools like python or hive but also hadoop and r and the way to urge access to urge your 30 day free access just found out your sas profile or login if you've got already won then accept the contract and on the training page choose sus academy for data science free trial this virtual lab provides a competitive learning experience and how it is a good way to figure with world cases so you'll practice and you'll apply concepts and learning and better steel oneself against your exams and is that the "> what is the career advantage of gaining skills in sas ongoing education is the most precious investment data scientists can make in their future and soft skills and professional level credentials can assist you to require advantage of the work possibilities that are available and that are merging in the market and advance up your current career ladder.

[kourdem]

How to Master Your Data Scientists Skills?

so skills they're a number of the foremost in-demand competitive data science skills to need for an industry professional in our rapidly changing and data-driven economy which they can assist you to accelerate your current job role or redirect your career journey into all types of latest possibilities and sus academy for data science may be a virtual learning experience that equips data scientists with sas knowledge and skills they need ed to empower society organizations and research through data i recently had the chance to look at these courses

 and gain an in-depth perspective of how the training can augment data scientists within the ir work there are three different credential levels counting on your interest experience level and academic goals the primary one is data curation you'll be ready to go from being a knowledge science beginner to having an understanding of hadoop data management and different big data concepts so ready to "> you'll help companies to urge the insights and therefore the refore the value that they need from all of their actionable data and available data answer important questions and drive their goals forward the other may be a dvanced analytics you'll be ready to solve important business problems regardless of the domain by building and by comparing and by describing complicated models and applying your extended analytics skills in sophisticated analytics technologies like machine learning but also predictive analytics and the third one may be a i and machinery so you'll be able to approach business challenges from an analytics foundation and choose deploy and manage models so you'll help organizations to use ai to reinforce their operations and to innovate so data creation credentials are often combined with advanced analytics or ai and machine learning credentials so you'll become a licensed data scientist who is that this for you're an aspiring or a practicing data scientist who want to further your educational journey and gain practical new skill sets by using sas along side professional level credentials and did you recognize 70 of analytics jobs are in sas programming this is often consistent with recent research and you would like to eventually get hired by a corporation whether it's in finance pharmaceuticals or manufacturing or even healthcare or government or retail or technology we use various sas features to enhance their operations and advance innovation through technologies like cloud like ai machine learning and iot in current circumstances they have brought tons of uncertainty but certain job skills have remained resilient and integral to the acceleration of digital transformation and advancement for businesses and entities across any sort of domain so what does this boil right down to is how are you able to hone your data science capabilities with top skills needed to support today's data centric digital organizations then government agencies or research institutions in their initiatives to rework to innovate and to grow sales academy for data science is a digital learning program that mixes courses in data creation advanced analytics and ai and machine learning with hands-on virtual labs a lively community of peers and sas experts you get 30 days of this program for free of charge and starts sponsoring this so data scientists can gain a far better understanding of how sus can augment their work and what you'll learn you will learn to develop the talents you would like to become a more versatile data scientist or a business analyst using sas tools like why data creation is a critical data science skill and the way it's wont to answer key business questions and the way to use sus technology to wash data and to form it more usable you furthermore may train in machine learning in computer vision and forecasting and optimization to find out the way to prepare data and use and deploy machine learning models you will apply concepts with the entire access to the sas software and you will have the chance to utilize open source tools like python or hive but also hadoop and r and the way to urge access to urge your 30 day free access just found out your sas profile or login if you've got already won then accept the contract and on the training page choose sus academy for data science free trial this virtual lab provides a competitive learning experience and how it is a good way to figure with world cases so you'll practice and you'll apply concepts and learning and better steel oneself against your exams and is that the "> what is the career advantage of gaining skills in sas ongoing education is the most precious investment data scientists can make in their future and soft skills and professional level credentials can assist you to require advantage of the work possibilities that are available and that are merging in the market and advance up your current career ladder.

[kourdem]

What Is Big Data?

what is big data in simple terms so big data may be a dditionally data though it's huge with more complex data sets especially from new data sources that's growing exponentially with time these data sets are so large that traditional processing softwares just cannot manage them but these substantial volumes of knowledge are often wont to address business issues you would not are ready to tackle before a classic example of massive data is facebook the statistics 

shows that 500 plus terabytes of latest data gets ingested into the info bases of facebook every single day the info is primarily generated in terms of comments videos and photo uploads message exchanges etc now talking about the kinds of massive data big data is a combination of structured semi-structured and unstructured data collected by companies which will be mined for information and utilized in machine learning projects predictive modeling and other advanced analytics applications structured data is that the data which will be stored accessed and processed within the sort of fixed format semi-structured data can contain both sorts of data unstructured data is that the data with unknown form or the structure additionally to the dimensions being huge unstructured data possesses multiple challenges in terms of its processing for deriving values out of it now coming to the characteristics of massive data they include favorite volume the name big data itself relates to a size which is large size of knowledge plays a particularly crucial role in determining the worth out of knowledge number two variety subsequent element of massive data is its variety variety refers to heterogeneous sources of knowledge and therefore the nature of data are both structured also as unstructured number three velocity the term velocity describes the pace at which data is generated how briskly the knowledge is produced and processed to satisfy the stress determines the important potential within the data number four variability this describes the inconsistency which may be proven by the info itself sometimes therefore hampering the method of having the ability to handle and manage the data effectively big data are often utilized in development predictive maintenance customer experience fraud and compliance machine learning and operational efficiency now why is big data important the importance of massive data doesn't revolve around what proportion data a business has but how a corporation utilizes the collected data big data analytics helps a corporation in cost saving by use of massive data tools like hadoop and cloud-based analytics time reduction by use of high-speed tools like adobe and in-memory analytics understand the market conditions by analyzing big data control online reputation by sentiment analysis boost customer acquisition and retention by using big data analytics solve advertisers problems and offer marketing insights by using big data analytics big data analytics as a driver of development and innovations.

[kourdem]

what is data developer and what's the difference between data analyst and data engineer?

big data developer and therefore the big data developer may be a bit different from a knowledge analysis actually there are tons of differences between the large data developer or data big data engineer they're managing for like billions of billions of knowledge well for the info analysis probably they such an enormous amount of knowledge like many data from an area company that generates their local data for instance they're solid products and that they got this orders or the locations or something like that the info isn't that enormous for the large data for instance 

the entire bank or the entire system or the stock exchange the middle of the stock exchange or something like that they have to handle like sort of a fuel tb of knowledge you recognize one get to shop for equals the 1024 ready to "> you'll buy so those are the info that we'd like to handle this is often often often the likes of huge amount of knowledge well therein case there are tons of sort of a nd structured data those data are isn't like a catalyzed they're those data aren't that or people got to use it they got to manage it like uh people call it data dictionary or index so those people are my work or my department's work goes to manage all those hr data and put into the position or we call it ods which suggests these resources are the first resources and put into an edge that we'd like to sometimes calculate it first then we're getting to manage it and put into a special departments because those of knowledge actually from like um solid positions or different like are our centers from different parts we'd like to offer it to the precise specific place and that we got to handle it for the tools we are getting to use tons of various tools due to those billions of billions alittle amount of number due to the numbers huge just one service sometimes and not be able to directly put into it because this server this online server features a limited space in order that they need to put into the many many different servers you recognize different places so therein case we'd like to tug this data and use it like different tools you recognize heave hadoop but spark extract extracted data means like i buy it from the nugget i buy it from this mountain i buy something from it i buy some plants we go get some food from it that's called it is a persistent extract then we're getting to use it to try to to something like i'm getting to cook so i buy some food then i buy into i cook it i cook a dinner cook a lunch this is the method called transaction then i'm getting to make it to the place to the to the place called dw where means such as you put it to the info then i provides it to the truth part well I even have to ascertain it into different dimensions or different timeline for instance there's an order so who bought it at which era at which specific place or what departments or what so we are getting to see into a special dimensions so within the future once we want to urge it getting to "> we'll know alright this is a knowledge that's getting to we will use it to to stress that in fact within the future albeit the info developer not that uh almost like the info analysis but some works is union are are similar so need to analysis it but a really cool a part of the info developer is that we're getting to see it at the life like something has changed then this data or or this report or we are going to ascertain an enormous screen to see this stuff change and locally and different various things .

[kourdem]

how to engineer data for analysis ?

Server Based Network A Server-based network may be a network during which network security and storage are managed centrally by one or more servers. How Server-based Networks Work? during a server-based network, special computers called servers handle network tasks like authenticating users, storing files, managing printers, and running applications like database and e-mail programs. Security is usually centralized during a security provider, which allows users to possess one user account for logging on to any computer 

within the network. Because files are stored centrally, they will be easily secured and protected . Server-based networks are more costly and sophisticated to line up and administer than peer-to-peer networks, and that they often require the services of a full-time network administrator. they're ideal for businesses that are concerned about security and file integrity and have quite 10 computers. Microsoft Windows 2012 or Windows 2016 are ideal operating systems for server-based networks. they provide centralized network administration, networking that's easy to line up and configure, NTFS filing system security, file and print sharing, user profiles that allow multiple users to share one computer or allow one user to go online to several computers, Routing and Remote Access for supporting mobile users, and Internet Information Services (IIS) for establishing an intranet or Internet presence.