Ninth annual Cullowhee Canoe Slalom

first_imgThe ninth annual Cullowhee Canoe Slalom will offer a family-friendly paddling competition beginning at 9 a.m. Saturday, Sept. 22, on a calm section of the Tuckaseigee River near Western Carolina University. The competition will start at the Locust Creek put-in just downstream from the greenway bridge on Old Cullowhee Road, with the course including nine gates on flat but moving water and competitions for canoes, kayaks and paddleboards. Canoes, paddles and personal floatation devices will be provided, but kayakers and paddle boarders should bring their own boats. All participants should bring closed-toed shoes, a water bottle and clothes that can get wet.The course will be open for practice from 1 to 7 p.m. Friday Sept. 21.$5 per person, per event, with food available on-site for purchase. Register by 5 p.m. Wednesday, Sept. 19, by emailing [email protected] Day-of registration not available.Hosted by WCU Parks and Recreation Management Program students and the Jackson County Parks and Recreation Department. Proceeds benefit the Parks and Recreation Management Scholarship Fund.last_img read more

Board members login

first_imgBoard members can access Board documents online. Please login to gain access:{loadposition loginbox} If you do not have a password, please contact Sallyann Niven on  +27-11-4841400.last_img

Google Posts Strong Q2 Earnings While Putting Motorola on the Books

first_imgTop Reasons to Go With Managed WordPress Hosting Why Tech Companies Need Simpler Terms of Servic… Adding Motorola to the family caused Google’s employee numbers to skyrocket. The company already had 34,000 workers, and Motorola adds another 20,000. The Motorola loss breaks down to $192 million for the mobile segment and $41 million for the home segment. The loss represents 19% of Motorola’s Q2 revenue of $1.25 billion, $843 million of which came from mobile and $407 million from the home segment.The earnings call will stream live on YouTube momentarily. Watch it here: Related Posts jon mitchell A Web Developer’s New Best Friend is the AI Wai… 8 Best WordPress Hosting Solutions on the Market Tags:#Google#web Google has announced its earnings for the second quarter of 2012. It closed the deal on its acquisition of Motorola Mobility this quarter, and that acquisition’s assets and liabilities are now on the balance sheet. It was a strong quarter for Google overall, posting 21% year-on-year revenue growth for Google’s own properties, but Motorola racked up a $233 million loss. Despite running at a loss, Motorola netted Google $1.25 billion in revenue.Google’s total quarterly revenue was $12.21 billion, up 35% from Q2 2011, including revenue from Google-owned sites and partner sites not part of the main Google portfolio. Revenues from outside the U.S. accounted for 54% of Google’s quarterly revenue, holding steady from a year ago.Paid clicks increased just 1% over the first quarter of 2012, but that’s up 42% from a year ago. Cost per click increased 1% over last quarter as well, but that’s down 16% from last year.The most interesting number to watch is Google’s traffic acquisition cost, which is the amount of money it costs Google to get user eyeballs on its ads, whether on desktop or mobile. This cost increased to $2.6 billion this quarter, up from $2.11 billion in Q2 2011. As a percentage of advertising revenues, TAC was 25% this quarter, compared to 24% last year.last_img read more

Know Your Enemy: The 5 Different Types of Data Breach

first_imgRichard Parker is senior writer at https://www.equities.com/user/Richard & https://www.theselfemployed.com/profile/richardparker/ . He covers industry-specific topics such as Entrepreneurship, Data/Security, Startups, Industrial, Growth Equity Community, Smart Cities, Connected Devices & Smart Homes. Leveraging Big Data that Data Websites Should T… AI: How it’s Impacting Surveillance Data Storage Richard Parker How Data Analytics Can Save Lives A Web Developer’s New Best Friend is the AI Wai… Data breach, the bane of many security experts. Anybody can fall victim to a data breach at any time. The damage is usually extensive and expensive if not utterly debilitating. Breaches are a cancer that never knows remission and a significant cause of concern in the connected world of today. What is a data breach to begin with? Well, you need to know your enemy, and there are about five different types of data breach.Here is a quick and straightforward analogy. If a burglar picks your lock or breaks your window and enters your house, that is a security breach. If the burglar steals your documents and personal information and then leaves, that is a data breach.According to an article on Wikipedia, “A data breach is a security incident in which sensitive, protected or confidential data is copied, transmitted, viewed, stolen or used by an individual unauthorized to do so.” A friend might steal a couple of your randy pictures to expose or prank you on Facebook; data breaches usually happen on a colossal scale involving millions if not billions of records. Big companies (you know, the kind you’d never imagine would fall victim) such as Yahoo, and Equifax among others aren’t safe either. When you think of it, attackers seem to love big and blue-chip companies because of the more significant the impact, the fatter the paycheck.The stolen information is then used to commit credit card fraud, identity theft, and a host of other heinous crimes. Some attackers will even sell the information in bulk on the dark web, giving even more bad guys the chance to commit abhorrent atrocities ranging from espionage to blackmail and the list goes on. Data breaches are a severe problem that mandates organizations to prepare beforehand.The first step in preparing is awareness about the 5 different types of data breach. If you know how the enemy operates, you can put countermeasures in place.For each of the five types of data breach, you’ll learn a couple of preventative measures so that you can bolster the security of your systems. Keep in mind that attackers hardly rest, so don’t you sleep either. Keep learning and implementing the best security practices and stay ahead of the bad guys. Always remember to share your concerns about security and give each other the best security tips you hear about.5 Different Types of Data Breach 2019This list of data breaches is in no particular order, but they are all serious areas of concern for any organization or person looking to stay safe from data breach.Physical TheftWho has ever watched the Mission: Impossible film that was released in 1996? If you haven’t seen it — find it and watch it. For those who watched the film, I think you’ll agree when I say: We should laud the director, Brian De Palma, for that one famous scene where Ethan Hunt (Tom Cruise) rappels from the vent of an incredibly secure CIA vault to steal the NOC list that contained the real names of agents in the field.THAT PEOPLE is a classic example of data breach by physical theft, but we celebrated Tom Cruise for the act. In the real world, things might not be as dramatic, but data breach by physical theft is very much a reality for many organizations. It could be as simple as someone plugging a USB drive into a server containing sensitive and business-critical information, or as brazen as someone carrying a hard disk out of your business premises. If anybody can walk out of your premises with sensitive business data, you’re in deeper trouble than you would like to admit.Leaving confidential documents in plain sight or disposing of sensitive information improperly (yes, a determined data thief won’t have qualms about going through your trash) can also expose you to a data breach. It’s the main reason vaults (but clearly not that CIA vault in Ethan’s case), shredders and furnaces were invented – to protect and get rid of sensitive information that mustn’t fall into the wrong hands.To protect your organization from physical theft of data, implement stringent security protocols that ensure only authorized people have access to privileged and sensitive data. Have you ever heard of chit-key vaults and safe deposit boxes? Well, you might need to school yourself up on such secure storage options if you’d like to keep physical data breaches at bay.What about your prized server room? We recommend you invest in military-grade security, laser sensors, motion detectors, sentry guns, the Death Star, the Infinity Gauntlet; whatever works for you – just ensure you leave nothing to chance. Pardon all the movie references, but we all know what happens when hackers release nude pictures of female celebrities and media files that were meant to stay private. The fallout if often nasty and people lose face and jobs, but I digress.Cyber AttackCyberattack is one of the most prevalent forms of data breach since the attacker needn’t be physically present on your business premises to steal your data. All a cyber attacker needs is a computer with internet access and a couple of hacking tools to grab your data without your knowledge.Data breach by cyber-attacks can go on for months or even years without anyone noticing, especially if the hacker did his/her job well. Often, the intrusion is discovered when the damage has already been done, i.e., after the data breach has taken place.But how does a criminal hacker on the other side of the globe gain access to your system? The attackers rarely reinvent the wheel unless they have to. They use old hacking methods that are known to work. If they devise a new tactic, it’s mostly a combination of old tactics meant to exploit vulnerabilities in your system.Common mechanisms hackers use to break into your systems include malware, keyloggers, fictitious websites, trojans, backdoors, and viruses, among others. Usually, they trick users into clicking and as a result, install malicious programs on the system, which is how they mainly gain access to your data. Others will intercept the information you send and receive over an unsecured network in what is commonly known as the man-in-the-middle (MitM) attack.An attacker may dupe an unsuspecting staff member to steal login credentials. The attacker then uses the login credentials to login to the staffer’s computer, from where they launch a lateral attack on the rest of your system. Before long, the attacker has access to restricted areas of your network, and BAM – your data is gone, lost or rendered useless.With criminal cyberattacks making up over 48% of data breaches according to the Cost of Data Breach Study by IBM, how do you protect yourself from cybercriminals looking to harvest your data? Preventative measures to keep cyber attackers at bay include:Encourage staffers to use strong and unique passwords. Never use the same password for different accounts. If you can’t remember many different passwords, considering investing in a password manager such as LastPass and Cyclonis, among others. And please, never ever use “123456,” “password,” “admin” and such easy-to-guess passwordsInvest in a state of the art VPN to secure your network. A VPN encrypts your data such that it’s unreadable even if attackers manage to steal itRedesign your tech infrastructure with a security-first approach in mindEnable two-factor authentication to protect your servers and other storage devices containing sensitive dataUse an antivirus and firewallsUpdate your software to seal security holes and improve functionality. Best is to keep your software updated at all timesTo learn more about protecting your organization and yourself against cybercrime, here is a list of relevant posts for further study.6 Emerging Cyber Threats to Lookout for in 2019.How IoT has Exposed Business Organizations to Cyber Attacks.11 Ways to Help Protect Yourself Against Cyber Crime.Employee Negligence aka Human ErrorHave you ever sent out an email blast and be like “No, No, No, No, Nooo!” Yeah, most of us have been there, and it’s one of the worst feelings ever – especially if you send confidential or sensitive information to the wrong recipients. Or what happens when you send the wrong attachment to the right recipient? That photo you mean to send to your significant other?Both scenarios constitute data breach, and when it happens in an organization, it can cause unprecedented chaos and unrest. But perhaps the above examples don’t cut it for you, so here is a fun fact. Did you know networked backup incidents and misconfigured cloud servers caused by employee negligence exposed over 2 billion records in 2017? According to the 2018, IBM X-Force Threat Intelligence Index published on itweb.co.za.The point is to err is human; we all make mistakes, and it’s inevitable. But mistakes that could take your company off the pivot can’t be taken lightly or for granted. To mitigate this type of data breach, you must educate your employees on the essential elements of information security, and what will happen if they aren’t vigilant when performing their duties. It might sound like a weak point, but a little training could go a long way in combating data breach due to employee negligence.On top of that, educate non-technical staff members on data security awareness procedures and policies. At the end of the day, you should embrace a zero-tolerance policy to data breaches that result from employee negligence. Inform your employees on the importance of keeping data safe and the repercussions should the unthinkable happen.Insider ThreatWhile most organizations focus on mitigating external threat factors, insiders pose a more significant threat than you’d typically imagine. According to an Insider Threat study by CA Technologies and Cybersecurity Insiders, 53% of organizations faced insider attacks, with the main enabling factors being:Many users have excessive access privilegesAn increased number of devices with access to sensitive dataThe increasing complexity of information technologyFrom the same source, 90% of organizations feel vulnerable to insider attacks, and 86% of organization already have or are building insider threat programs. According to IBM Insider Threat Detection, insider threats account for 60% of cyber attacks. Wow, just wow – quite the staggering figure if I must point out the obvious, which also means you must be extra vigilant or one of your employees will drive a steel stake through the heart of your organization.Data breaches resulting from insider threats are quite common nowadays, and extremely difficult to detect. Network protectors can quickly combat malicious outsiders, but the job becomes harder when threats come from trusted and authorized users within the organization.The job becomes 10 times more challenging since there are different types of insider threats, namely:Disgruntled employees – This category of criminal insiders commit deliberate sabotage or steal intellectual property for monetary gain. It’s common for employees to steal information before and after quitting or being fired. Some harmful elements sell trade secrets to competitors, but others want to take down the enterprise.Nonresponders – Some employees never respond to security awareness training, no matter the resources you invest. These are the people who usually fall prey to phishing scams repeatedly because, well, you can stick your security awareness training up your (you know where).Insider collusion – Professional cybercriminals will go to great lengths to steal your data. They scout the dark web looking to recruit your employees. If one of your employees collaborates with a malicious attacker, you will have a severe security and data breach, and you don’t need a rocket scientist to tell you that. In some cases, an employee may even cooperate with another employee in the same organization, exposing you to all types of cybersecurity problems. If you need a little prodding in the right direction, just think how insider collusion can expose your enterprise to fraud, intellectual property theft, and plain old sabotage.Inadvertent insiders – Ignorance is not bliss as far as cybersecurity goes. Negligence on your employees part invites all manner of trouble since attackers are savvy to vulnerabilities that inadvertent insiders cause. Negligent staff members expose your organization to malware, phishing, and man-in-the-middle (MitM) attacks, among other forms of attack. Attackers may take advantage of negligence in your organization to exploit misconfigured servers, unsecured/unmonitored microsites, and so on.Persistent malicious insiders – Criminal “second streamers,” i.e., employees seeking supplemental income maliciously, won’t protect your data. Instead, they will commit a slew of malicious acts such as exfiltrating data for financial gains. And this category of people will remain undetected for long periods to maximize the benefits of data theft. And since they are aware of network monitoring tools, they will steal data slowly instead of committing data theft in bulk. As such, they can operate under the radar for months or years.How do you prevent data breach caused by insiders? How do you protect your data when the threat comes from the same people you trust. To protect your data from insider threat, you need to implement measures such as endpoint and mobile security, Data Loss Prevention (DLP), data encryption at rest, in motion and use as well as Identity and Access Management (IAM). You can even adopt behavioral analysis and reduce vulnerabilities. These measures will combat, among other things, unauthorized access, negligence, and data loss in case of a breach.RansomwareWhat comes to mind when you see the word RANSOMWARE? WannaCry? $700,000 of losses? Laws? The HIPAA perhaps? CryptoWall? CryptoLocker? Ransomware can constitute a data breach depending on the malware that attacks your systems. Other factors such as the type of data stolen, the current status of said data and – again – laws. Anybody who puts your data at risk of loss has committed data breach to some extent. If some hacker somewhere holds your data hostage, your organization will surely experience losses in all fronts. And you determinedly would instead carry on as usual – plus money doesn’t just grow on trees.The attacker who hijacks your data has demonstrated that they can steal or destroy your data at will.Clearly, they are talented, and ransomware comes in a million shades of nasty. Could take over your system right this minute considering there are more than 4,000 ransomware attacks per day according to the Federal Bureau of Investigation (FBI). It’s one of the reasons the US government has a $15 billion budget for cybersecurity. The majority of attackers use ransomware to cover their tracks. Just think about it for a minute. Some guy breaks into your system steals your data, and if that isn’t enough, holds your data hostage for profit as they cover a data breach.Ransomware ruins your reputation. It takes blood, sweat, and tears to build a name, so say “no” to ransomware.You can avoid ransomware of you’re cautious enough. Plus, you can always ramp up your defenses. And please install a powerful antivirus program (my favorite is Eset Nod32), and ensure you activate web file protection and firewalls to combat malware-laden emails and messages that pass spam filters. Additionally, invest in a clever backup plan so that you can simply wipe the drives to eliminate ransomware, and then restore backups. That way, you can beat ransomware attackers at their own game, instead of paying a ransom.Final WordsSecurity goes beyond mere awareness, so don’t take data breach sitting down. You can effectively protect yourself, and if the worst happens, rise from the ashes stronger than before.  Keep learning and implementing the best security policies and procedures to protect your business against the various forms of data and security breaches. Keep the conversation going until you have everything you need to safeguard yourself and your organization against all five of the data breach of types. Tags:#security breach Related Posts last_img read more

4 Mistakes of Machine Learning Startups

first_imgChina and America want the AI Prize Title: Who … MetricsAccuracy is an essential metric in machine learning. However, senseless seeking absolute accuracy can become a problem for an AI project.  Particularly, if the goal is to create a predictive recommendation system. It is obvious that the accuracy can reach an incredible 99% if the grocery online-supermarket offers to buy milk. I bet a buyer will take it, and the recommendation system will work.  But I’m afraid he would buy it anyway thus there is little sense in such a recommendation. In the case of a city resident, who buys milk daily, it is an individual approach and promotion of goods (which the one didn’t have in the basket earlier) that matters in such systems.ValidationA child learning the alphabet gradually masters letters, simple words, and idioms. He learns and processes information at a certain level. At the same time, the analysis of scientific papers is incomprehensible for the toddler, although the words in the articles consist of the same letters that he learned.The model of an AI project also learns from a specific data set.  However, the project won’t handle an attempt to check the quality of the model on the same data set.  To estimate the model, it is necessary to use specially selected for verification pieces of information that were not used in training. In such a way, one can achieve the most accurate model quality assessment.TechnologyThe choice of technology in an AI project is still a common mistake, leads if not to fatal, but serious consequences that influence the efficiency and time of the project deadline.No wonder, you can hardly find a more hyped theme in machine learning than neural networks, due to its suitable-to-any-task universal algorithm. But this tool won’t be the most effective and the fastest for any task.The brightest example is Kaggle competition. Neural networks do not always take the first place; on the contrary, random tree networks have more chances to win; it is primarily related to tabular data.Neurons are more often used to analyze visual information, voice, and more complex data.Using a neural network as a guide one can see, nowadays, it is the simplest solution. But at the same time, the project team should understand clearly what algorithms are suitable for a particular task.I truly believe machine learning hype won’t be false, exaggerated, and ungrounded. Machine learning is another engineering tool that makes our life simpler and more comfortable, gradually changing it for the better.For many massive projects, this article may be just a nostalgic retrospective about the mistakes they have already made but still managed to survive and overcome serious difficulties on the way to the product company.But for those who are just starting their AI venture, this is an opportunity to understand why it isn’t the best idea to take a selfie with a wounded bear and how not to fill up the endless lists of “dead” startups. AI Will Empower Leaders, Not Replace Them I am CBDO of EnCata Soft, a part of EnCata company. Take interest in technologies, autos, gadgets A Web Developer’s New Best Friend is the AI Wai… Alex Kurbatov Alex Kurbatovcenter_img Tags:##AI #machine learning #AI risks Related Posts AI: How it’s Impacting Surveillance Data Storage Have you heard of the Darwin Awards? Hop on YouTube and take a look. It’s generally pretty funny stuff. It’s a tongue-in-cheek honor that recognizes people for the most sophisticated attempts to do something they think is cool. One takes a selfie with a wounded bear, another one screws a jet engine to a skate. These bold actions lead to fatal mistakes with dire consequences and funny comments. Spoiler alert — sadly — they all die. You don’t want your startup “to die” from the mistakes of machine learning.For the past 25 years, I’ve seen thousands of times when a person makes errors — but never when a machine makes a mistake. Today, a blunder in the learning projects can cost companies millions and several years of useless work. For this reason, the most common errors in machine learning related to data, metrics, validation, and technology are collected here.artificial intelligenceData.Chances to make a mistake working with data are rather high. It is easier to successfully pass a minefield than not to make a mistake while working with the data set. Moreover, there can be several common mistakes:Unprocessed data. Unprocessed data is rubbish that will not allow you to be confident about the adequacy of the constructed model. Therefore, only pre-processed data should be the basis of any AI project.Anomalies. To check data on deviations and anomalies and get rid of them. Getting rid of errors is one of the priorities of every machine learning project. The data may always be incomplete, incorrect, or some information may be lost for some period.Lack of data. Perhaps, the easiest way is to conduct 10 experiments and get the result, but still not the most correct one. A small and unbalanced amount of data would drive to a conclusion far from the truth. So, if you need to train the network to distinguish spectacled penguins from spectacled bears, a couple of bears’ photos won’t fly. Even if there are thousands of penguins’ images.Lots of data. Sometimes limiting the amount of data is the only correct solution. That is how you can get, for example, the most objective picture of human actions in the future. Our world and the human race are incredibly unpredictable. As a rule, to foretell someone’s response based on their behavior in 1998 is like reading tea leaves. The result, being quite the same, will be far from reality.last_img read more

Validating Models: A Key Step on the Path to Artificial Intelligence

first_imgTo stay competitive in a digital economy, businesses increasingly need to move beyond simple reporting and descriptive analytics to a more predictive approach that puts artificial intelligence (AI) strategies to work to engage with customers in new ways.So how can you find a practical way to start applying AI in your business? One path forward follows three steps: leverage predictive models to improve how you engage with customers, put machine learning to work to improve those models, and then validate your models. In an earlier blog, I explored the dynamics of predictive analytics and machine learning. In this post, I will focus on the validation of predictive models First let me provide a quick overview of predictive analytics and machine learning, and explain why validation is important when you apply these approaches.Predictive analyticsPredictive analytics is about using algorithms to predict the result of a measurement that you can’t make, based on measurements that you can make. Why can’t you make the measurements you need? Perhaps you are trying to predict what’s going to happen next. Unless you have a time machine (in which case you are probably wasting your time on analytics!), you can’t measure something that hasn’t happened yet. Forecasting customer behavior, business trends and future events is always of value in running a business.Often, it is not possible to measure something in the present because of practical considerations. For example, suppose you want to present a lunch ordering application for your chain of lunchtime food delivery restaurants. Your best chance of getting someone’s attention is when they are hungry and online. Obviously, it’s not possible to ask everyone who is online whether they are hungry, so you need to infer their hunger status from their behavior: Is it lunchtime? Are they looking up lunch options? The goal of your predictive analytics in this case is to infer who is most likely to respond to your product or offer, based on the data you are able to collect.In general, your predictive analytics application can take into account a customer’s past account history, past conversations with the call center, behavior of “similar” customers, the location of the customer, and even what’s trending on social media at any given time. Good predictive analytics will give you the best chance of a mutually beneficial interaction with your customer.The challenge is that, compared with diagnostic and descriptive analytics, predictive analytics is a new world. You are actually making predictions or inferences based on past data. To be successful at this, and to avoid making grossly inaccurate predictions (or at least understand how accurate your predictions may be), you will need to validate your models to ensure that you have discovered useful, generalizable patterns in your data.Machine learning as part of a predictive analytics systemSo how do we build a good predictive analytics application? Two words: Machine learning (ML). Predictive analytics leverages machine learning algorithms that build systems that learn iteratively from data, identify patterns, and predict future results. Machine learning algorithms organize things into meaningful groups, find unusual patterns in data, and can predict the next data point in a time series.There’s an important caveat to call out here. Machine Learning algorithms learn from data, but on their own they are not great at distinguishing between memorizing past data and finding generalizable underlying patterns in data. When learning from past data for predictive analytics the goal is to generalize, not memorize. Poorly constructed ML algorithms can memorize all of the data in a huge data set, resulting in a system that is very poor at predicting the outcome of any situation they haven’t already seen in the past. Instead, you need to train ML algorithms to focus on a limited number of free parameters that enable reliable predictions about the future. ML algorithms that generalize well are called “robust” algorithms.The Importance of ValidationGiven the concerns described above, how do you know if you can trust the results generated by a ML algorithm? You need to validate your predictive models.Validation is the automated process of looking at all the data you have in different ways to determine how robust your predictive models are likely to be. To understand why validation is important, let’s look at the process of creating predictive analytics from data using machine learning models. This will help us see why not all models are created equal.One way to proceed would be to create a model that you think is predictive, try it out for a while, and then see if it actually works. But there’s a big downside here. If the model doesn’t work, you could end up paying a big price in terms of lost opportunities and misguided business strategies. In practice, you will monitor the performance of your model in the “real world,” but you don’t want to only rely on that approach alone.A better way to validate is to split your available data into a training set and a test set. You will use the training set to train your ML algorithm to predict the known outcomes in your training data. You then try out the model on the data in the test set to see how well your predictions match the actual outcomes in the test set.This gives you a number of quantitative performance measures for your ML model. If your model has poor performance on the test set, then you know that the ML model you came up with did not discover a generalizable pattern in your data and you will need to change your assumptions and build another model.Going forward, you can tweak your model to try different training and test set data partitions and repeat the validation sequence multiple times—define your training set, train your algorithm, test your algorithm. Most ML libraries, like Intel® Data Analytics Acceleration LibraryOpens in a new window (Intel® DAAL) can automate this process for you using a variety of methods called “cross validation.” Cross validation enhances the likelihood that your algorithm will be reliable and robust.Tools for the Data ScientistIntel offers various software technologies and hardware products to support the efforts of data scientists and application developers who want to use ML methodologies to extract value from huge datasets. Let’s take a few examples.Intel spearheaded the initiative to create the open source Trusted Analytics Platform. TAP accelerates the creation of advanced analytics and machine learning solutions by streamlining the process of assembling big data tools and by automating the many steps required to build and publish big data applications. It’s a key platform for putting powerful analytics into the hands of every business decision maker. For a fuller explanation, see my earlier post discussing three attributes of TAP.Intel also offers access to a range of open-source frameworks for machine and deep learning, as well as code and reference architectures for distributed training and scoring. You can explore these resources on our Machine Learning site.And then there is also the hardware side of the story. The Intel® Xeon® processor E7 v4 family delivers the processing performance and large memory capacities required for real-time analytics on huge datasets. It’s ready for the largest high-volume workloads, like those in healthcare, energy, financial trading, and logistics applications.Intel non-volatile memory (NVM), including Intel® Optane™ technology, speeds things up even more. It can greatly accelerate machine learning systems by reducing memory latency to a matter of just tens of nanoseconds. These are the kind of breakthroughs you can achieve with new 3D XPoint™ technology, which brings non-volatile memory speeds up to 1,000 times faster than NAND, the most popular non-volatile memory in the marketplace today.The main takeaway for a data scientist to achieve success in predictive analytics, if you’re using a predictive model, you want to be confident that it is both reliable and robust. The process of validating your ML model gives you this confidence.last_img read more