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5 Ways To Accurately Process Data at Scale in Your Data Center

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5 Ways To Accurately Process Data at Scale in Your Data Center


In modern times, organizations recognize efficient and omnipresent data centers’ role in operations and industry growth.

They now depend more and more on employing data to find new models and insights and sharing that information to sustain sound business choices and problem-solving. In addition, as data consumption continues to grow, IT teams must ensure that their data centers are always available and scalable.

If not, the outcomes of not owning data centers with adequate measures or storage resources can be overwhelming, starting with performance concerns, followed by error reports, and then having business applications shut down and lock people out. However, the good news is that businesses using cloud computing technology or cloud data centers will find it more manageable to maintain scalability, be it subtracting or adding storage or compute resources. 

Understanding the Role of On-Premise Data Center

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Successfully handling data center scalability begins with strategic expansion and cooperation between a company and IT administrators. Also, IT leaders need to comprehend business requirements to predict the future capacity requirements of the data center and increase scalability

In addition, seeking the benefit of all that data to find beneficial ways to use in decision making and enhancing other departments’ access to information is just the beginning of the list. With overall data quantities on the rise, companies must ensure they remain ready to deliver, and data center scalability becomes crucial to yielding the most profitable outcomes.

How to Accurately Process Data at Scale in the Data Center?

Big Data Maze

Exceptional data center infrastructure administration remains a catalyst for tighter integration and innovation of enterprise functions. The everyday, real-time functional benefits are seemingly limitless with more workplace mobility and higher availability and scalability of workloads. 

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It further includes better collaboration facilities for staff, expedited recovery from safety incidents, and lower TCO (total cost of ownership) due to optimally using hardware, software, and virtual resources. Mentioned hereunder are some of the top ways to accurately process data at scale in a data center.

#1. Remain on the Edge 

Edge computing has evolved as the next significant expansion in data administration and information architecture, and it extends organizations exceptional opportunities to handle and compute data efficiently. Also, since it includes tallying data close to the data source rather than crowding the core cloud center, it makes it more manageable for a company– especially a large one – to spread out its information load and scale quickly. Also, the outcome is a system better qualified to do more with almost an identical amount of investment.

#2. Leverage the Advantages of SDN 

SDN (Software-defined networking) is another exceptional technology facilitating data center scalability. Also, using software-defined networking in a data center enables a company to smartly distribute its data storage requirements as SDN users can access information from many more points on the interface. 

In addition, this implies that instead of one gigantic, costly data center, organizational IT teams can spread the load over a more effortless space of smaller data centers.

#3. Bulk Up Your Entire Interface 

Data centers are beneficial, but for organisations driving to edge computing, they will soon discover that there will be an increased demand on endpoint devices and a need for a new class of machines, such as gateways, edge servers, and other tools. 

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Also, while edge computing decreases the demand for one giant data center, it puts a whole new demand on numerous smaller centers.

#4. Elastic Capacity

A typical pitfall while data center scaling is server acquisition, as expansion in demand remains challenging to predict. Another difficulty is deciding what to do when you do not require the extra capacity. That’s where the concept of elastic capacity comes into the picture. Data centers can now remove, add or move virtual processors and memory during times of spiked use or supervision. 

Instead of spending on additional capacity, you only have to spend for the servers required. Moreover, data center operators can manually modify the assistance levels for an application as frequently as they want, then employ those service levels for automation.

#5. Change Management with Hybrid Cloud

Data center operations are more complicated than ever. Applications and workflows are being stopped, added, modified, or repurposed at any given point according to growing business requirements. 

IT administrators need to determine and control the workflows with the potential to cause a transformation in the data center’s virtual and physical assets and then manage infrastructure modifications accordingly while keeping efficiencies. 

Final Words 

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In the long run, a data center offers a cost-effective solution that lets any business spend more time contemplating how to employ their IT infrastructure to drive their business than to execute the technology assets they need to thrive. 

Also, partnering with a suitable data center provides businesses with the flexibility to adjust to varying needs and maximize their existing resources to contend in a crowded industry.

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3 Best Practices For Predictive Data Modeling

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3 Best Practices For Predictive Data Modeling


Predictive modeling is used to develop models that use past occurrences as reference points for organizations to forecast future business-related events and make clever decisions.

It is heavily involved in the strategy-making processes of companies in industries such as healthcare, law enforcement, pharmaceuticals and many more. The practices that can be used to make predictive data modeling error-free can be of great importance to everybody.

Predictive data modeling involves the creation, testing and validation of data models that will be used for predictive analysis in businesses. The lifecycle management of such models is a part of predictive data modeling. Such models, which use data captured by AI systems, machine learning tools, and other sources, can be used in advanced predictive analysis software systems used by organizations. The predictive data modeling process can be broken down into four steps:

  • Developing a model

  • Testing the model

  • Validating the model

  • Evaluating the model

There are a significant number of application areas for predictive analysis, such as financial risk management, international trade, clinical trials, cancer detection and many others. As we can see, each application area specified above is sensitive to mistakes or prediction inaccuracies. An inaccurate prediction could lead to incorrect diagnoses, potential patient deaths or financial turmoil in such industries. Therefore, organizations must implement certain practices to optimize the process of predictive data modeling. They must also continuously monitor the performance of the models.

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1)  Keeping the First Model Simple

As a process, predictive data modeling uses plenty of resources before organizations can expect it to bear fruit for them. Therefore, the competence of IT infrastructure present in the organization to carry out predictive data modeling is vital for streamlining the process without lag or inefficiencies. Accordingly, businesses must invest time and money to firstly make sure that their IT infrastructure is able to handle the process. This can be made sure with actions such as checking network connectivity, checking internet speeds, cybersecurity-related elements, and other factors before your business can use predictive data modeling. Additionally, your business needs to make sure that all your IT tools are aligned perfectly to make the model development process smoother.

More importantly, the first model created by an organization need not be overly complex or fancy. The first model will not be used for hardcore endpoint applications. A simplistic model provides the metrics and behaviors that can be used as a yardstick to test bigger and more complicated data models in the future. During the initial phases, businesses need to answer a few queries related to carrying out the predictive data modeling process. Some of such questions are related to the number of features needed to test a specific hypotheses, whether features that are useful are practical to make for the future, where they can store a model for maximum data security and threat protection, and finally, whether every significant decision-maker believes that the architecture and tools present currently in the organization are good enough to carry out the process.

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Having an advanced hardware and software infrastructure conducive to predictive data modeling is vital for the process to be a success. Maintaining the simplicity of the first data model is valuable to train other, more complex models easily in the future.

2)  Validating Models Consistently

Result validation involves organizations running their model and evaluating its results with visualization tools. To carry out the validation process, organizations need to understand how business data is generated, and how it flows through organizational data networks. As we know, today, data analytics is highly integrated into nearly every business aspect. Individuals at every level in an organization use company resources and the web to make calculated business decisions. Information is gathered for predictive model training purposes too. Accordingly, getting the datasets that can be used to train predictive models also requires a lot of effort. The level of effort involved in data collection means that predictive models are quite highly valued, and each model may have the power to influence organizational data compliance (in a good or a bad way), financial bottom lines, as well as the creation of legal risks for the organization. As a result, such high-value assets need to be validated consistently.

Additionally, businesses may be under the impression that model validation is a one-off process and does not need to be carried out in the future. However, as an expert in the field of model training will tell you, that is a misconception. Predictive models need to constantly evolve with time to become more adept at making accurate forecasts, and so, the validation process needs to take place on a consistent basis. Here are some of the tasks that must certainly be carried out in the validation process:

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The Thorough Validation of ‘Predictor’ Variables

A model is made up of several variables. Some of those variables may have strong predictive abilities. Such variables are labeled ‘predictors’ due to those capabilities. While predictors are useful for regular business work, they may, in some cases, also cause unwanted risk exposure for their organization when they are used for predictive analysis. For example, the absence of ultra-personal details of users in models may be a conscious effort taken by network administrators to not fall into legal troubles regarding privacy violations of users.

The Validation of Data Distribution

This type of validation is carried out by organizations to get an understanding of the distribution of predictor and target variables. Over time, there may be distribution shifts in such variables and models. If such shifts are detected in variables within data models, such models will have to be retrained with new data as they wouldn’t be able to provide predictive analysis with accuracy.

The Validation of Algorithms

As we know, analytical algorithms are used to train models. Validation must be done for algorithms that train models, which go on to carry out predictive analysis in businesses. Also, only certain types of models can provide clear, interpretable predictions. For example, there are multiple types of models, such as decision trees and neural networks. Decision trees provide more open and interpretable—albeit less accurate—results whereas neural networks do not—with more accurate results. So, decision trees must be validated more frequently as they participate more in predictive analysis. Data administrators need to choose between interpretability and prediction accuracy when carrying out the validation of such algorithms.

Compare Model-Prediction Accuracy Tests

To know the actual competence of a model, it must be compared with other models for accuracy. The most accurate models must be used in predictive analysis systems. This is also a validation task and must be carried out regularly if newer, more accurate models enter the fray with time. After all, the improvements in predictive analysis performance carry on perpetually.

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Additionally, tasks such as auditing of models, and keeping track of every validation log entry are included under the umbrella of validation. Finally, the performance of models is monitored before and after deployment. Before deployment, businesses must test them for operational glitches that may impact their decision-making and predictive capabilities. Pre-deployment checking is essential because most models chosen for predictive analytics are used in real-world environments.

After a model is deployed, it needs to be monitored for wear, as, generally, models tend to degrade over time. So, validation helps with phasing such models out from a predictive analytics system and replacing them with new, useful ones. With constant validation, models could become less error-prone and more time-efficient. Constant validation is a potent practice as it improves the predictive data modeling process in several ways.

3) Recognizing Data Imbalances

Imbalanced data is a classification issue where the number of observations per class is not equally distributed. As a result, there may be a higher number of observations for a given class—known as a majority class—and much fewer observations for one or other classes—known as minority classes. Data imbalances cause inaccuracies in predictive analysis.

A data imbalance in a model can cause it to be erratic, and not very useful. For example, let’s talk about a fraud forecasting system proposed for a bank. Now, the bank may have a record of 95%— meaning that 95% of its transactions turn out to be non-fraudulent. In an imbalanced system, a system may state that the bank is 100% safe. Now, while the system may be right, and the bank will face fraud only 5% of the time, the forecast system will be in trouble whenever any fraud takes place because the system had clearly stated that the safety quotient was at a 100%.

Predictive data modeling is a tough task in the current digital world due to certain potential weaknesses that may creep into its functioning. By following the best practices, businesses can be sure of avoiding poor forecasting.



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To Leverage Deep Learning, You Must Know This First!

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To Leverage Deep Learning, You Must Know This First!


Before implementing deep learning for business, it is vital that business leaders understand the capabilities and features of this path-breaking technology.

What is Deep Learning?

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Deep learning
is a type of machine learning in AI that gathers huge datasets to make machines act like humans. Due to the use of neural networks, deep learning produces optimized results. You must have observed how Facebook automatically finds your friend in an image and suggests you tag her. Here, Facebook uses deep learning to recognize your friend. We were amazed to read what Gartner had to predict for about deep learning. It said, “Deep learning will soon provide best-in-class performance for demand, fraud and failure predictions.” Such a prediction encourages business leaders to implement deep learning for business and drive their business to greater success. While most business leaders are aware of the term, deep learning, they have very little to no understanding of the technology. Before leveraging deep learning for business, leaders should take a look at what deep learning offers and what the future of deep learning will look like.

Deep Learning for Business

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For any business, the ultimate goal is to make profits. Organizations can make profits only if customers buy their products or service. Hence, every company aims at keeping their customers happy and fulfilling their demands. In any business, leaders must ask questions like:

  • What are the challenges faced with the current model?

  • How can deep learning help overcome the challenges?

  • How will the technology help to keep customers happy and attract new customers?

  • What are the areas in their business where they can implement deep learning to make higher gains?

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Shared below are few deep learning applications that every business leader must leverage:

  • Image recognition – Deep learning algorithm recognizes various elements that are located in an image. One of the most common examples of image recognition is Google Images. Based on the content that we are searching for, Google offers us a set of relevant images. Another example is the self-driving vehicles that use image recognition to recognize obstacle son road and act in time. Also, the healthcare industry is using image recognition to understand the human anatomy better.

  • Sequence learning – Using predictive analysis, business leaders can predict the future outcomes in their business. An example is the product recommendation, when we shop online.

  • Machine translation – Google’s translate engine helps translate the language you enter to any other language of your choice.

The Future of Deep Learning

The future of deep learning is bright. Soon we will see self-driving vehicles becoming a part of our daily lives, thereby reducing accidents and air pollution. Amazon is already using drones for delivering products in 30 minutes or less. There are enormous opportunities for various industries to benefit with the deep learning technology. Business leaders should find these hidden opportunities and drive their business ahead of the curve.

Now that you have a comprehensive idea about the capabilities of deep learning, you should start planning to implement deep learning in your business.



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5 Things You Didn’t Know About Artificial Intelligence

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5 Things You Didn’t Know About Artificial Intelligence


Artificial intelligence (AI) has been a hot topic for a while now.

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Source: NVIDIA

While it might appear to be a fairly new concept, artificial intelligence has actually been around since the first computer was built back in the 1930s.

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Source: Decan Herald

Based on capabilities, artificial intelligence can be classified into three types:

  • Narrow AI
  • General AI
  • Super AI

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Source: McKinsey&Company

The concept of AI involves other subsets such as machine learning and deep learning.

Computers could learn and act on data sets without any human programming. Essentially, AI is mimicking the human brain, but it will soon surpass our mind. AI learns and adapts to information and scenarios, and it gets smarter in the process. Over time, it can begin to react differently to achieve better results.

While AI has been around for quite some time now, there are still several things that many people don’t know about it. Let’s take a closer look. Here are 5 things you didn’t know about artificial intelligence.

1. Artificial Intelligence is Already Present In Our Everyday Lives

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Without even realizing it, you’re likely interacting with artificial intelligence every day. If you’ve searched for information on your smartphone, asked your digital assistant for the weather forecast, or plugged in directions in your car’s navigation system, you’ve used machine learning, which is a subset of AI. It might not seem all that significant, but these little things help us live our daily lives.

2. Artificial Intelligence Can Assist with Weather Forecasting

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Artificial intelligence has the ability to take in massive volumes of data and uncover patterns that would take humans days, weeks, or even months. In such a capacity, it can assist weather forecasting. It can predict severe weather conditions, such as hail or hurricanes, that can help utility companies make smarter decisions. It can also be beneficial for solar power system companies. In recognizing specific patterns, it can also predict weather patterns that might impact solar production. This can be helpful for power producers, enabling them to adjust accordingly.

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3. Artificial Intelligence Can Restore Vintage and Damaged Photographs

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Vintage photographs provide us with a glimpse into how things used to be in the past. The thing is, pictures don’t last forever. Old photos fade or get damaged. Since these images were created before computers were really a thing, they’re likely to disappear forever once they’re gone.

Thanks to artificial intelligence, we can save vintage and damaged pictures. NVIDIA developed an AI that uses what’s known as image painting to successfully restore images with rips, holes, or missing spots. After being fed thousands of images, the AI can analyze any picture and automatically fill in the missing areas, effectively repairing them.

4. Artificial Intelligence Can Help Spot “Deepfakes”

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Deepfakes are AI-generated videos that are becoming increasingly more common and realistic-looking. These images take faces of people and put them on the bodies of others. They can also create some rather convincing audio. Well-done videos and pictures can make it appear as though someone said or did something they didn’t actually do.

Not only can artificial intelligence make deepfakes, but it can help to spot them, too. As convincing as many deepfakes are, there are some tell-tale signs that they’re not real. The voice might sound a bit too robotic, or there’s something a little off with the image (such as hair not quite matching up). AI can help to spot these inconsistencies, which can protect people. In some cases, it may even help to prove a person’s innocence.

5. Artificial Intelligence is Helping Medical Professionals Fight Cancer

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Computers are an integral tool in the field of medicine. One way artificial intelligence is helping is by assisting medical professionals in the fight against cancer. AIs developed by the University of Surrey and the University of California were fed thousands of accounts of people affected by cancer. Now, they can predict which people are likely to develop cancer. The technology may also be able to detect symptoms of cancer in its earliest stages.

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Conclusion

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Source: Deloitte

These are just a few of the incredible things artificial intelligence can do. AI is smart, and it’s getting better. It’s beating humans at their own games, such as solving Rubix cube puzzles and topping the high score charts in video games. While it can be a strange thought that machines are becoming smarter than humans, AI can go a long way in helping us achieve things we never thought possible.



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