While the Internet originally started as a connection between individual computers online sharing data with one another, times have changed. Now, much of the data that makes up the Internet is hosted on platforms known as data centers. Sharing unfathomable amounts of information every second, data centers have become a critical component of the world wide web.

The big drawback when it comes to data centers is that creating a new one comes with many challenges and difficulties. Fortunately, the advent of new data center technology and more sophisticated design choices are making these challenges easier to overcome. Let’s take a look at some of the main challenges of opening a data center, and the data center solutions that are making life easier for big businesses and consumers alike.

What is a Data Center?

A data center is a structure built to handle the processing and handling of large amounts of data on a 24/7 basis. In addition to this, data centers also store and distribute information as well. Currently, there are between half a million to a million data centers in the world depending on how the term is defined. To ensure uninterrupted service, data centers typically have high-level security devices, backup power supplies, and environmental controls.

Data Center Variants

There are a variety of different data center classifications, but in the end, it tends to come down to three main types: exclusive, managed hosting, and colocation. Here is a brief recap of how they work and what makes them different:

Colocation Data Centers

This data center variant is designed to serve multiple business clients by renting out space that they fill with their own IT equipment. If a client needs more space they can either rent more or expand to other data centers.

Exclusive Data Centers

Exclusive data centers are typically owned by a single big business entity such as Google or Facebook. These data centers are operated and managed exclusively by their owner to serve their specific business needs.

Managed Hosting Providers

A managed hosting provider is a data center in which users don’t rent actual space, but rather the IT equipment that resides within the data center itself. These data centers and the equipment within are owned, operated, and managed by a third party.

The Availability Problem

While all data centers try to provide as much uptime for their users as possible, things will inevitably go wrong. This means that businesses or individuals that need greater availability of service will have to choose data centers that have systems in place to guarantee the availability of service. Systems such as redundancy facilities, mirrored sites, and disaster contingencies are all used to maximize service uptime. Sometimes a business that needs to ensure 100% uptime will use multiple data centers just to avoid putting all its eggs in one basket should a catastrophic failure occur at its primary data center. All businesses, regardless of size and industry, need an effective data center risk management plan to maximize availability and security at all times.

Getting LEED Certified

To encourage environmentally sustainable buildings, the United States Green Building Council created the Leadership in Energy and Environmental Design rating system. Achieving a high certification level with LEED is important for data centers because they use very high amounts of electricity and water. The good news is that advances in green technology are helping cut operation costs while becoming more environmentally friendly.

Challenges When Opening or Choosing a Data Center

Opening a data center comes with many challenges, most of which overlap with the factors that need to be taken into account when a business chooses a data center to meet its needs. Let’s go over these challenges and see how they’re being overcome with new technology and smart design decisions.

Weather and Geographical Stability

Every location carries its own environmental risks when it comes to opening a data center. This includes factors like seismic activity and extreme weather. To overcome these issues, data center construction has to balance cost vs risk when choosing a location. In some instances, such as the Bahnhof Data Center in Sweden which is buried under a mountain, extreme measures have been taken to ensure continued service.

Climate Patterns

Since the equipment used within data centers can only operate within a very specific temperature range, the local climate is a big factor when choosing a location for a data center. However, data centers can still be built in very hot or very cold areas. The drawback is that this can increase power costs by as much as 50% to keep the center’s equipment within the desired temperature range.

Power Supply

Power is typically the biggest cost when it comes to data center operation. Large amounts of electricity is needed to keep the temperature of the center stable and to power failure tolerance and redundancy systems. In some cases, an uninterruptable power supply will be required to guarantee continuous service. This can include the use of backup generators or private power grids if necessary.

Distance to Businesses and Customers

The physical distance between a data center and the people using it can have a large effect on the speed and latency of data transmission. Generally speaking, the closer the data center is to those using it, the better the quality of service. For businesses that own their own data center, having it built closer to their area of operation can make it easier to manage and supervise the center.

Overcoming Data Center Challenges

As you can see, opening a data center is a very “risk vs cost” endeavor. However, modern data center solutions are making it easier to build new data centers and for exiting data centers to provide uninterrupted service. Going forward both the risks and costs of opening a data center should continue to decrease as technology continues to advance.

Python – Often a beginner in machine learning gets lost in the sea of available resources to master this subject. There are so many feely available Python machine learning resources as well a plethora of paid resources in both offline as well as online mode. In this blog, we’ll try to help a newcomer in machine learning to chart out a plan to master this subject

While learning any new thing, the first step is often the hardest to take, and things get more complicated when we are offered too much choice in terms of direction. So let’s help a newcomer having minimal knowledge of machine learning in Python or to understand a python tutorial to become a knowledgeable practitioner in just 7 steps entirely. To make matters more clear, I assume that you are not an expert in either Python or Machine learning and just have some knowledge of Python’s popular machine learning, scientific computing, or data analysis libraries.

Here are the seven steps to master ML using Python.

Step 1🡺 Learn and master basic Python skills

Step 2 🡺 Build a strong foundation in Machine Learning Skills

Step 3 🡺 Develop a working knowledge of popular Scientific Python Packages

Step 4 🡺 Start developing basic ML problems in Python

Step 5 🡺 Explore intermediate ML topics

Step 6 🡺 Start coding advanced ML problems

Step 7 🡺Begin learning Deep learning problems and start coding them in Python

Let us deep dive into each of these steps.

The first step to master Machine learning using Python is to know Python in and out. You have to form a strong foundation of programming in general and Python in particular. Python is very popular both as a general purpose programming language, as well as a specialized programming language for both scientific computing and machine learning. The internet is full of Python beginner’s tutorials. You must choose a course as per your level of experience in both Python and generic programming.

The first baby step is to have a python installation ready. For that, you need to install Python. The two best IDEs for Python for scientific or machine learning are PyCharm and Anaconda. I would recommend using Anaconda since it is not only well supported and documented, it has a very easy to use GUI and offers a plethora of editors to do coding. It is used in industry for Python development on Linux, OSX, and Windows, complete with the required packages for machine learning, including numpy, scikit-learn, and matplotlib. Anaconda also contains an iPython Notebook that offers an interactive environment to code and see the display in real time. I would suggest moving to Python 3 since Python 2.7 has now gone obsolete. You must always refer to Python official documentation for any doubt.

The second step is to build a strong base in Machine learning. You must brush up your knowledge on probability, statistics, set theory and mathematics to understand Machine learning since all machine learning algorithms use these 4 branches of science. You do not need to gain an intimate understanding of machine learning algorithms but should have a working knowledge of how an ML algorithm works. You do not need to master all theoretic aspects of each and every ML algorithm but should have basic to intermediate knowledge of how the algorithm works.

The third step is to develop a working knowledge of popular Scientific Python Packages. Once you are done with step 1 and step 2, you must be fairly confident on coding any problem using Python and can also give a shot on providing its theoretical explanation. Now, it is the time to move to actual coding for machine learning problems. Since ML is a specialized and complex topic, there are a number of open source libraries generally used to facilitate practical machine learning. Some of the popular libraries that are heavily used in ML are as follows:

1. numpy – NumPy is the de-facto python library when it comes to large multi-dimensional arrays and matrix processing. It has a built -in collection of high-level mathematical functions to work on matrix, vectors and scalars. It is hugely used for fundamental scientific computations in Machine Learning. Apart from that, it is also useful for linear algebra, Fourier transform, and random number capabilities.
2. pandas – Pandas is a popular Python library for data analysis, data extraction and preparation. Pandas library provides many inbuilt methods for data grouping, data subset combining and data filtering.
3. Scipy – SciPy is a popular library that contains different modules for optimization, linear algebra, integration and statistics.
4. scikit-learn – Scikit-learn is the most popular ML library to implement classical ML algorithms such as random forest, naive bayes, decision trees etc. It internally uses NumPy and SciPy. Scikit-learn has inbuilt support for most of the supervised and unsupervised learning algorithms. Scikit-learn is also handy for doing data-mining and data-analysis.
5. matplotlib – Matplotlib is a very popular Python library for data visualization. It is basically a 2D plotting library. It contains a module named pyplot that makes it easy for programmers for plotting as it provides features to control line styles, font properties, formatting axes, etc.
6. Theano – Theano is a popular python library that is basically an advanced mathematics processing library. It is used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays in an efficient manner. It does so by optimizing the utilization of CPU and GPU (if available).
7. TensorFlow – TensorFlow is another highly advanced open-source library to perform complex numerical computation. It was developed by the Google Brain team. It can train and run deep neural networks that can be used to develop several AI applications. TensorFlow is widely used in the field of deep learning research and application.
8. Keras- It is a very popular Machine Learning library for Python that has a high-level neural networks API which runs on top of TensorFlow, CNTK, or Theano. It can run seamlessly on both CPU and GPU. Keras makes it really for ML beginners to build and design a Neural Network and go for easy and fast prototyping.
9. PyTorch – It is a popular open-source Machine Learning library for Python based on Torch (Torch is C based open-source Machine Learning library). It has an extensive choice of tools and libraries that support Computer Vision, Natural Language Processing (NLP) and many more ML programs.

Also check out some of the other Machine learning libraries and tools in the below image

The fourth step is to work on basic ML problems in Python. Now is the time when you can start implementing machine learning algorithms with Python’s de facto standard machine learning library, scikit-learn. You will find out that most of the ML tutorials and exercises are done on iPython (Jupyter) Notebook, which is an interactive environment for executing Python. The benefit of iPython notebooks is that they can be viewed online or downloaded and interacted with locally on your own computer. You can begin by going through important functions of each ML algorithm in scikit-learn libraries and then take up each class of ML problems beginning with simple linear regression. You can solve each problem on a medium dataset using functions in scikit-learn. In the process, you will learn how to preprocess a data set, split a data set, create a ML pipeline, train the model, display the metrics and finally predict using the model on unseen values.

The fifth step is to focus on intermediate level machine learning topics and start coding them in Python. Post step 4; you have built your foundation in ML. It is time to move on to the next level. Now, you can proceed towards some more in-depth explorations of the intermediate problems involving advanced algorithms. You can start with k-means clustering, one of the most popular unsupervised machine learning algorithms that is used in clustering. You can then focus on the k-nearest neighbor’s algorithm. Post that, you can switch to classification using advanced classification methods such as decision trees and  logistic regression.

The penultimate step is to move towards advanced machine learning topics and start coding them in Python. Now it is the time for some advanced stuff. You can study support vector machines and solve classification problems on intermediate to large data sets. SVM is both a linear and non-linear classifier that does classification by doing complex transformations of data into higher dimensional space. In the process, you would also learn about various types of Kernel deployed, hyper parameters and their tuning while studying SVM. Post SVM, you can choose random forests which are ensemble classifiers.

Then you can move towards PCA or Principal Component Analysis, which is a particular form of unsupervised dimensionality reduction. Dimensionality reduction is a method for reducing the number of variables in a problem domain.

Towards the end, you will realize that you have not only gone through most of the ML algorithms ranging from basic complexity to advanced complexity. And you have also examined some additional machine learning support tasks such as dimensionality reduction, parameter tuning and model validation techniques. Now, you have a useful toolkit for yourself.

The last but the least step is to start learning about Deep Networks and coding bigger more complex problems using Deep neural networks in Python. Deep learning builds on advanced neural network research of several decades. To begin with, you can explore Theano and Caffe , two leading contemporary Python deep learning libraries. These have several inbuilt examples and tutorials on constructing simple to complex deep neural networks. Once you have mastered the basics, you can move towards real world multi disciplinary problems such as building a content moderation system.

If you follow this action plan, you can confidently take on real work problems on Machine Learning problems and successfully solve them. Best of luck!

The field of content marketing and sales operations encompasses a wide range of concepts and terminology. Having a solid grasp of industry jargon is essential if you want to be taken seriously as an expert in your field of expertise.

The terms “leads” and “demands” are sometimes used interchangeably, which is a problem because they represent two completely different concepts. Any B2B sales executive or sales professional will be familiar with these two terms.

Consider your goals to determine which of these two strategies is most suited to your marketing objectives. Though they differ, this does not mean that they cannot work together.

What is demand generation?

Demand generation is the process of generating interest in your products or services. There are various factors that can prohibit a requirement from being turned into a purchase, including pricing, a lack of organizational preparation – such as inadequate resources, training, or infrastructure – a lack of executive buy-in, and a host of other considerations.

On the other hand, this demand is a critical component of the ultimate goal of demand generation strategies b2b, which is to produce net new or direct or cross-sell opportunities.

From generating initial prospective interest and leads to nurturing and real success to the first sale and cross-selling, demand generation is critical at every stage of the marketing and sales cycle.

Indeed, demand creation is critical to giving the comprehensive education in the Inbound Marketing Process that is required to build long-term, profitable client relationships.

Suppose you are a B2B sales professional who is new to the concept of lead generation. In that case, it is common for you to experience a transition in your thinking and actions from lead generation to lead generation and revenue generation.

What is lead generation?

Creating qualified or exciting leads for our product or service is a subtype of demand generation; it is the development of leads who are eligible or genuinely interested in our product or service.

Collection of information about specific persons that can be utilized to convert them into sales-ready contacts, pipeline opportunities, and finally, customers is referred to as prospecting.

B2B lead generation programs have a more specific purpose than generic demand programs: they seek to convert focused audiences into a large number of high-quality leads as quickly as possible.

In other words, lead generation is concerned with the performance of the upper funnel, whereas demand generation is concerned with the performance of the entire funnel.

The difference

Creating leads is merely one aspect of the demand creation process. The process of lead creation, on the other hand, is not required as part of the demand generation process.

Demand generation refers to the process of attracting new clients to your products and services through word of mouth. On the other hand, lead generation is the process by which you pick people to convert them into leads and prepare them for the next step of your marketing plan, with the ultimate goal of generating revenue for your company.

There are numerous distinctions between the two groups. Different sorts of content are used in the demand creation process in order to attract buyers to your items. Demand generation creates an issue and then gives a solution within the content itself, which is usually in the form of a video, post, essay, or guide.

Lead generation, on the other hand, frequently employs a different methodology. Lead generation is accomplished through the use of white papers, checklists, and PDFs that are distributed on a regular basis through mailing lists. There are several advantages to using a personal approach, including generating more leads and converting your potential clients into actual customers.

Lead generation is the process through which marketing professionals nurture and grow the existing relationship between the customer and the brand, thereby increasing awareness, loyalty, and the creation of new leads. Demand generation involves both this initial step as well as other techniques that encourage customers to return to your organization.

Both are practical methods that top marketing executives utilize worldwide to increase their effectiveness.

Can there be lead generation without demand generation?

It’s common for inbound marketers and B2B marketing teams to employ lead generation strategies without a demand creation mindset.

Some of these teams have had remarkable success and have been able to routinely surpass their revenue targets by using tactics such as optimizing landing pages, A/B testing form fields, and creating unique, high-quality lead content for their prospects.

However, without the top-down approach used by demand generation methods, B2B marketing teams would be unable to give the kind of support required to ensure that those leads are converted into real value for the company in the form of customers, revenue, and profit.

Demand generation strategies involving lead generation approaches are likely to yield the best outcomes for most B2B sales businesses.

E-Invoices Customer Invoice Processing – Creating a policy to manage, store, and hold critical accounts payable can be time-consuming, but cardless companies are on the move without a policy.

How To Manage Accounts Payable Efficiently

With a compelling accounts payable policy, you can:

• Reduce the time it takes to approve invoices
• Better to keep and keep important documents
• Make it easy to extract documents for investigations, audits, or litigation

We’re here to get you started with this free guide. You will receive 9 tips for creating an easy payroll policy for employees to understand and follow, plus a template with suggested subject areas and sample text.

The Accounts Payable Expense Management Survey 2021 is the third global survey of financial leaders conducted in the last 18 months with our partner IFOL (Institute of Financial Operations and Leadership). The first assessment was conducted at the start of the first quarter of 2020 during the development of the pandemic, the second in the summer of 2020, and this last survey through August and September 2021. However, this is the first year that the United States was included in the study. The results show that in most of the categories studied. For example, the United States leads on a global average in expense management automation. In particular, 38% of US business leaders said their businesses were largely too automated, compared to 35% globally. The difference is even clearer when adopting AI for expense management: 35% of US companies are already using AI-related to the global average of just 30%.

Who is EasyAP?

Easyap, an expert in electronic invoicing, is a service company consisting of accountants and IT specialists that has focused since 2001 on supporting its customers throughout the invoice processing process.

Easyap develops tools that it makes available to its customers to improve the processing of all invoices, regardless of their format, and achieve savings.

Easyap can take an active part in invoice processing as part of its services.

How Does It Help In Electronic Invoices

Easyap has developed a portal for issuing/receiving electronic invoices. Einvoicing enables the connection between customers and suppliers (issuer and recipient), remove technological barriers, and offers advantages for both parties. Issuing electronic invoices to clients compatible.

Electronic Invoicing Service Companies

The companies on this list that offer electronic invoicing services meet the following conditions checked by the General Administration of the State:

• Connect with easyap – General Point of Entry of Electronic Invoices of the General State Administration via the automated interface of web services that are available to the providers.
• Have submitted electronic invoices via this interface.

If you famine your company to appear in this directory, your application will be evaluated and if it is satisfactory and meets the above requirements, it will be published in this directory as a provider of electronic billing services for providers of goods and services in your relationship. with public administrations.

The receipt of electronic invoicing services is the process of receiving the document with which a supplier certifies that he has provided us with a product or service at a certain price but in digitized form. Thanks to the computerization of these progressions, we can currently manage the receipt of supplier invoices using programs from specialized companies such as Easyap. In this way, issuing and receiving electronic invoices, which has traditionally been a tedious process in any company’s financial control, is now much faster and easier.

In turn, resorting to the services of companies specializing in receiving invoices from suppliers gives us the legal and mandatory guarantees that the sale of a product or service implies. Using the electronic invoicing services of companies like Easyap, therefore, enables us to streamline and automate processes and save time and resources that we can devote to other aspects of the business. And not only that, but we do it with all legal guarantees.

Easyap is an international platform that specializes in electronic invoicing solutions and value-added services in the tourism sector. In addition, the Voxel Group is also familiar with electronic invoicing services.

Our services include:

• Electronic invoice exchange, for which we integrate most of the ERPs on the national and international market.
• Connection to Easyap and other public administration access points for sending electronic invoices.
• Compliance with e-invoicing laws in major European countries and other international markets.
• Implementation of previous validation systems when sending and receiving electronic invoices.
• Service of legal retention and storage of electronic invoices.
• Electronic billing service from the Internet.
• Dedicated electronic invoicing service for restaurant, taxi, and parking costs to facilitate the reimbursement of VAT to customers.

Technology is Transforming the Workplace –

Technology is rapidly transforming every area of our lives. One of the most widespread changes is the boom in people working from home and videoconferencing, but there are many other ways that tech has made it into the workplace as well. Below are several ways tech is poised to continue changing how we do our jobs.

Telematics

Telematics is making inroads into many industries. For example, for trucking companies and other businesses that have a fleet of drivers, telematics can transfer data to offices in real time, which you can read about in a guide on the subject. Employers can see not just where drivers are at any given time but may be able to track a number of other things, such as driver behavior, and this helps improve their overall fleet management. Some insurance companies are using telematics as well, offering discounts for safer drivers or healthier practices. In the medical field, this quick transfer of data can mean people may not have to visit a doctor’s office for a sleep study or to get other information if it can be monitored and transmitted using a wearable.

Big Data and Artificial Intelligence

Thanks to AI, companies can make use of far more data than they have ever been able to before. This means getting granular data on customers and their preferences, which in turns allows companies to shape their marketing approach based on customer behavior. The use of big data and AI can also give companies information on how to be more efficient. AI-driven predictive modeling can help them reduce costs. Increasingly, this technology will allow many more tasks to be automated, freeing up people to do other types of work.

Robots

Robots does not just refer to what most people think of when they think of robots, tech that is roughly humanoid in shape. A robot may be a device that has a single function in an operating room, for example. They can also do work that is dangerous for humans, such as cleaning or inspecting machines in a factory. Of course, robots have also been at the forefront of space exploration for some time, with rovers on Mars sending back photos and collecting samples. They will increasingly be used for tasks that are mundane, repetitive, hazardous or impossible for humans to perform.

5G Wireless Technology

While most people think about 5G technology in terms of how it will affect their mobiles devices, it has huge implications for business because of its potential for reliable transfer of enormous amounts of data. For example, 5G makes it possible for doctors to perform remote surgery, working on someone hundreds or thousands of miles away. It will also enhance communication with customers, allowing companies to better demonstrate products and services and permit more interaction.

With the introduction of secure software development companies can feel confident exploring this new technology. Companies that rely on streaming will be able to deliver higher quality video than ever before. Businesses will also increasingly be able to access the Internet of Things, which faced some lags on 4G technology. This will mean being able to gather more customer data in retail locations among other uses.