• Security-related certifications pay on average over $17,000 per year more than the median IT certification salary.
• Citrix certifications have annual salaries that range from $99,411 to $105,086 with a median salary of $102,365.
• AWS Certified Solutions Architect – Associate is paying a median salary of $125,091.
• Project Management Professional (PMP) certifications are the most pervasive, with 730,000 active PMPs in 210 countries and territories worldwide.

These and many other insights about the highest-paying certifications this year are from Global Knowledge’s latest research on the salary levels and market conditions for IT certifications. Their recent survey is summarized in the article, 15 Top Paying Certifications for 2017. This year Global Knowledge distributed the survey globally, providing the 15 top paying IT certifications for the United States recently. A certification had to have at least 115 survey responses to ensure that the data was statistically valid, and the certification exam had to be currently available.

Key insights on the 15 top paying IT certifications in 2017 include the following:

• Strong demand continues for IT professionals with Risk and Information Systems Control (CRISC) certifications. Global Knowledge estimates than more than 20,000 people worldwide have earned this credential. Ninety-six percent of those who have earned it keep it current. Demand for this certification is outstripping supply, driving up salaries in 2017.

• All five AWS certifications available pay above market with the average salary being $125,591. Global Knowledge found that each of the five available AWS certifications pays above $100,000 a year. There’s clearly a shortage of certified AWS architects available today, as IT organizations often compete to hire IT professionals with this and more AWS certifications. Please see the Global Knowledge post, What It Takes To Earn a Top-Paying AWS Certification for additional details.

• Honourable mentions include Cisco and CompTIA certifications, including CompTIA+ Security which pays an average salary of $89,147. CompTIA A+ certifications pay $79,877, CompTIA Network+ certifications pay $81,601, and Cisco CCNA Routing and Switching, $83,945.

• Average income across all 15 certifications is $109,721. Certifications that specialize in security pay an average salary of $127,061 a year, over $17,000 more than the median salary across the top 15 certifications.  The three Microsoft certifications range in salary from a high of $101,150 to a low of $93,718, with a median salary of $98,142. Citrix certifications have annual salaries that range from $105,086 to $99,411, with a median salary of $102,365. The following table provides a breakout of the top 15 IT certifications in 2016 according to Global Knowledge’s salary study.

Read more: The key cloud certifications needed to boost your career in 2017

Enterprises and governments rely upon the timely transfer of mission-critical information to keep their projects and operations flowing smoothly. To do so requires a complex chain of communications hubs working efficiently, and even when they are working optimally, there can still be seconds of lag time between communications. That may not seem like much, but being able to get information a second faster than the competition (or the enemy) can make a huge difference.

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SYS-CON Events announced today that Grape Up will exhibit at SYS-CON’s 21st International Cloud Expo®, which will take place on Oct. 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Grape Up is a software company specializing in cloud native application development and professional services related to Cloud Foundry PaaS. With five expert teams that operate in various sectors of the market across the U.S. and Europe, Grape Up works with a variety of customers from emerging startups to Fortune 1000 companies.

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The battlefield for cloud pricing will shift from virtual machines to object storage, with databases among others undergoing similar pressures in the next 18 months, according to the latest note from 451 Research.

The trend of vendors undercutting each other for VMs has long been apparent; take January last year as just one example, where Microsoft announced price reductions of up to 17% on its Azure D-series Dv2 VMs shortly after Amazon Web services announced their latest cuts.

According to the figures, obtained via 451 Research’s Cloud Price Index, object storage prices are declining across every region, including a drop of 14% over the past year.

451 puts price cuts beyond compute down to an ever-maturing market and an increase in cloud-native development, although adding that the market ‘is not highly price-sensitive at this time, although naturally, end users want to make sure they are paying a reasonable price’.

“The big cloud providers appear to be playing an aggressive game of tit for tat, cutting object storage prices to avoid standing out as expensive,” said Jean Atelsek, digital economics unit analyst at 451 Research in a statement. “This is the first time there has been a big price war outside compute, and it reflects object storage’s move into the mainstream.

“While price cuts are good news for cloud buyers, they are now faced with a new level of complexity when comparing providers,” Atelsek added.

One analyst firm which attempts to solve the imbroglio of cloud pricing for customers is Cloud Spectator, who issues a yearly report outlining the best vendors taking into account both price and performance. The company’s analysis puts 1&1 at the top of the charts, giving it the benchmark score of 100 out of 100, with AWS (24), Azure (27) and Google (48) struggling by comparison, although adding performance on VMs was less variable with the major players.

Back in May, 451 Research argued there was a ‘very limited relationship’ between price and market share and that the ‘race to the bottom’ was something of a misnomer, with the supply of higher value services being key to long term growth for vendors.

You can find out more about the Cloud Price Index here.

Machine learning is being put to use in all sorts of areas today. From smart cars and homes and beyond, the use of artificial intelligence (AI) and machine learning (ML) are becoming a larger part of how many companies conduct business. As more and more businesses are hit with cyber crime rather than physical crimes, there has been a needed shift from commercial surveillance systems towards cyber security systems to protect confidential data. More recently, we’ve seen ML sink its teeth into anti money laundering (AML) with big potential impacts there.

Most current AML systems are founded on an extensive list of rules. Banks and institutions are required to comply with the Bank Secrecy act and implement certain AML rules. These regulations are meant to help detect and report any suspicious activity that could indicate money laundering or terrorist financing. As these regulations have become more demanding, the traditional rules-based systems has become more and more complex with hundreds of rules driving know your customer (KYC) activity and Suspicious Activity Report (SAR) filing. As financial institutions monitor billions of transactions a day, the data mined from each creates a silo of information and data that any person would find overwhelming to sift through. More and more cases are being flagged for investigation but more and more false positives pop up.

Along with in an increase in the false positive rates, another challenge found in AML is the fact that it hardly ever signifies as the activity of just one transaction, account, business or person. Because of this, detection cannot focus on singles instances but rather requires analysis of behavioural patterns of transactions occurring over time. Therefore it is nearly impossible for personnel to investigate all cases in a timely manner.

Additionally, there are very little historical data surrounding ML making it difficult to pinpoint exact tactics and methods for ML. From trusts to black market currency exchanges or loan-back schemes, there is, unfortunately, no typical ML case. This also lends to indefinable data labels and sets, which have required manual analysis in the past. Despite the growing difficulties to monitor AML, more financial institutions are turning to the various technological tools such as AI, ML and big data analytics to detect ML cases.

When combined, these systems can merge across massive spectrums of data sources and dig through boundless mountains of data. When ML or AI has been implemented, however, these hybrid systems are able to translate those unlabelled points of data into signals to detect behavioural anomalies and intent. These ML systems can establish “normal behaviour” patterns and then identify anomalous behaviour next to it, thereby weeding out the false positives from true ML cases. In this instance, there is massive reduction of false negatives and false positives.

Moreover, FICO has created an AML Threat Score that helps to prioritize investigation queues for SARs, utilizing behavioural analytics from Falcon Fraud Manager. FICO utilizes transaction profiling tech, self-calibrated models and customer behaviour lists all which can adapt to the constant changing dynamics within a financial institution. Other ML systems that have seen progress has been the “unsupervised learning” — a form of machine learning that uses algorithms to draw inferences from data sets that lack labelled responses. Due to the large gap in historical data on ML cases, there is a weighty need for ML technology to be able to analyse and gain insight from data without prior knowledge of what to look for. Unsupervised machine learning learns from that unlabelled data and results in the ability to differentiate between the relevant and irrelevant data and can then divide this unlabelled data into usable clusters. This is achieved through link analysis, temporal clustering, associative learning and other techniques that allow financial institutions to track entity interactions, behavioural changes and transaction volatility.

The benefits of using machine learning to prevent ML can be seen when labelled and unlabelled data from this slew of sources can be ingested into a system that is flexible enough to accept a multitude of data points across a myriad of sources while also analysing its potential for a ML case. As the AML regulations that are required of banks become more intense and fines for complying grow, you can be sure to see the implementation of machine learning built on top of the traditional AML systems already in place today.

Grape Up is a software company, specialized in cloud native application development and professional services related to Cloud Foundry PaaS. With five expert teams that operate in various sectors of the market across the USA and Europe, we work with a variety of customers from emerging startups to Fortune 1000 companies.

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Monitoring of Docker environments is challenging. Why? Because each container typically runs a single process, has its own environment, utilizes virtual networks, or has various methods of managing storage. Traditional monitoring solutions take metrics from each server and applications they run. These servers and applications running on them are typically very static, with very long uptimes. Docker deployments are different: a set of containers may run many applications, all sharing the resources of one or more underlying hosts. It’s not uncommon for Docker servers to run thousands of short-term containers (e.g., for batch jobs) while a set of permanent services runs in parallel. Traditional monitoring tools not used to such dynamic environments are not suited for such deployments. On the other hand, some modern monitoring solutions (e.g. SPM from Sematext) were built with such dynamic systems in mind and even have out of the box reporting for docker monitoring. Moreover, container resource sharing calls for stricter enforcement of resource usage limits, an additional issue you must watch carefully. To make appropriate adjustments for resource quotas you need good visibility into any limits containers have reached or errors they have caused. We recommend using alerts according to defined limits; this way you can adjust limits or resource usage even before errors start happening.

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Most of us have moved our web and e-commerce operations to the cloud, but we are still getting sales reports and other information we need to run our business long after the fact. We sell a hamburger on Tuesday, you might say, but don’t know if we made money selling it until Friday. That’s because we still rely on Batch processing, where we generate orders, reports, and other management-useful pieces of data when it’s most convenient for the IT department to process them, rather than in real time. That was fine when horse-drawn wagons made our deliveries, but it is far too slow for today’s world, where stock prices and other bits of information circle the world (literally) at the speed of light. It’s time to move to In-Stream Processing. You can’t – and shouldn’t – keep putting it off.

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