Artificial Intelligence to Make DevOps More Effective

Artificial Intelligence to Make DevOps More Effective
Artificial Intelligence (AI) has found its way into numerous industrial processes. Companies implement AI not just to accelerate production, but also to increase precision and efficiency. Nevertheless, AI used to be a buzzword only a few decades ago, when the developments were not as far reaching as they have become now; it needed particular minds to both develop and operate it. However, now, our modern IT infrastructure has become endowed with talented thinkers that can turn impossibility into reality.

With an increasing emphasis on DevOps, organizations are focusing on efficiency and better reliability. The multi-leveled and interwoven IT strategies require equally sharp eyes and a keen mind to notice and trace critical events that trigger a specific function – this is where real-time and centralized log analytics plays a vital role. AI helps to troubleshoot the main issues quickly and efficiently, while also predicting future problems.

AI has gone from being a buzzing luxury to becoming a necessity of industries today; AI is redefining the entire system of proceedings itself. It is being combined with human knowledge to create breakthroughs and opportunities that would have been impossible without its intervention. Even in IT, where the environment has increasingly become agile and dynamic due to DevOps, the complex methodologies are being simplified through AI implementation. Apart from procedural ease, AI enables IT professionals in gaining insights into the problems that are otherwise so hard to trace.

The immensely complicated DevOps process often falls outside the reach of the human mind. The operations involved need precision, pace and, big data streaming, which are possible only with AI intervention. Thus, AI has become a powerful and essential tool for efficiently analyzing and taking over decision-making processes for better results. AI fills the gaps between human capability and big data through applications of operational intelligence. Additionally, AI speeds up troubleshooting and real-time decision-making.

AI’s Cognitive Insights
One of the most groundbreaking pieces of AI technology is applied in IT operations, namely Cognitive Insights (CI), which utilizes machine-learning algorithms to match human domain knowledge with log data, open source repositories, discussion forums, and social threads. Through this informational repertoire, CI forms relevant insights that contain solutions to a wide range of critical issues faced by DevOps teams on a daily basis. DevOps engineers face numerous challenges, which can be effectively attenuated by integrating AI into log analysis and other concerning operations. There are several applications of Cognitive Insights, which include:

Frequent attacks such as Distributed Denial of Service (DDoS) have become all the more prevalent. Threats which used to be limited to high-profile public websites and multinational organizations are now targeting small-scale servers, SMBs, and mid-sized enterprises. Having a centralized logging architecture to identify and pinpoint potential threats from numerous entries is essential for warding off such attacks. For this purpose, the application of anti-DDoS mitigation through Cognitive Insights has been highly effective. Leading organizations such as Dyn and British Airways had sustained potential damage from DDoS attacks in the past and subsequently installed a full-fledged ELK-based anti-DDoS mitigation strategy to restrict hackers and secure their operations against future attacks.

IT operations
Cognitive Insight can compile logs at a centralized point, with each entry carefully monitored and registered. It also provides the luxury of viewing the process flow clearly and executing queries of records from various applications; this thereby increases overall efficiency. With AI Cognitive Insight it is becoming straightforward to pinpoint the small, yet potentially harmful, issues in vast streams of log data. The core of this program is based on ELK stack and makes it easier to have a clear view of DevOps processes through the help of data simplification and assortment.

Besides these cases, AI integration in DevOps can yield several other useful outcomes including:

• AI-driven log analytics systems efficiently solve issues of identifying and resolving critical issues, which subsequently amplifies management and overall operational pace
• Improved customer success due to better results
• Monitoring and customer support becomes even easier
• Risk reduction and resource optimization
• Maximize efficiency by making logging data easily accessible

In other words, Cognitive Insights and other such Artificial Intelligent integrations can be of great help in data log management and troubleshooting. They can quickly pinpoint the issues from thousands of log entries which are often time consuming and erroneous when a human mind handles them.

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Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business

Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business

Automation is the key to success; every company is expanding on this domain’s expertise, as organizations take on a more global approach. Given the problems of decision making, learning, and the need for adaptability when understanding data, data scientists introduced the concept of Machine Learning within the realm of Artificial Intelligence. These practices have been able to bring about a radical change in modern business efficiency.

Artificial Intelligence is commonly a platform which performs tasks intelligently, without incurring the need for human intervention. On the other hand, Machine Learning is an exclusive part of the Artificial Intelligence world, which encapsulates the know-how and the logic behind making the concept of Artificial Intelligence a real success story. Through the use of Machine Learning, machines can be taught to work more sensibly, thereby allowing them to recognize different patterns and understand new circumstances with ease.

Machine Learning has come to be used extensively, especially when it comes to providing analytical solutions to the world of consumers and technology. Through large systems of data, Machine Learning has been able to drive solutions, which help create a more data-driven approach towards solving problems.

How Artificial Intelligence is Changing Enterprise Applications

Corporate enterprises are showing a growing interest in the field of Artificial Intelligence and Machine Learning. From IBM’s Watson to Google’s DeepMind to AWS’s multiple Artificial Intelligence services, there is a lot of activity happening in the market these days.

Other features of Machine Learning include the likes of Deep Learning, computer vision and natural language processing (NLP). With all these innovations languages in place, computers can enhance their functionalities, including pattern recognition, forecasts, and analytical decision-making.

By incorporating Artificial Intelligence and Machine Learning techniques in day to day functions, large enterprises can automate everyday tasks and enhance their overall efficiency in the long run.

Here are some ways in which Machine Learning techniques are helping enterprises enhance their efficiency:

Improving Fraud Detection: Fraud detection has become the need of the hour, as more and more companies are investing heavily in these new capabilities. With more companies falling prey to fraudulent practices, there is an imminent need to be ahead in the game of fraud detection. With Artificial Intelligence and Machine Learning in place, companies and organizations can extensively direct their resources towards enriching their fraud prevention activities, to help isolate potential fraud activities.

Loss Prediction and Profit Maximization: When it comes to deriving insights from heaps of data, there is nothing better than Machine Learning to prevent loss prediction and maximize profits. The stronger the techniques, the more foolproof the loss prediction methodologies would become in the long run.

Personalized Banking: In this era of digitization, everything is automated. For this reason, banks often seek to deliver customized, top notch, personalized experiences to their customers to keep loyalty intact. By leveraging their data, banks can aim to unearth customer needs and fulfill them with the utmost precision and dedication.

Robotic Financial Advisors: Portfolio management has become the talk of the town these days, especially since robotic financial advisors have stepped into the game. Clients can benefit immensely by this advancement, since the right opportunities are mapped with their portfolio needs and demands. Robotic applications are easy to merge with services such as Alexa and Cortana, allowing banks to provide exceptional service to their customers. Through this integration, financial institutions can hope to acquire new customers and also offer more individualized services to existing customers.

Next-Era Digital Traveling: Through the use of recommendation engines, travelers can experience the new recommendations for their travel aspirations. Organizations can play a role by allowing customers to converse with chatbots, which are created through the use of Artificial Intelligence and Machine Learning. As predicted by Gartner, by the year 2020, 25% of all customer service operations will rely on virtual assistant technology to make their business ends meet.

Detailed Maintenance: Through the help of predictive maintenance, industries like aviation, transportation, and manufacturing are expecting to be able to provide the best customer service in the market. Through the use of predictive models, such industries can accurately forecast prices and predict their losses, thereby, reducing any redundancies in the future.

With digitization paving the path of the future, there is a bright scope for companies and organizations which are investing heavily in these new age technologies of Machine Learning and Artificial Intelligence. Third party consulting services such as Idexcel are ready to help companies looking to take their first step with industry leading consulting and cloud-advisory services.

As we progress through the years, what should be interesting to note are the changes we will get to see in the various industries, as every sector aims to provide exceptional customer service to their customers in multiple ways.

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The Future of Data Science Lays within Cloud-Based Machine Learning and Artificial Intelligence

The Future of Data Science Lays within Cloud-Based Machine Learning and Artificial Intelligence

From working in solitary cubicles to working with artificial bots, automation has come a long way and has changed how the modern generation works. Today, Artificial Intelligence (AI) and Machine Learning (ML) have become regarded as the future of tomorrow’s workforce and culture. At Idexcel, we have been keeping track of these ongoing trends.

It has been said that “with great power comes great responsibility.” If we tweak this statement a bit to reflect more contemporary times, we can also assuredly say that “with great technology, comes heaps of data.” The more we progress on the path of digitization, the more massive our datasets have become, and the idea that fascinates all data scientists is linked with the emergence of AI and ML technologies – how can they be used efficiently?

Here are top 10 trends that you should look out for as they shape the direction of data analytics in our future:

Augmented Analytics: This technology broadly utilizes the power of machine learning to automate data preparation and presentation. Through the use of augmented analytics, data scientists hope to be able to aid human intelligence, to produce rapid outcomes in different data-driven domains.

Artificial Intelligence and Machine Learning: Many of us might still be living in a bubble when it comes to AI assisted work. However, the fact of the matter is, that this bubble is going to burst. With robotics and artificial intelligence taking over at an increasing pace, there is a very heavy emphasis on getting data up and running to meet organizational goals. AI and ML will be used extensively to simplify work processes through the use of Big Data analytics.

Big Data: As technology has been advancing over the years and more affordable machines have emerged, faster processing powers have been available to businesses. Now, as cloud services are taking over traditional storage methods, there is a lot to look forward to regarding the increased output of processed information. As all these sources of information generate data, there is an imminent need to draw meaningful conclusions from the data – this is where Big Data comes into the picture. As the sources for storage get defined, Big Data provides excellent methods for allowing the manipulation of stored data to draw analysis and get the ball rolling.

Cloud and Edge Computing: One can easily say that technology has reached the Clouds. Companies such as Amazon, Google, and Microsoft are providing Cloud Services to organizations for storing their day to day data. Edge Computing, which is another form of shared computing, has become the next generation’s technology. Through the means of Edge Computing, organizations can overcome connectivity and latency issues, so that the distance data has to travel is reduced significantly. Edge Computing has seen an increasing rate of growth in mobile computing, as well as in the decrease of computer hardware. The rising use of IoT-enabled devices has ushered in an era of new technology.

Predictive Analytics: As more problematic situations emerge, there is a need to develop systems which can solve problems with ease and provide meaningful solutions. Predictive analytics prove to be the solution for such issues. The better the insights, the more structured are the solutions. Such are the capabilities of predictive analytics, as they help organizations gear up to tackle the worst possible issues.

Blockchain Technology: Digital currencies, such as BitCoin, owe their very existence to Blockchain technology. Given the success rate of cryptocurrencies, there is a lot of focus on merging Blockchain technology with the world of data science. The idea is to fuse the two methodologies together, to maximize the results. Since Blockchain technology is a versatile source, it can store any digital data; it has become a well-received option with data scientists.

As more and more organizations are taking analytics and data science seriously, there is an imminent need to progress to the next level of technology. Here at Idexcel, we work with clients every day to provide DataOps Consulting and Services; it has become an inseparable part of the modern organizational structure. As we progress into the future, the thin line between business intelligence and artificial intelligence will be removed; data will become smarter than ever before.

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How Artificial Intelligence is Transforming Cloud Computing

1.	How Artificial Intelligence is transforming Cloud Computing
Everyone in touch with technology is aware of cloud computing. It has already turned out to be an important part of the current digital era. It has transformed the manner in which individuals, professionals and even companies store their essential information and data.

The market for cloud computing has tremendously made progress over the past few years which in turn has strongly affected the lifestyle and work culture in various ways. But the cloud is a new technology, due to which companies are worried about whether it will evolve over time or not. Recent trends such as the use of mobile phones instead of computers have even made petty changes in the cloud technology. Therefore, AI has come up to enhance the cloud technology (AI is the capability of a robot controlled by a computer or a digital computer that carry out tasks that are usually linked with the intelligent beings). Cloud computing and AI are bringing major changes in the corporate world and their fusion is believed and known to be the coming future of technology.

The cloud technology can help AI’s by providing the required information for the learning processes while the AI can help cloud by providing information that can offer more data. AI is capable of streamlining the immense capacities of the cloud. It equips cloud technology with enormous powers. It enables the machines to act, react, think and learn in the manner human beings do. AI assists different machines in learning and analyzing the historical data, making decisions and identifying the patterns. Such a process helps in eradicating the chances of human errors. Therefore, AI enhances the process of decision making of various organizations.

Cloud technology is spread among a number of servers in various languages with huge data storage and across various geographies. Organizations can make use of this data to make up intelligent and automated solutions for customers and clients. Cloud computing is getting more powerful with AI as its applications are extended across multiple diversified sectors in the economy. Thus, even organizations can make use of AI cloud computing to attain long-term goals for their businesses.

Another crucial aspect of the fusion of AI cloud computing is the process of machine learning. Such a process helps in making reliable and quick decisions, reducing the chances of cyber crimes and improving the experiences of the customers. In the recent years, machine learning has been able to quickly employ complicated mathematical calculations to large amounts of data. It is even capable of delivering more accurate and quick results on a huge scale which drives new business opportunities and growth strategies for organizations around the world.

The fusion of AI cloud computing has brought about a huge change in information technology and several other industries. It can potentially change the manner in which the data was stored earlier and processed among several geographies. Such an amalgamation also offers unique opportunities for the professionals of AI and cloud to look over the boundless possibilities for future.

Cloud, when stood alone, has the capability of becoming a significant computing commodity in several fields. But the AI cloud computing integration will enhance its requirement in the market. With huge strides existing in the growth of both cloud and artificial intelligence, their future seems to be highly tied together. Cloud computing gets much easy to protect, scale and handle with artificial intelligence. Above that, the more the businesses are getting on the cloud, more it needs to be integrated with AI to attain efficiency. A point will come when no cloud technology would be existing without artificial intelligence.

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