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Artificial Intelligence (AI)

Why AIOps Needs Big Data and its Importance in Business

AIOps is an Artificial Intelligence used for IT Operations. It mainly uses Analytics, Machine Learning (ML), and Big Data to automate IT

Published

6 months ago

November 25, 2020

TwinzTech

The Role of AI in digital marketing Artificial Intelligence (AI) Marketing, Business

Introduction

The current IT environment has evolved to a point where old, manual methods were not sufficient to keep up with today’s needs. Increasing complexity, the need for quick solutions, and the massive size of data in IT operations require AIOps to function smoothly.

Table of Contents

1. What is AIOps?

AIOps is an Artificial Intelligence used for IT Operations. It mainly uses Analytics, Machine Learning (ML), and Big Data to automate IT operations and produce results in real-time. It is an essential tool for monitoring and managing IT Operations.

If issues in digital services are not quickly detected and resolved, business operations will be negatively affected. Customers will not have a satisfying experience. To avoid this situation, AIOps must be implemented.

AIOps does an algorithmic analysis of all the data and helps the IT Operations and DevOps (Development Operations) teams to identify and resolve issues with high speed. AIOps prevents outages, reduces downtime, and provides seamless services. AIOps can give better insights as all the information is centrally stored in one place.

2. Aspects of IT Operations monitoring using AIOps:

a. Data Selection

The modern IT environment generates massive amounts of heterogeneous data. For example, event records, metrics, logs, and other types of data from different sources like applications, networks, storage, cloud instances, etc. This data is always high in volume, and the majority of it is redundant. AIOps use entropy algorithms to remove noise and duplication.

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b. Pattern discovery

Selecting meaningful data and grouping them by correlation and identification of the relationship between them by using various criteria. These groups of data can be further analyzed to discover a particular pattern.

c. Inference

Recurring problems are analyzed, and root causes are found. Identifying such issues makes resolving them more comfortable and quicker.

d. Collaboration

AIOps tools help in reporting to required operators for collaboration without any mishaps, even when these operators are in different departments or different geographical locations.

e. Automation

Automation is the heart of the AIOps. When the business’s infrastructure continues to grow and multiply, AIOps helps automate all the business processes. It helps in storing data centrally, auto-discovering, and mapping the infrastructure, updating databases (CMDB), automating redundant tasks and processes. Thus, leading to agile and efficient IT and business operations.

3. What is Big Data?

Big Data is a high volume of structured and unstructured data that is generated by businesses at high speed at varying veracity.

It systematically extracts meaningful insights from this data to make better decisions and strategic business moves. With the advent of digital storage in the year 2000, data creation increased as digital storage was cheaper than analogue storage. DVDs made data sharing easier.

As institutions like universities, hospitals, businesses started using technology, the amount of data created went through the roof. This resulted in two problems.

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a. The rigidity of relational data structures

This was solved by using Data lakes. The data lake is a centralized repository which allows data storage of all the structured and unstructured data (usually files or object blobs) at any scale and makes it available for analysis.

b. Processing queries in the relational database has scaling issues.

When queries were processed in a single queue, it was time-consuming. The use of Massive Parallel Processing (MPP) resolved this technical issue.

Hadoop 1.0 is an open-source software framework. It was implemented using data lakes and MPP. Apache Hadoop facilitated the use of big data in all organizations. Hospitals, Scientists, and businesses used big data to analyze large sets of data and derive valuable insights quickly.

Hadoop 1.0 had few drawbacks. Optimization of data was complicated. The organization had to employ data scientists to get the required insights.

The introduction of Hadoop 2.0 resolved those issues and further commoditized big data. Hadoop 2.0 also enabled the use of AIOps.

4. The necessity of big data for AIOps

AIOps can function only with big data as older datasets are small and inefficient.

Hadoop 2.0 had a YARN feature that supported data streaming. It also enabled interactive query support. It allowed the integration of third-party applications.

This means that analytics could be improved, but only if it was re-architectured. Organizations without data science resources still had difficulty in optimizing and using Hadoop for better data analytics.

The requirement for more purpose-built and easy use solutions brought companies like Logstash, Elastic, and Kibana to the market. They replaced Hadoop in a few use cases.

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5. What does this mean for your business?

This is important for both Core IT Operations and Service Management because they both rely on an interactive query and streaming data technology.

The Digital Transformation of organizations elevated the need for IT solutions. IT had to deal with increasing complexity, massive data size, and speed.

Transition by upgrading or re-architecture method to support Big Data was also tricky due to purpose-built applications, and the data remained in silos.

AIOps makes Artificial Intelligence take over manual analysis. Data from all the silos form the dataset. Interactive solutions are designed from both technical and usability perspectives.

Conclusion

IT operations need to work on diverse data, analyze real-time streaming data, identify and automate workflows, derive meaningful insights, and support historical analysis. All this requires businesses to build a Big Data backend on the AIOps platform.

AIOps initiative must not be built with a traditional, relational database. AIOps improves the functionality of IT operations. Hence, we can say that AIOps needs Big Data to function efficiently. Also, businesses and corporations who need to store large amounts of data will need AIOps to function correctly, automate tasks, obtain insights, and work efficiently as per the trending demands from end-users.

Artificial Intelligence (AI)

5 Ways Artificial Intelligence Can Positively Impact The Future Of Web Streaming

5 Ways Artificial Intelligence can Positively Impact the Future of Web Streaming. Better Encoding, Zero Privacy Issues, Improved UI

Published

1 month ago

March 27, 2021

TwinzTech

5 Ways Artificial Intelligence Can Positively Impact The Future Of Web Streaming

Despite inspiring a considerable amount of intimidation, AI is at the forefront of innovation, especially in the post-pandemic era. However, as a futuristic technology, AI’s real essence needs to be ascertained from an open mindset. Reasons being the diversity of approach and the usefulness of AI as a multivariate resource for simplifying human indulgences!

Then again, AI’s role in optimizing video streaming experiences has been under a lot of speculation in 2021. As individuals rely heavily on streaming devices, streaming platforms, and even web streaming services to keep up with the leisure spells, even service providers are wrapping their heads around the inclusion of AI to make the experiences even more rewarding.

Those who aren’t of the positive impact of AI regarding the video streaming modules and workflows should closely follow this discussion. In the subsequent sections, we shall take a closer look at home Artificial Intelligence can impact the diverse clientele’ web streaming experiences, sans privacy and quality compromises.

1. AI and Video Streaming: The Relation

Artificial Intelligence AI Web Streaming

Before we move any further with this discussion, it is important to note how AI technology works, with video streaming being the subject of interest. AI put, equates to mimicking the human mind, albeit an intelligent one.

Before AI became a fad, intuitive human minds used to manually manage certain processes, slowing down the entire system in the process. On the other hand, AI goes relentless about the task and even empowers any given process with cumulative computational power about machines.

As with video streaming, AI comes in as a handy purported inclusion as the technology allows you to sift through massive data sets in a matter of seconds, something that is virtually impossible for any human mind. Therefore, in the immediate future, AI will aim to optimize streaming workflows by calculating bitrate, user expectations, and even network conditions.

Therefore, broadcasters will not have to worry about catering to diverse audience bases separately. The platform itself will be extremely intelligent, and content quality will vary as per preferences and resources.

If you are still unsure as to how AI will make such a lasting impression, considering the already evolved nature of the streaming devices, platforms, and services, keep reading on:

2. 5 Ways AI can help

a. Better Encoding

If you are not aware of how the concept of video streaming works, a better understanding of encoding is expected to help. This means the content-aware realms process and stream videos, where the quality, inclusion, resolution, and other aspects are in sync with the receptors hardware configuration.

While most encoding tasks are still being initiated manually, AI can help automate the process more with trained machine learning algorithms to work with.

Once AI is incorporated into the mix, optimizing bitrate, accounting for latency, and syncing along the video streaming protocols become easier than ever.

While most streaming platforms and applications is already pursuant in a given capacity, this inclusion will make the most sense of website streaming and relevant content, often lacking a dedicated UI to optimize the broadcasted quality.

Not to mention, the role of AI would impact VDO and even live streaming workflows, allowing broadcasters, enterprises, and companies to save a lot of money by facilitating curated bandwidth usage.

b. Zero Privacy Issues

Web streamers often find it hard to curate content based on locational and copyright issues. This means, if a web stream is broadcasting something sensitive where certain parts of the content are confidential and need to be blurred out, manual settings might not be as helpful.

On the other hand, AI can check and optimize the entire workflow and essentially take care of the brand names, signs, logos, and other information bits via advanced and trained computer vision.

This way, it becomes easier to send forth unique content.

c. Improved User Experience

Eventually, it all boils down to improving the overall customer experience, something most web streamers strive for. While a handful of web streaming resources already offered the best possible experience by eliminating clutter and issues related to buffering, AI implementation in other resources can have a similar effect.

Content tagging happens to be one such aspect as it allows the streamers to broadcast specific content pieces, depending on audience preferences.

Artificial intelligence, therefore, makes watching USTVGo on Firestick a more lucrative experience as you can get access to curated content, sans broadcasting issues.

Not just USTVGo, but other web streaming resources can also benefit from the concept of content cataloguing whilst helping convert prospects into active viewers.

d. Automated Detection

Have you ever seen certain channels blurring out sharp images and elements for a particular program or a movie? This is what AI aims to achieve on a larger scale.

Instead of manually monitoring every program, Artificial intelligence will bring in the ability to detect certain elements automatically whilst managing them as per preferences. This way, any web streaming platform can go safely and securely about VOD streaming without worrying about legal issues.

e. Zero Copyright Infringement

Here comes a more scalable aspect relevant to the AI implementation. Ascertain brands and enterprises are quite serious about their logos; streamers often look for technologies that can take care of the same by selectively hiding or blurring out the same.

Leveraging artificial intelligence can make this concept work, but streamers must first train the systems with relevant algorithms to facilitate seamless detection.

3. Bottom-Line

Artificial Intelligence, if leveraged right, can change the way web streaming is perceived in the modern era. With the streamers becoming more intelligent, it will be easier for the relevant broadcasters to rely on selective and optimized broadcasting.

While we are currently looking at web streaming, AI can also simplify the way businesses work with live streaming workflows and sessions.