Introduction The term AIOps was coined by Gartner in 2016; it is an acronym for Artificial Intelligence Operations and refers to an analytical machine learning approach that enhances IT operations analytics. AIOps encompasses and machine learning capabilities to automate the IT operational processes such as event correlation, anomaly detection, and cyber threat remediation, restoration, and determination to help organizations optimize their revenue streams. big data Importance of AIOps for Businesses The collection and analysis of data help organizations make more effective decisions by leveraging this data to predict important future business outcomes and even proactively remediate security threats before they become an issue. AIOps is beneficial for organizations in the following critical ways: a) It helps them in operationalizing their business continuity and resilience frameworks in ways that allow negligible or null downtime b) The businesses get a single dashboard view for all IT infrastructure and applications; allowing them to analyze their strengths and opportunities, and contribute more focus to the critical areas of improvement c) There’s a significant reduction of manual work and IT operational costs for organizations over a period of time d) Faster ticketing systems for critical business tasks e) Data-driven decision-making based on more precise precognitive insights How an Open Source and Decentralized Environment Smoothens Operationalizing AI According to an article by Forbes, AIOps serves as a Sherlock for combating network challenges as AIOps platforms can “enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.” This statement emphasizes the criticality of open source and decentralization in the evolution of AIOps. According to research by Mordor Intelligence, AIOps solutions are playing a pivotal role in optimizing IT operations for digitally-conscious organizations and are expected to be a lucrative industry worth more than $40 billion by 2026. Open source and decentralization contribute to the AIOps model, making it dynamic and optimally beneficial for community and information sharing. Democratization of information not only virtues information to be processed independently but also allows for operationalizing of data points across various computing devices and IoT. Open source and decentralized environments foster a culture of rapid innovation amidst the AI environment leading to the rapid evolution of the AIOps framework within the organizations. In a world where distributed workload has been normalized, a decentralized environment can fuel up some much-needed renovation to evolve the enterprises’ AIOps models and take them to the next level. Open source and decentralized environment facilitate leveraging big data, machine learning, artificial intelligence (AI), and other technologies in a community ecosphere bringing in greater innovation and automation for enterprises' IT infrastructure and service management. There's also greater visibility to combat infrastructure challenges continually. Also, if your company deals with data solutions, such infrastructure becomes even more important. How AIOps Benefits From Decentralized and Open Source Movements The evolution of AI operational systems is driven by a push toward greater decentralization and openness. In this blog, we will discuss how AIOps can benefit from being decentralized and open source in order to leverage these benefits, such as scalability and transparency. By embracing these movements, our customers can more easily iterate quickly without sacrificing security or privacy. So let’s delve right into the details: Conversational AI Developed Because of Decentralization & Open Domain Conversational AI was born because of decentralization and open domain. Because of this, we have datasets that are different from each other but can still be integrated and used with each other via intermediate layers to form a common training and learning process. Conversational AI is based on different datasets, uses different models and intermediate layers need to be formed. At present, many data such as images, videos, text, and speech are available in the decentralized computing system. Some cutting-edge technologies such as deep learning are also widely used for speech recognition, image recognition, and search result prediction. In addition, it can be applied to other fields such as smart cars and smart homes because it can share data and use different models without having to integrate with central systems like cloud platforms. For example, a machine learning model and a deep neural network are two different machine learning algorithms. Many of the open-source libraries on GitHub have helped budding developers across the globe develop their own conversational AI models. Moreover, possibilities are endless, with some of the popular deep learning frameworks being open source, such as Google’s TensorFlow, Facebook's PyTorch, Caffe2, Torchcraft AI and Hydra, etc. Platforms for ML were built for Multi-channel Datasets The number of open datasets has grown quickly over the past few years, and businesses are seeing the value of allowing customers to create their own apps. A growing number of companies are building platforms for ML to tackle this problem. Some of these platforms allow users to upload their own data sets, while others simply host existing datasets that can be analyzed by a wide variety of applications. Multi-channel datasets are available for many different purposes. Some people use these datasets for research, others for personal use or for creating content on a website, and others for marketing purposes. These data sets contain a large number of individual records that are stored over time or between different platforms (e.g., Instagram vs Snapchat). Also, there are multiple versions that include different metadata that could differ from one another as well (e.g., numeric vs text). When you start working with big data, especially multi-channel datasets, the CNN works fine only if you have a High-Performance Computing Platform to handle all your processing requirements. For example, Facebook has democratized and open-sourced many of its libraries including PyTorch, Flashlight, Opacus, PyTorch3D, Detectron2, and Prophet that helped advance Facebook AI Research (FAIR) and bolster machine learning and AI technologies across the globe with the advent of the state of the art technology toolsets. Building & Nurturing a Community around a Platform and Its Contributions lead to the Evolution of AI models Open source community is developed by people, who contribute to the asset for community growth and development, thus leading to innovation and evolution of AI models. Using centralized models often makes it difficult for organizations to maintain and upgrade their AI models as new knowledge and technologies are introduced. Collaboration and contribution are the essences of evolution within a decentralized environment. Also, decentralization means that people from all verticals and functions can share their knowledge and experience to improve the models irrespective of their core areas of expertise. Decentralization Leads to Faster Delivery Within a centralized environment, there exist a vertical hierarchy, rules, and guidelines that delay the delivery process as it gets halted by excessive scrutiny at each step. Within a decentralized environment, the guidelines are distributed and there is no single guideline evaluator for each platform which not only democratizes knowledge for everyone by sticking to the “Learning the Longer Way Philosophy,” but also helps in faster delivery. This instills and reverences a data-intelligent and AI and ML-driven organizational culture. As a decentralized environment allows for data intelligence and AI practices to be applied across organizational boundaries, which will ultimately lead to faster delivery. This can be attributed to its horizontal structure where knowledge is shared across all members of the organization, creating a culture of learning as one grows – the long way. Decentralizing Doesn't Mean Super-Decentralizing Or Else There Will be too Many Mistakes Decentralization does not mean super-decentralization. The easiest way is to simply keep it running and avoid over-engineering, or else you’ll spend all your time trying to fix things. Keep it very decentralized and in tandem; get it running; don’t make it super-decentralized or else there will be a lot of mistakes. After all, every organization wants to be at the core of their products yet take advantage of the ecosystems that surround them. This is where they can really excel allowing their products to be truly useful for their user base instead of just providing one feature that works well for specific use cases. So, while decentralization helps, we always need to define our boundaries around how much of it will be a healthier approach for our organizations to adopt. Sharing knowledge within a decentralized environment can fuel radical transformation and radical candor for the enterprises facilitating AIOps for their in-house teams or their clients. There needs to be a proper alignment for sharing knowledge across the organization wherein speed of delivery and ownership of building become mission-critical factors. To minimize biased actions and impacts within such systems, conflicts must be addressed and mitigated on time. Keeping Hiring Decentralized Helps The decentralized hiring helps the new age of work. Anyone can do anything, but HR is not there. The only thing that matters to getting hired is your skills, not your connections. Whoever is interested in the job can apply. You can hire a connected or a remote employee or both of them. Anyone can hire anyone, and this can help get rid of shady practices in hiring. Decentralized workplaces have redefined the dynamics of hiring. Decentralized protocols will take over HR as they lower the overall costs while increasing transparency. Radical & Sporadic Transparency In Decentralized Virtual Spaces Lead to Impeccable Results Decentralized workplaces can support the ever-changing aspect of digital social networking in the future. Since people are multiform and multifaceted, with different needs and desires, virtual spaces need to be adaptive in order to directly cater to these people. This is where radical transparency comes in handy because asking someone or forcing someone to show what they want or disclose something is not the right tool for today's users who wish to remain private by default. The results in a decentralized virtual space are impeccable as it is controlled by the people. But to ensure this users need to show what they want, so they can be judged and rewarded accordingly. To deliver an exemplary product or service within a decentralized virtual space, each person has to be involved in the solution, not just everyone else who shows up and leaves. Decentralization and the significance of transparency in virtual spaces have been highlighted as imperative these days. A certain minimum Level of Infrastructure is required to support this kind of decentralization. Each employee within a decentralized workplace is a full-time enabler not just an employee and he or she can help many others excel at tasks that are conventionally associated with his or her core competency and others can do the same for him or her as well. A decentralized virtual space is a self-organized and self-managed environment that allows individuals and groups to communicate openly and unhindered. Sporadic transparency leads to impeccable, reliable, and marvelous results most of the time because no mechanism for censure exists. Asynchronicity is Pivotal Within Decentralized Communities Asynchronicity is pivotal within decentralized communities. Staying on the frontier is an Initiative that the employees take, which is a conscious decision to avoid feeling out of control, like you have no influence or control over your project. Your team will not cancel projects, reschedule meetings or cancel others' work. Leadership has an important role in mentoring, challenging, and inviting input from employees within your decentralized community – especially those who are often at the forefront of change. The employees of a company have to stay on the frontier and always faced a variety of challenges. They have to adapt their workflows and tools, keep the company’s culture alive and running, deal with new technologies to make their processes more efficient and scalable, as well as keep up with internal culture shifts going from an old-timer culture to a more diverse set of employees. Simultaneously there also needs to be a very organic, value-driven, and flexible plan for prospering of such companies. The biggest benefit of working within such an organizational culture is that nobody at any time is driven by the opinion of only the internal team members but a community at large. Community support helps you adapt faster amidst the ever-dynamic business landscape and keeps you cutting-edge. A Non-Propriety Approach to Creating & Delivering Values Wherein Capturing a Small Portion of Value is Sufficient to Generate More Profitable Revenue Streams Than a Propriety Model You don’t need to capture 100% of the Open Source Value. You just need 1% of the value and you’ll get more revenue streams than a Proprietorship model that captures. A lot of people are in the mindset that they need to capture 100% of their value, but you don’t. You will do great if you capture 1% of the Open Source value that you create. A brand such as WordPress or a social media platform like Facebook captures only a minor proportion of its value in Open Source. The crux is your open source delivery model will be more successful by capturing 1% of the open source value and generating 1000x more revenue streams. This approach is more profitable than conventional propriety approaches focused on capturing as much as 100% of the value by creating very less value in the first place. Eventually, as your customers will get allured to your open-source software solutions they will also subscribe to your paid or enterprise solutions. The focus of a non-propriety approach is not on revenue generation but on keeping their product features evolving – especially the features of the premium products that they cater to. This is also important because the AI market and industry are constantly evolving and an ML model that can be premium today might become mainstream soon. So, continuously evolving commercial and non-commercial products are the core focus for the organizations going open-source. This helps them in delivering constant value and living up to the expectations of the community, customers, and stakeholders. With continuous evolution being a norm customers keep on getting enamored with the free versions of the products and solutions and later they pay for the premium subscriptions as well. The evolution of the non-commercial part of the product is paramount and probably the hardest thing; the commercial part of the product is the reflection of the culture of the rest of the company so it’s not that difficult. Most demands come organically and that contributes to a healthy revenue stream. The main focus of a company should be to create value based on massive usage and that’s hard enough; and then operating from the faith and assumption that you can capture a considerable part of that. Choosing Collaboration Over Competition Open source communities have the innate DNA that allows them to adopt a collaborative approach to competition. The paramount focus again here remains on creating value, not on capturing it. Open source is like a house built on plenty of lands. The builders know they are building their own home and they don't need to use every bit of space, but once it is built, they can choose to expand. This allows them to allocate time, resources, and energy towards the things that will make the most difference for them and their community. The fact that the inherent nature of open source software is collaborative by design has also helped AIOps evolve, as public cloud providers such as Amazon AWS, Azure, and Google Cloud have collaborated with a lot of open-source ML model enablers such as Hugging Face. In fact, open source projects often evolve through the community and create a single source of truth on how they can be used to support other products or services. To Sum It Up Open source development and decentralization have been core components of many technological advances over the past decade, and are now at the heart of the building and evolving AIOps. Based on Statista data, the market for AI business operations is projected to touch $10.8 billion by 2023 and $43.3 billion by 2025. With AI adoption growing across businesses, open source libraries and architecture will become more essential for future success. Of course, an organizational structure that supports building, supporting, and extending communities will always be at the heart of open source. The fact that we are able to create a large and scalable business on top of open source is wonderful. When organizations are built around open source, it makes sense to use a mix of organizations and individuals to effectively contribute back. Also, the most crucial part of it - creating impact and value will always be pivotal. Open source organizations are not mirrored images of others. The culture, leadership, and make-up of each organization are a little different. CEOs should have a different approach than any other leaders in the market. Consider your team's context, outlook, and expectations to create world-class open source offerings that bring financial success and growth. Leaders are needed to develop, maintain and expand the community. They foster open-source communities by encouraging and supporting their efforts towards growth and expansion, eventually paving the way for the proliferation and evolution of AIOps. Github, for the long haul, has served as an excellent example of a platform democratizer for building, supporting, and extending community support to coding – paving the way for open source ML model-based organizations such as Hugging Face. If Github was not acquired by Microsoft, its story could have been entirely different. Anyways, the acquisition doesn't influence the ideology of the organization nor does it define it. The major challenges for most decentralized and open source organizations remain to deal with external pressure to keep abreast with the ever-changing environment and align themselves for sustainability and long-term value; however, with continuous community support and competent leadership, these can be done away with. Open source organizations foster open-source communities by encouraging and supporting their efforts towards growth and expansion, eventually paving the way for the proliferation and evolution of AIOps. Once your IT Ops team determines that AIOps is the right move forward for your organization, your next step is to choose between a commercial or open-source tool. In today's world, open source and decentralized technologies have been recognized as valuable business models. But with this newfound appreciation comes the responsibility of choosing wisely. Evaluate the features and costs of many open-source and commercial AIOps solutions and ensure that you don’t compromise on your organizational security. Also, blending some open-source tools and commercial ones might address your organizational needs in the best way.

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How Open Source and Decentralization Lead to the Evolution of AIOps

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