What Is AIOps &#038; Why It Is The Future Of IT Operations
The term AIOps which means “algorithmic IT operations” was coined by a research firm refers to the integration of analytics and machine learning for scaling and improving various IT operations. AIOps is positioned as a continuous integration and continuous deployment for core IT functions and has two main components — big data and ML. It denotes a shift from siloed data to a more dynamic business environment which will be crucial for digital transformation. AIOps platforms are popularly defined as software systems that combine big data and artificial intelligence functionality to enhance or phase out a broad range of IT operations processes and tasks, related to analysis, performance monitoring, IT service management and automation. AIOps is a new platform approach to overhaul the traditional IT operations and performance monitoring so that businesses are free to innovate on new products, services, applications, partnerships and technologies within the digital enterprise. A research had recently predicted that by 2022, 40% of all large enterprises will combine big data and ML functionalities to support and partially replace monitoring, service desk and automation processes. The key function of AIOps platform is to refine traditional IT tools and processes which are out of sync with today’s industry demands. Current business needs require enterprises to move away from siloed databases, tools and lack of interoperability to cut down on costs and manage shifting budgets. How Is AIOps Platform Different Than A DevOps Platform AIOps platform works real-time, feature dynamic pattern identification on vast amounts of data created by innovation-enabling technologies like bursting microservices and hybrid IT infrastructure, without the need for human intervention. It also has the capability to organise and analyse according to data sources that traditional processes, driven by functional silos, are unable to understand. AIOps platforms consist of a big data platform which combines data from all sources. Some of the capabilities of AIOps platform is historical data management, streaming data management, log data ingestion and many analytics capabilities. Through the platform, IT team members can apply analytics and leverage visualisation capabilities also to view the insights. A key part of the AIOps platform is unsupervised ML algorithms which are used for algorithmic analysis. The key difference between DevOps and AIOps is that the latter is a multi-layered platform which can automatically automate traditional IT operations and also has key components such as ML algorithms which enable algorithmic analysis. On the other hand, DevOps platforms are leveraged for agile development methodologies and it is used to automate self-service operations. DevOps platforms essentially automate the build deployment and integration process through containers and open source automation server tools. But there are certain areas in which DevOps fails are system operations, security and compliance. DevOps basically streamlines the build process through CI/CD pipelines while AIOps provides a scalable framework for automation and management framework. AIOps will soon phase out the way businesses develop and deploy applications by automating tasks. AIOps will play a crucial role is because the next generation of enterprise applications are running on multiple cloud platforms and data integration is tough. AIOps Is The Future Of DevOps Even though DevOps has become the de facto standard for automation, AIOps is being positioned as the next generation of DevOps since it reduces dependencies on specific tools. Since data changes, algorithms can help in bug-tracking and issue tracking services. AIOps And Business Integration As discussed earlier, businesses need to move away from traditional IT operations and enable timely problem identification, by assessing the behaviour of infrastructure. AIOps can monitor behaviour at the edge of infrastructure and also keep cost controls in check by dynamically managing public cloud utilisation. Since AIOps platforms draw from huge data sources, this also enables a unification of multiple data sources and IT resources and also arms the IT teams to have the right set of tools do this. AIOps helps in aligning the data resources to optimize work processes. This will also improve the quality of data fed into the machine learning systems. Outlook According to experts, AIOps is expected to become the next big thing in IT management and AI-led correlation can also execute a series of change in IT operations and overall business performance. AIOps platform will also play a pivotal role in overcoming the complexities of the current enterprise IT operations model and also pave the way for digital transformations.
The term AIOps which means “algorithmic IT operations” was coined by a research firm refers to the integration of analytics and machine learning for scaling and improving various IT operations. AIOps is positioned as a continuous integration and continuous deployment for core IT functions and has two main components — big data and ML. It […]
AIOps is positioned as a continuous integration and continuous deployment for core IT functions and has two main components — big data and ML. It denotes a shift from siloed data to a more dynamic business environment which will be crucial for digital transformation
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["Go", "machine learning", "artificial intelligence", "AI", "ML", "Scala", "RAG", "microservices", "analytics", "R"]
["AI", "artificial intelligence", "machine learning", "ML", "analytics", "RAG", "microservices", "R", "Go", "Scala"]
Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.