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Artificial Intelligence for IT Operations (AIOps) is a practice that uses artificial intelligence and machine learning to enhance and automate various aspects of IT operations. It is designed to optimize IT environments by analyzing large volumes of data generated by complex IT systems, including system logs, performance metrics, and network data. AIOps aims to streamline IT workflows, predict potential issues, automate incident response, and ultimately improve the performance and efficiency of enterprise IT environments. [1]
The term refers to the multi-layered complex technology platforms which enhance and automate IT operations by using machine learning and analytics to analyze the large amounts of data collected from various ITOps devices and tools, automatically identifying and responding to issues in real-time.[ citation needed ]
With AIOps, you must shift from isolated IT data to aggregated observational data (e.g., job logs and monitoring systems) and interaction data (such as ticketing, events, or incident records) within a big data platform.[ citation needed ]
AIOps then applies machine learning and analytics to this data. The result is continuous visibility, which, combined with the implementation of automation, can lead to ongoing improvements. s.[ citation needed ]
AIOps connects three IT disciplines—automation, service management, and performance management—to achieve continuous visibility and improvement. This new approach in modern, accelerated, and hyperscaled IT environments leverages advances in machine learning and big data to overcome previous limitations. [2]
AI can optimize IT operations in five key ways: First, intelligent monitoring powered by AI helps identify potential issues before they cause outages, improving metrics like Mean Time to Detect (MTTD) by 15-20%. Second, performance data analysis and insights enable quick decision-making by ingesting and analyzing large data sets in real time. Third, AI-driven automated infrastructure optimization efficiently allocates resources and reduces cloud costs. Fourth, enhanced IT service management reduces critical incidents by over 50% through AI-driven end-to-end service management. Lastly, intelligent task automation accelerates problem resolution and automates remedial actions with minimal human intervention. [3]
AIOps tools use big data analytics, machine learning algorithms, and predictive analytics to detect anomalies, correlate events, and provide proactive insights. This automation reduces the burden on IT teams, allowing them to focus on strategic tasks rather than routine operational issues. AIOps is widely used by IT operations teams, DevOps, network administrators, and IT service management (ITSM) teams to enhance visibility and enable quicker incident resolution in hybrid cloud environments, data centers, and other IT infrastructures. [1]
In contrast to MLOps (Machine Learning Operations), which focuses on the lifecycle management and operational aspects of machine learning models, AIOps focuses on optimizing IT operations using a variety of analytics and AI-driven techniques. While both disciplines rely on AI and data-driven methods, AIOps primarily targets IT operations, whereas MLOps is concerned with the deployment, monitoring, and maintenance of ML models. [4]
Customer service is the assistance and advice provided by a company through phone, online chat, mail, and e-mail to those who buy or use its products or services. Each industry requires different levels of customer service, but towards the end, the idea of a well-performed service is that of increasing revenues. The perception of success of the customer service interactions is dependent on employees "who can adjust themselves to the personality of the customer". Customer service is often practiced in a way that reflects the strategies and values of a firm. Good quality customer service is usually measured through customer retention.
Decision management, also known as enterprise decision management (EDM) or business decision management (BDM) entails all aspects of designing, building and managing the automated decision-making systems that an organization uses to manage its interactions with customers, employees and suppliers. Computerization has changed the way organizations are approaching their decision-making because it requires that they automate more decisions, to handle response times and unattended operation required by computerization, and because it has enabled "information-based decisions" – decisions based on analysis of historical behavioral data, prior decisions, and their outcomes.
Operational intelligence (OI) is a category of real-time dynamic, business analytics that delivers visibility and insight into data, streaming events and business operations. OI solutions run queries against streaming data feeds and event data to deliver analytic results as operational instructions. OI provides organizations the ability to make decisions and immediately act on these analytic insights, through manual or automated actions.
Samsung SDS Co., Ltd., Established in 1985 as a subsidiary of Samsung Group, is a provider of Information Technology (IT) services, including consulting, technical, and outsourcing services. SDS is also active in research and development of emerging IT technologies such as Artificial Intelligence (AI), Blockchain, Internet of Things (IoT) and outsourcing in engineering. In 2019, Samsung SDS reported a net profit of 750.4 billion won, an increase of 17.5% year-on-year. The company is estimated to have the 11th most valuable brand among global IT service companies, at US$3.7 billion as of January 2020. Samsung SDS has headquarters in South Korea and eight other overseas subsidiaries, one in America, Asia-Pacific, China, Europe, Latin America, Middle East, India, and Vietnam.
Business process management (BPM) is the discipline in which people use various methods to discover, model, analyze, measure, improve, optimize, and automate business processes. Any combination of methods used to manage a company's business processes is BPM. Processes can be structured and repeatable or unstructured and variable. Though not required, enabling technologies are often used with BPM.
Marine technology is defined by WEGEMT as "technologies for the safe use, exploitation, protection of, and intervention in, the marine environment." In this regard, according to WEGEMT, the technologies involved in marine technology are the following: naval architecture, marine engineering, ship design, ship building and ship operations; oil and gas exploration, exploitation, and production; hydrodynamics, navigation, sea surface and sub-surface support, underwater technology and engineering; marine resources ; transport logistics and economics; inland, coastal, short sea and deep sea shipping; protection of the marine environment; leisure and safety.
In a business context, operational efficiency is a measurement of resource allocation and can be defined as the ratio between an output gained from the business and an input to run a business operation. When improving operational efficiency, the output to input ratio improves.
Sumo Logic, Inc. is a cloud-based machine data analytics company focusing on security, operations and BI use-cases. It provides log management and analytics services that use machine-generated big data. Sumo Logic was founded in April 2010 by ArcSight veterans Kumar Saurabh and Christian Beedgen, and is headquartered in Redwood City, California.
In the fields of Information Technology (IT) and Systems Management, IT operations analytics (ITOA) is an approach or method to retrieve, analyze, and report data for IT operations. ITOA may apply big data analytics to large datasets to produce business insights. In 2014, Gartner predicted its use might increase revenue or reduce costs. By 2017, it predicted that 15% of enterprises will use IT operations analytics technologies.
Dynatrace, Inc. is a global technology company that provides a software observability platform based on artificial intelligence (AI) and automation. Dynatrace technologies are used to monitor, analyze, and optimize application performance, software development and security practices, IT infrastructure, and user experience for businesses and government agencies throughout the world.
Gigamon is a privately held computer security company with products that delivers network-derived intelligence and insights to cloud, security, observability, and network management tools. It is one of the main parts in the deep observability market. Formerly traded publicly, it is now owned by Elliott Management and headquartered in Santa Clara, California.
Industrial big data refers to a large amount of diversified time series generated at a high speed by industrial equipment, known as the Internet of things. The term emerged in 2012 along with the concept of "Industry 4.0”, and refers to big data”, popular in information technology marketing, in that data created by industrial equipment might hold more potential business value. Industrial big data takes advantage of industrial Internet technology. It uses raw data to support management decision making, so to reduce costs in maintenance and improve customer service. Please see intelligent maintenance system for more reference.
MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous delivery practice (CI/CD) of DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics.
ModelOps, as defined by Gartner, "is focused primarily on the governance and lifecycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models". "ModelOps lies at the heart of any enterprise AI strategy". It orchestrates the model lifecycles of all models in production across the entire enterprise, from putting a model into production, then evaluating and updating the resulting application according to a set of governance rules, including both technical and business key performance indicators (KPI's). It grants business domain experts the capability to evaluate AI models in production, independent of data scientists.
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Artificial Intelligence engineering is a tech discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions. It merges aspects of data engineering and software engineering to create real-world applications in diverse domains such as healthcare, finance, autonomous systems, and industrial automation.