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MLOps
MLOps or ML Ops is a set of practices 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 development practice 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 (software development lifecycle, continuous integration/continuous delivery), orchestration, an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ModelOps
ModelOps (model operations), as defined by Gartner, "is focused primarily on the governance and life cycle 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 life cycles 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 KPI's. It grants business domain experts the capability to evaluate AI models in production, independent of data scientists. A Forbes article promoted ModelOps: "As enterprises scale up their AI initiatives to become a true Enterprise AI organization, having full operationalized analytics capability puts ModelOps in the center, connecting both DataOps and D ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ML Ops Venn Diagram
ML or ml may refer to: Computing and mathematics * ML (programming language), a general-purpose functional programming language * .ml, the top-level Internet domain for Mali * Machine language, the direct instructions to a computer's central processing unit (CPU) * Machine learning, a field of computer science * Markup language, a system for annotating a document * Maximum likelihood, a method of estimating the parameters of a statistical model * Mathematical Logic, a variation of Quine's system New Foundations Measurement * Megalitre or megaliter (ML, Ml, or Mℓ), a unit of capacity * Millilitre or milliliter (mL, ml, or mℓ), a unit of capacity * Millilambert (mL), a non-SI unit of luminance * Richter magnitude scale (''M''L), used to measure earthquakes * Megalangmuir (ML), a unit of exposure of a surface to a given chemical species (convention is 1 ML=monolayer=1 Langmuir) Other * 1050, in Roman numerals * ''ML'' (film), a 2018 Philippine film * ML 8-inch shell gun * ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DevOps
DevOps is a set of practices that combines software development (''Dev'') and IT operations (''Ops''). It aims to shorten the systems development life cycle and provide continuous delivery with high software quality. DevOps is complementary to agile software development; several DevOps aspects came from the ''agile'' way of working. Definition Other than it being a cross-functional combination (and a portmanteau) of the terms and concepts for "development" and "operations", academics and practitioners have not developed a universal definition for the term "DevOps". Most often, DevOps is characterized by key principles: shared ownership, workflow automation, and rapid feedback. From an academic perspective, Len Bass, Ingo Weber, and Liming Zhu—three computer science researchers from the CSIRO and the Software Engineering Institute—suggested defining DevOps as "a set of practices intended to reduce the time between committing a change to a system and the change being placed i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DataOps
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations. DataOps incorporates the Agile methodology to shorten the cycle time of analytics development in alignment with business goals. DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of software. This merging of software ''development'' and IT ''operations'' has improved velocity, quality, predictability and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Software Development Lifecycle
In software engineering, a software development process is a process of dividing software development work into smaller, parallel, or sequential steps or sub-processes to improve design, product management. It is also known as a software development life cycle (SDLC). The methodology may include the pre-definition of specific deliverables and artifacts that are created and completed by a project team to develop or maintain an application. Most modern development processes can be vaguely described as agile. Other methodologies include waterfall, prototyping, iterative and incremental development, spiral development, rapid application development, and extreme programming. A life-cycle "model" is sometimes considered a more general term for a category of methodologies and a software development "process" a more specific term to refer to a specific process chosen by a specific organization. For example, there are many specific software development processes that fit the spiral ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Continuous Delivery
Continuous delivery (CD) is a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time and, following a pipeline through a "production-like environment", without doing so manually. It aims at building, testing, and releasing software with greater speed and frequency. The approach helps reduce the cost, time, and risk of delivering changes by allowing for more incremental updates to applications in production. A straightforward and repeatable deployment process is important for continuous delivery. Continuous delivery contrasts with continuous deployment Continuous deployment (CD) is a software engineering approach in which software functionalities are delivered frequently and through automated deployments. Continuous deployment contrasts with continuous delivery (also abbreviated CD), a similar ... (also abbreviated CD), a similar approach in which software is also produced in short cycles bu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gartner
Gartner, Inc is a technological research and consulting firm based in Stamford, Connecticut that conducts research on technology and shares this research both through private consulting as well as executive programs and conferences. Its clients include large corporations, government agencies, technology companies, and investment firms. In 2018, the company reported that its client base consisted of over 12,000 organizations in over 100 countries. As of 2022, Gartner has over 15,000 employees located in over 100 offices worldwide. It is a member of the S&P 500. History Gideon Gartner founded Gartner, Inc in 1979. Originally private, the company launched publicly as Gartner Group in 1986 before Saatchi & Saatchi acquired it in 1988. In 1990, Gartner Group was acquired by some of its executives, including Gartner himself, with funding from Bain Capital and Dun & Bradstreet. The company went public again in 1993. In 2000, the name was simplified from ''Gartner Group'' to Gartn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence For IT Operations
Artificial Intelligence for IT Operations (AIOps) is a term coined by Gartner in 2016 as an industry category for machine learning analytics technology that enhances IT operations analytics. AIOps is the acronym of "Artificial Intelligence Operations". Such operation tasks include automation, performance monitoring and event correlations among others. There are two main aspects of an AIOps platform: machine learning and big data. In order to collect observational data and engagement data that can be found inside a big data platform and requires a shift away from sectionally segregated IT data, a holistic machine learning and analytics strategy is implemented against the combined IT data. The goal is to enable IT transformation, receive continuous insights which provide continuous fixes and improvements via automation. This is why AIOps can be viewed as CI/CD for core IT functions. Given the inherent nature of IT operations, which is closely tied to cloud deployment and the man ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
