except Control when jobs are not created. environment Name of an environment to which the job deploys. dependencies Restrict which artifacts are passed to a specific job by providing a list of jobs to fetch artifacts from. dast_configuration Use configuration from DAST profiles on a job level. coverage Code coverage settings for a given job. cache List of files that should be cached between subsequent runs. before_script Override a set of commands that are executed before job. artifacts List of files and directories to attach to a job on success. A failed job does not cause the pipeline to fail. Jobs configured with job keywords: Keyword Description after_script Override a set of commands that are executed after job. workflow Control what types of pipeline run. variables Define CI/CD variables for all job in the pipeline. stages The names and order of the pipeline stages. include Import configuration from other YAML files. Global keywords that configure pipeline behavior: Keyword Description default Custom default values for job keywords. KeywordsĪ GitLab CI/CD pipeline configuration includes: If you are editing content on this page, follow the instructions for documenting keywords. gitlab-ci.yml file, you can validate it with the gitlab-ci.yml file, try a tutorial that demonstrates a gitlab-ci.yml file used in an enterprise, see the For a collection of examples, see GitLab CI/CD examples.This document lists the configuration options for your GitLab. Globally-defined image, services, cache, before_script, after_script.(Example: Identifying and anticipating cyber attacks before they occur). Securing a business from risks such as fraud and cyber – improving quality and consistency while enabling greater transparency to enhance brand trust. (Example: Recommending new product concepts and features based on customer needs and preferences mined from social media). Redefining where to play and how to win by using AI to enable innovative new products, markets, and business models. (Example: Using conversational bots that can understand and respond to customer sentiment to address customer (Example: Reducing factory downtime by predicting machinery maintenance needs).Ĭhanging the way people interact with technology, enabling businesses to engage with people on human terms rather than forcing humans to engage on machine terms. Improving understanding and decision making through analytics that are more proactive, predictive, and able to see patterns in increasingly complex sources. (Example: Accelerating the process of drug approval by using predictive insights to create a synthetic trial). Reducing the time required to achieve operational and business results by minimizing latency. (Example: Automating data entry and patient appointment scheduling Looking across all AI use cases, there are generally six major ways that AI can create value for a business:Īpplying AI and intelligent automation solutions to automate tasks that are relatively low value and often repetitive, reducing costs through improved efficiency and quality. However, reading through this collection should give you a much clearer sense of what AI is capable of achieving in aīusiness context-now, and over the next several years-so you can make smart decisions about when, where, and how to deploy AI within your own organization (and how much time, money, and attention you should be investing in it today). Of course, the best uses for AI vary from one organization to the next, and there are many compelling use cases for AI beyond the ones highlighted here. The dossierĪlso includes several emerging AI use cases for each industry that are expected to have a major impact in the future. Each use case features a summary of the key business issues and opportunities, how AI can help, and the benefits that are likely to be achieved. This dossier highlights dozens of the most compelling, business-ready use cases for AI across six major industries. Significant questions about what AI can actually do for their businesses. Yet, amidst the current frenzy of AI advancement and adoption, many leaders and decisionmakers still have After decades as science fiction fantasy, artificial intelligence (AI) has made the leap to practical reality and is quickly becoming a competitive necessity.
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