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The repository also features architecture specifically designed for ComputerVision (CV) and Natural Language Processing (NLP) use cases. Security: We have included steps and best practices from GitHub’s advanced security scanning and credential scanning (also available in Azure DevOps) that can be incorporated into the workflow.
MLOps workflows for computervision and ML teams Use-case-centric annotations. Data storage and versioning You need data storage and versioning tools to maintain data integrity, enable collaboration, facilitate the reproducibility of experiments and analyses, and ensure accurate ML model development and deployment.
The DevOps and Automation Ops departments are under the infrastructure team. They also need to monitor and see changes in the data distribution ( datadrift, concept drift , etc.) Data infrastructure and tool stack for Brainly’s visual search team “Our data stack varies from one project to another.
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