References
- Dowling, J. (2024a, July 11). From MLOps to ML Systems with Feature/Training/Inference Pipelines. Hopsworks. https://www.hopsworks.ai/post/mlops-to-ml-systems-with-fti-pipelines
- Dowling, J. (2024b, August 5). Modularity and Composability for AI Systems with AI Pipelines and Shared Storage. Hopsworks. https://www.hopsworks.ai/post/modularity-and-composability-for-ai-systems-with-ai-pipelines-and-shared-storage
- Joseph, M. (2024, August 23). The Taxonomy for Data Transformations in AI Systems. Hopsworks. https://www.hopsworks.ai/post/a-taxonomy-for-data-transformations-in-ai-systems
- MLOps: Continuous delivery and automation pipelines in machine learning. (2024, August 28). Google Cloud. https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
- Qwak. (2024a, June 2). CI/CD for Machine Learning in 2024: Best Practices to build, test, and Deploy | Infer. Medium. https://medium.com/infer-qwak/ci-cd-for-machine-learning-in-2024-best-practices-to-build-test-and-deploy-c4ad869824d2
- Qwak. (2024b, July 23). 5 Best Open Source Tools to build End-to-End MLOPs Pipeline in 2024. Medium. https://medium.com/infer-qwak/building-an-end-to-end-mlops-pipeline-with-open-source-tools-d8bacbf4184f
- Salama, K., Kazmierczak, J., & Schut, D. (2021). Practitioners guide to MLOPs: A framework for continuous delivery and automation of machine learning (1st ed.) [PDF]. Google Cloud. https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
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