← Introduction to Machine Learning
AI Systems
Resources
Books
- Kleppmann, M. (2017). Designing Data-Intensive Applications. O’Reilly Media
- Huyen, C. (2022). Designing Machine Learning Systems. O’Reilly Media
Papers and Reports
- Sculley, D., et al. (2015). Hidden technical debt in machine learning systems
- Breck, E., et al. (2016). The ML test score: A rubric for ML production readiness and technical debt reduction
- Paleyes, A., et al. (2020). Challenges in deploying machine learning: a survey of case studies
- Cabrera, C., et al. (2022). MAACO: A Dynamic Service Placement Model for Smart Cities
- Cabrera, C., et al. (2023). Machine Learning Systems: A survey from a Data-Oriented Perspective
- Cabrera, C., et al. (2025). The Systems Engineering approach in times of Large Language Models
Web
- Deploying Machine Learning using Flask
- MLOps: Continuous delivery and automation pipelines in machine learning
- Kubeflow: Machine Learning Toolkit for Kubernetes
- MLflow: An open source platform for the machine learning lifecycle
- TensorFlow Serving
- TorchServe: Model serving for PyTorch
- Seldon Core: Cloud native machine learning deployment
- Weights & Biases: MLOps platform
- Neptune.ai: Experiment tracking and model registry