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10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
- The 2024 Dora Report emphasizes the significant impact of Platform Engineering, increasing deployment frequency by 60%, developer productivity by 8%, and overall team performance by 10%.
- Here's a quick list of Platform Engineering tools I recommend to simplify AI/ML workflows and reduce infrastructure complexity: 1. KitOps, 2. Kubeflow, 3. Data Version Control (DVC), 4. Seldon Core, 5.BentoML, 6. Apache Airflow, 7. Prometheus, 8. Comet, 9. MLflow, and 10. Feast.
- KitOps simplifies ML workflows with reusable components, centralized versioning, and secure ModelKit packaging.
- Kubeflow is a Kubernetes-native, open source platform that simplifies ML workflow management on Kubernetes.
- Data Version Control is a powerful version control tool tailored for ML workflows. It ensures reproducibility by tracking and sharing data, pipelines, experiments, and models.
- Seldon Core addresses the complexity of Kubernetes by enabling ML engineers to deploy models at scale without requiring Kubernetes expertise.
- BentoML is a Platform Engineering tool designed to deploy machine learning models at scale and build production-grade AI systems using any open source or custom fine-tuned models.
- Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python.
- Prometheus handles everything related to alerting and monitoring your metrics.
- Feast simplifies the features management by storing and managing the code used to generate machine learning features, and facilitates the deployment of these features into production.
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