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Kubeflow is a Kubernetes-native, open source platform that simplifies ML workflow management on Kubernetes. It handles the complexities of containerization and supports end-to-end pipeline automation and distributed training on large datasets, making it ideal for production-grade ML systems.
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Judoscale
Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.
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Unlike the other tools, Feast solves a different issue: the management of ML feature data. 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. Typically, it integrates with your data sources to streamline management.
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Prometheus handles everything related to alerting and monitoring your metrics. As an open source monitoring platform tool, it allows AI developers and ML engineers to gain insights into their Infrastructures, create custom dashboards, and monitor their ML workflows in real time.
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MLflow provides developers with comprehensive tools for managing the entire ML lifecycle. Its four primary components—tracking, models, projects, and model registry—facilitate efficient, reproducible, and scalable ML pipeline building.
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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. With its Git-like interface, it integrates seamlessly with existing Git repositories. It supports various cloud storage like AWS S3 and Azure Blob, thus enabling versioning of large datasets without bloating your Git repositories.
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Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools.
