awesome-ml-pipelines and awesome-ml-experiment-management
These are complements: ML pipelines orchestrate the execution of workflows, while experiment management tracks and compares the results and metrics produced by those pipeline runs.
About awesome-ml-pipelines
awesome-mlops/awesome-ml-pipelines
A curated list of awesome open source tools and commercial products that will help you manage machine learning and data-science workflows and pipelines 🚀
This is a curated collection of tools and products that help data scientists and machine learning engineers manage and automate their complex data and machine learning workflows. It provides options for orchestrating tasks, scheduling jobs, and monitoring the entire lifecycle of a machine learning project, from data ingestion to model deployment. The resources help you build robust, repeatable, and scalable machine learning pipelines.
About awesome-ml-experiment-management
awesome-mlops/awesome-ml-experiment-management
A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management 🚀
When you're developing machine learning models, you often run many experiments with different datasets, model architectures, and parameters. This resource helps you keep track of all those experiments, including the inputs, configurations, and results, so you can easily compare them and understand what worked best. It's for anyone involved in developing and iterating on machine learning models, from individual data scientists to ML engineering teams.
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