Machine Learning Pipeline using Argo workflow

Argo Workflows is an open-source, container-native workflow engine for orchestrating parallel jobs on Kubernetes. Key points about Argo Workflows include:

MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering to streamline the entire lifecycle of machine learning models in production. It focuses on automating and monitoring all steps of ML system construction, including integration, testing, releasing, deployment, and infrastructure management.

Machine Learning pipelines are a key component of MLOps. They are structured workflows that automate the process of building, training, evaluating, and deploying machine learning models. ML pipelines typically include steps such as:

1. Data collection and preprocessing 2. Feature engineering and selection 3. Model training and hyperparameter tuning 4. Model evaluation and validation 5. Model deployment and monitoring

By incorporating ML pipelines, MLOps aims to bridge the gap between model development and production deployment, ensuring that ML models are reliable, scalable, and maintainable in real-world applications.

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