Machine learning workflows and pipelines are vital components that facilitate the automation and management of tasks in machine learning projects, encompassing phases such as data collection, pre-processing, model training, evaluation, and deployment. The blog post discusses over ten tools designed for orchestrating these workflows and pipelines, highlighting the benefits of automation and efficient resource management. Tools like Kale, Flyte, MLRun, ZenML, Prefect, Argo, and others provide an open-source interface for creating scalable, maintainable, and reproducible workflows, enhancing the productivity of data scientists by focusing on essential tasks. These orchestration tools help streamline the machine learning process by automating dependencies, tracking experiments, and managing infrastructure, ultimately supporting the rapid deployment and monitoring of ML models. Most of these tools are open-source, allowing for experimentation without financial obligations, and are built to integrate seamlessly with various frameworks, making them versatile and adaptable to different project needs.