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In this workshop, we’ll talk about how using ML life cycle managers can speed up our work. Data science projects are, by definition, interdisciplinary, comprising teams with diverse levels of computing competence and experience. The data and analysis lifecycle management process is crucial to the reproducibility and sustainability of any DS effort. Any DS project must keep track of the results of exhaustive testing, as well as the associated parameters, metrics, artifacts, source code, and package dependencies. Individuals or teams of data scientists can use tools like MLFlow to create robust and reproducible machine learning pipelines. Due to its comprehensive support for a variety of machine learning frameworks and languages, including Python, R, and Java, MLFlow is designed to be easily incorporated into existing systems. The tracking feature enables developers to save all aspects of an experiment or model, from the code version to the model’s parameters and metrics. It is compatible with widely used environmental management frameworks, such as CONDA and DOCKER. Using the visual execution tool, a data scientist may easily recreate tracking processes. Additionally, MLFlow is an open-source project, ensuring the tool’s continued availability.
This is a hybrid workshop with the Zoom link(link is external) and Password: 147598.