In a world full of data, a company’s ability to compete is measured by how successfully it applies analytics to vast, unstructured datasets and gains insights that can be translated into business value. Data science has been growing steadily over the past years and has become an important field of study to achieve such a competitive advantage.
Developing a system that can be used to generate business value is inherently complex, and traditionally, data scientists are confronted with several challenges that can prevent them from ever reaching deployment.
To efficiently address these challenges, machine learning (ML) workflow tools have been developed, focused on managing the entire ML lifecycle and supporting users throughout the development process. However, as these tools are relatively new, there is currently no standard among these tools, making it difficult to decide which tool serves which purpose the best.
This white paper proposes a framework that can be used to compare ML workflow tools, allowing users to identify their strengths and weaknesses. The framework consists of eight dimensions that are used to assess a tool’s ability to address a particular lifecycle challenge as well as eight dimensions that assess whether the tool fits into an organization’s current infrastructure. By using this framework, an organization can choose the right workflow tool and work towards an efficient machine learning development cycle. We applied the framework to five existing workflow tools: Metaflow, Kedro, MLflow, Kubeflow and Azure Machine Learning, and compared them to identify their strengths and weaknesses.
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