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QFlow: Solution for Managing ML Projects
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Executive summary
Alexander KnopJanuary 9th, 2024

In recent years, the rapid growth of AI technologies has resulted in an increasing number of organizations adopting machine learning techniques to solve complex business problems. However, the process of building and deploying ML models at scale can be challenging, involving numerous complex steps such as data preparation, feature engineering, model training, and deployment.

Various ML frameworks have emerged to address these challenges. This document will present a framework that provides a structured approach to managing ML projects, enabling teams to collaborate more efficiently, improve code quality, and automate various tasks. Like popular frameworks such as Kedro, Flyte, and SageMaker, this framework provides an opinionated structure to develop, maintain, and scale ML projects. The framework emphasizes modularity, reproducibility, and versioning, enabling teams to track changes easily and reproduce results.

In the following sections, we will dive into this framework's key features and benefits, along with examples and best practices for implementing it in your ML projects.

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About the author
Alexander Knop
Alexander Knop
Principal Mathematician

Alexander Knop is a dynamic professional navigating the realms of academia and technology, with a passion for bridging the gap between theoretical knowledge and practical application.

Alexander received his PhD from Steklov Institute of Mathematics and specialized in theoretical computer science and mathematical logic. As an Assistant Professor at UC San Diego, Alexander dedicated three impactful years to shaping the minds of future professionals in the fields of Mathematics and Computer Science.

At Quantori, Alexander is a Principal Mathematician delving into the complexities of mathematical and machine learning research or engineering solutions at the intersection of science and technology.

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