Machine Learning (ML) has become ubiquitous, fueling data-driven applications
across various organizations. Contrary to the traditional perception of ML in
research, ML workflows can be complex, resource-intensive, and time-consuming.
Expanding an ML workflow to encompass a wider range of data infrastructure and
data types may lead to larger workloads and increased deployment costs.
Currently, numerous workflow engines are available (with over ten being widely
recognized). This variety poses a challenge for end-users in terms of mastering
different engine APIs. While efforts have primarily focused on optimizing ML
Operations (MLOps) for a specific workflow engine, current methods largely
overlook workflow optimization across different engines.
In this work, we design and implement Couler, a system designed for unified
ML workflow optimization in the cloud. Our main insight lies in the ability to
generate an ML workflow using natural language (NL) descriptions. We integrate
Large Language Models (LLMs) into workflow generation, and provide a unified
programming interface for various workflow engines. This approach alleviates
the need to understand various workflow engines' APIs. Moreover, Couler
enhances workflow computation efficiency by introducing automated caching at
multiple stages, enabling large workflow auto-parallelization and automatic
hyperparameters tuning. These enhancements minimize redundant computational
costs and improve fault tolerance during deep learning workflow training.
Couler is extensively deployed in real-world production scenarios at Ant Group,
handling approximately 22k workflows daily, and has successfully improved the
CPU/Memory utilization by more than 15% and the workflow completion rate by
around 17%.

通过使用自然语言描述生成机器学习工作流，Couler 系统实现了云端的统一机器学习工作流优化，并通过自动缓存、自动并行化和自动超参数调整等方式提高了工作流计算效率，降低了冗余计算成本和提高了深度学习工作流训练的容错性。