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Recent advances in deep learning facilitate the training, testing, and deployment of models through so-called pipelines. Those pipelines are typically orchestrated with general-purpose machine learning frameworks (e.g., Tensorflow Extended), where developers manually call the single steps for each task-specific application. The diversity of task- and technology-specific requirements in deep learning projects increases the orchestration effort. There are recent advances to automate the orchestration with machine learning, which are however, still immature and do not support task-specific applications. Hence, we claim that partial automation of pipeline orchestration with respect to specific tasks and technologies decreases the overall development effort. We verify this claim with the ALOHA tool flow, where task-specific glue code is automated. The gains of the ALOHA tool flow pipeline are evaluated with respect to human effort, computing performance, and security. |
