Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. Our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

The widely adopted Business Process Model and Notation (BPMN) is a cornerstone of industry standards for business process modeling. However, its ambiguous execution semantics often result in inconsistent interpretations, depending on the software used for implementation. In response, the Process Specification Language (PASS) provides formally defined semantics to overcome these interpretational challenges. Despite its clear advantages, PASS has not reached the same level of industry penetration as BPMN. This feasibility study proposes using PASS as an intermediary framework to translate and execute BPMN models. It describes the development of a prototype translator that converts specific BPMN elements into a format compatible with PASS. These models are then transformed into source code and executed in a bespoke workflow environment, marking a departure from traditional BPMN implementations. Our findings suggest that integrating PASS enhances compatibility across different modeling and execution tools and offers a more robust methodology for implementing business processes across organizations. This study lays the groundwork for more accurate and unified business process model executions, potentially transforming industry standards for process modeling and execution.

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%.

In a recent blog post, Google announced the beta of Cloud AI Platform Pipelines, which provides users with a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. With Cloud AI Pipelines, Google can help organizations adopt the practice of Machine Learning Operations, also known as MLOps – a term for applying DevOps practices to help users automate, manage, and audit ML workflows. Typically, these practices involve data preparation and analysis, training, evaluation, deployment, and more. When you're just prototyping a machine learning (ML) model in a notebook, it can seem fairly straightforward. But when you need to start paying attention to the other pieces required to make an ML workflow sustainable and scalable, things become more complex.

Throughout this series on Artificial General Intelligence, one term keeps popping up without much in the way of an explanation, the Workflow. What exactly is a workflow and what is its role in an AGI? A workflow is a high-level description of a series of steps and sequences to be performed in order to achieve a pre-defined outcome. The above diagram describes a series of discreet activities which must be performed to'Update Weather'. Where we encounter branching, these activities can be performed in parallel.

Sramana Mitra: Can you take us through some use cases in your customer scenarios where you are seeing these kinds of impact? Mark Mullison: Just a little more context and then I'll get to the specifics. There are three big differentiators in Helios's platform. The first is a proprietary attribute model that we use to store all the knowledge about the site. We organize that in a special way. You might think, "Isn't that just a big database?

Journal of Machine Learning Research 1 (2019) 1-48 Submitted 8/19; Published 10/00 HyperStream: a Workflow Engine for Streaming Data Tom Diethe tdiethe@amazon.com Intelligent Systems Laboratory, University of Bristol, BS8 1UB, UK Editor: A. N. Other Abstract This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitations of other computational engines and provides high-level interfaces to execute complex nesting, fusion, and prediction both in online and offline forms in streaming environments. HyperStream is a general purpose tool that is well-suited for the design, development, and deployment of Machine Learning algorithms and predictive models in a wide space of sequential predictive problems. Introduction Scientific workflow systems are designed to compose and execute a series of computational or data manipulation operations (workflow) (Deelman et al., 2009).