The AgileML™ (Agile Modeling Language™) is a Lean (as in Lean manufacturing and methods) extended subset of the SysML system architecture modeling language standard that is used for precisely specifying scalable and simulatable Agile Architectures and Designs.

2019年5月15日 · Agile ML kept the data science team focused on outcomes by treating the model as a product and iteratively adding features. The build and deploy sprint structure perfectly integrates to a...

2023年10月28日 · Agile in AI (Artificial Intelligence) and Machine Learning (ML) Projects intends to describe a framework for rapid prototyping, iterative development, and close collaboration between developers, data scientists, and stakeholders.

Machine learning (ML) project deployments often have long lead times and may face delays or failures due to lack of data, poor data quality, and data drift. To address these problems, we introduce AgileML, a novel machine learning product development lifecycle where the end consumer and development team work collaboratively through an iterative ...

2021年2月25日 · Developing ML with agile has a few challenges that new teams coming up in the space need to be prepared for - from new roles like data scientists to concerns in reproducibility and dependency...

2024年3月5日 · As our data team has become more proficient with using machine learning (ML) as a solution, we ’ve created a recommended process to manage risk, enable cross-team learning, and encourage best practices. Our framework is grounded in Agile software development principles.

2021年11月1日 · To address these problems, we introduce AgileML, a novel machine learning product development lifecycle where the end consumer and development team work collaboratively through an iterative...

2022年9月28日 · In this article, we will use Agile as inspiration to structure modeling processes that are traditionally experimental. In the follow-up post, we will discuss details of a proposed modeling life cycle structured on Agile principles and reactive risk management.

Agile project management is a flexible and iterative approach that emphasizes collaboration, adaptability, and continuous improvement. Integrating AI and ML into Agile methodologies can enhance project outcomes, streamline processes, and optimize decision-making.

To solve the foregoing challenges, we introduce AgileML, a novel pipeline that engages end-users throughout an iterative development cycle, beginning with initial model experimen-tation. The...