Past Achievements and a Bright Future
In the previous blog we recapped Feast’s journey over the last 6 years and hinted about what is coming in the future. We also announced a new group of maintainers that joined the project to help drive it to the 1.0 milestone. Today, we will drill down a little bit into the goals for the project towards that milestone.
The Goals for Feast 1.0
- Tighter Integration with Kubeflow: Recognizing the growing importance of Kubernetes in the ML workflow, a primary objective is to achieve a closer integration with Kubeflow. This will enable smoother workflows and enhanced scalability for ML projects.
- Development of Enterprise Features: With the aim to make Feast more robust for enterprise usage, we are focusing on developing features that cater to the complex needs of large-scale organizations. These include advanced security measures, scalability enhancements, and improved data management capabilities.
- Graduation from LF AI and Data Foundation Incubation: Currently incubating under the LF AI and Data Foundation, we are setting our sights on graduating Feast to become a fully-fledged project under the foundation. This step will mark a significant milestone in our journey, recognizing the maturity and stability of Feast.
- Research and Development for Novel Use Cases: Keeping pace with the rapidly evolving ML landscape (e.g., Large Language Models and Retrieval Augmented Generation), we are committed to exploring new research areas. Our aim is to adapt Feast to support novel use cases, keeping it at the forefront of technology.
- Support for Latest ML Model Advancements: As ML models become more sophisticated, Feast will evolve to support these advancements. This includes accommodating new model architectures and training techniques.
This new phase is not just about setting goals but laying down a concrete roadmap to achieve Feast version 1.0. This version will encapsulate all our efforts towards making Feast more integrated, enterprise-ready, and aligned with the latest ML advancements.
Why Invest in Feast?
Many industry applications of machine learning require intensely sophisticated data pipelines. Over the last decade, the data infrastructure and analytics community collaborated together to build powerful frameworks like dbt that enabled analytics to flourish. We believe Feast can do the same for the machine learning community–particularly those that spend most of their time on data pipelining and feature engineering.
We believe Feast is a core foundation in the future of machine learning and we will build it to offer a standard set of patterns that will enable ML Engineering and ML Ops teams to leverage those patterns and industry best practices to avoid common pitfalls, while (1) offering the flexibility of choosing their own infrastructure and (2) providing ML Practitioners with a Python-based interface.
In Conclusion
This transition marks a pivotal moment in Feast’s journey. We are excited about the opportunities and challenges ahead. With the support of the ML community, the dedication of our new maintainers, and the clear vision set by our steward committee, Feast is poised to reach new heights and continue to be a pivotal tool in the ML ecosystem.
We invite everyone to join us in this exciting journey and contribute to the future of Feast. Together, let’s shape the next chapter in the evolution of feature stores and machine learning.
For updates and discussions, join our Slack channel and follow our GitHub repository.
This post reflects the collective vision and aspirations of the new Feast steward committee. For more detailed discussions and contributions, please reach out to us on our community channels.
