Which Part of a Full Stack A.I. Workflow Will Become The Most Breakout In 2023?
1
580Ṁ48
resolved Feb 1
ResolvedN/A
46%Other
37%
Training Tracking & Eval - https://github.com/pycaret/pycaret
1.3%
Feature Storage - HopsWorks https://github.com/logicalclocks/hopsworks
1.1%
Workflow Scheduling - Pachyderm https://github.com/pachyderm/pachyderm
1.0%
1.0%
Workflow Scheduling - Luigi https://github.com/spotify/luigi
1.0%
Workflow Scheduling - Kedro https://github.com/kedro-org/kedro
1.0%
Workflow Scheduling - Airflow https://github.com/apache/airflow
1.0%
Training Tracking & Eval - https://github.com/aimhubio/aim

The launch of ChatGPT has demonstrated the immense potential of Large Language Models, making it impossible for business leaders to ignore the importance of ML/AI.

While product development conversations can be dynamic and fluid, at the end of the day, developers must create code that works, compiling into results that can be displayed on screens, which means actual software .

To help facilitate planning and conversations about what is needed at what time, please participate in this market.

Market Description

Which Part of a Full Stack Machine Learning Workflow Will Become The Most Breakout In 2023, measured by Github Star growth in a particular repo?

This page gives a good breakdown of various systems within an AI or ML productionalization architecture. We could grab repo's from here and track and bet on those: https://github.com/EthicalML/awesome-production-machine-learning#quick-links-to-sections-in-this-page

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