Feature Storage - Feast https://github.com/feast-dev/feast

'/feast-dev/feast':6 'feast':3 'featur':1 'github.com':5 'github.com/feast-dev/feast':4 'storag':2

Model Serving - BentoML https://github.com/bentoml/BentoML

'/bentoml/bentoml':6 'bentoml':3 'github.com':5 'github.com/bentoml/bentoml':4 'model':1 'serv':2

Model Serving - Cortex https://github.com/cortexlabs/cortex

'/cortexlabs/cortex':6 'cortex':3 'github.com':5 'github.com/cortexlabs/cortex':4 'model':1 'serv':2

Model Serving - Kserve https://github.com/kserve/kserve

'/kserve/kserve':6 'github.com':5 'github.com/kserve/kserve':4 'kserv':3 'model':1 'serv':2

Model Serving - PredictionIO https://github.com/apache/predictionio

'/apache/predictionio':6 'github.com':5 'github.com/apache/predictionio':4 'model':1 'predictionio':3 'serv':2

Model Versioning - DVC https://github.com/iterative/dvc

'/iterative/dvc':6 'dvc':3 'github.com':5 'github.com/iterative/dvc':4 'model':1 'version':2

Model Versioning - ClearML https://github.com/allegroai/clearml

'/allegroai/clearml':6 'clearml':3 'github.com':5 'github.com/allegroai/clearml':4 'model':1 'version':2

Model Versioning - Catalyst https://github.com/catalyst-team/catalyst

'/catalyst-team/catalyst':6 'catalyst':3 'github.com':5 'github.com/catalyst-team/catalyst':4 'model':1 'version':2

Training Tracking & Eval - https://github.com/pycaret/pycaret

'/pycaret/pycaret':6 'eval':3 'github.com':5 'github.com/pycaret/pycaret':4 'track':2 'train':1

Training Tracking & Eval - Determined https://github.com/determined-ai/determined

'/determined-ai/determined':7 'determin':4 'eval':3 'github.com':6 'github.com/determined-ai/determined':5 'track':2 'train':1

Training Tracking & Eval - https://github.com/aimhubio/aim

'/aimhubio/aim':6 'eval':3 'github.com':5 'github.com/aimhubio/aim':4 'track':2 'train':1

Workflow Scheduling - Airflow https://github.com/apache/airflow

'/apache/airflow':6 'airflow':3 'github.com':5 'github.com/apache/airflow':4 'schedul':2 'workflow':1

Workflow Scheduling - Kedro https://github.com/kedro-org/kedro

'/kedro-org/kedro':6 'github.com':5 'github.com/kedro-org/kedro':4 'kedro':3 'schedul':2 'workflow':1

Workflow Scheduling - Luigi https://github.com/spotify/luigi

'/spotify/luigi':6 'github.com':5 'github.com/spotify/luigi':4 'luigi':3 'schedul':2 'workflow':1

Workflow Scheduling - Argo https://github.com/argoproj/argo-workflows

'/argoproj/argo-workflows':6 'argo':3 'github.com':5 'github.com/argoproj/argo-workflows':4 'schedul':2 'workflow':1

Workflow Scheduling - Pachyderm https://github.com/pachyderm/pachyderm

'/pachyderm/pachyderm':6 'github.com':5 'github.com/pachyderm/pachyderm':4 'pachyderm':3 'schedul':2 'workflow':1

Feature Storage - https://github.com/featureform/featureform

'/featureform/featureform':5 'featur':1 'github.com':4 'github.com/featureform/featureform':3 'storag':2

Feature Storage - HopsWorks https://github.com/logicalclocks/hopsworks

'/logicalclocks/hopsworks':6 'featur':1 'github.com':5 'github.com/logicalclocks/hopsworks':4 'hopswork':3 'storag':2

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.

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