/
/
Model Repository

Model Repository

In addition to tracking model parameters, trained models themselves can also be tracked across the model development lifecycle via a version control system. Versioning models allows data science and machine learning teams to more readily reproduce experiments and provides for greater consistency between prototyping and production environments. Models may be versioned by checking model code, serialized model objects (e.g. a Python .pkl file), a model’s Docker container, or other model artifacts into a Git repository, ideally automatically at appropriate points in the modeling workflow. Most modern ML platform offerings support versioning models.

READ MORE
SAS Visual Data Mining and Machine Learning
Solve the most complex analytical problems with a single, integrated, collaborative solution
Valohai
The MLOps Platform
RStudio
Take control of your R code
RapidMiner Studio
One platform, does everything
Polyaxon
An enterprise-grade platform for agile, reproducible, and scalable machine learning
Hopsworks
The enterprise feature store
Gradient
Modern MLOps focused on speed and simplicity
Weights & Biases
With a few lines of code, save everything you need to debug, compare and reproduce your models
Verta
AI and machine learning model management and operations for enterprise data science teams
Spell
Power your machine learning lifecycle