Quick Start

This library’s ModelStore enables you to export trained ML models and store it to your choice of storage.

Create a model store instance

modelstore currently supports storing models to:

  • A directory in a local file system

  • Google Cloud buckets: set up a Google Cloud project and have create a cloud storage bucket.

  • AWS S3 buckets: set up a project and create an s3 bucket.

  • 🆕 A storage service that we manage for you. This requires you to have API keys.

To save your models, create a model store instance with one of the following:

from modelstore import ModelStore

# A local file system
model_store = ModelStore.from_file_system(

# Google cloud bucket
model_store = ModelStore.from_gcloud(

# AWS S3 bucket
model_store = ModelStore.from_aws_s3(

# A managed storage service
model_store = ModelStore.from_api_key(

Upload a model to the model store

The modelstore library has separate up functions to store models that were trained with different ML libraries, such as scikit-learn or tensorflow. They all follow the same pattern.

For example, to store a scikit-learn model, use:

model_store.sklearn.upload(domain="domain-name", model=my_model)

When you upload a model, you need to specify a domain. This is the string that groups several models that are for the same end-usage together. For example, let’s assume you are training several models to predict whether an email is spam. Setting domain="spam-detection" will store all of those models together, and you will then be able to list and retrieve them all.

To read more about the supported libraries, see: Supported Machine Learning Libraries.

To read more about how this library organises models, see The Model Store Structure.

Download a model from the model store

To retrieve a model from your chosen storage, use download():

file_path = model_store.download(
   local_path=".", # Where to download the model to
   domain="example-model", # The model's domain
   model_id="model-id"  # Optional; the ID of the specific model

If you do not provide a model_id parameter, the download() function will default to the last model that was stored for the given domain.