Giới thiệu về MLflow
Weston Bassler
Senior MLOps Engineer

Kho lưu trữ tập trung
Quản lý vòng đời

Model
Registered Model
Model Version
Model Stage


Experiments
Runs
Model Versions
Registered Models

# Import from MLflow module from mlflow import MlflowClient# Create an instance client = MlflowClient()# Print the object client
<mlflow.tracking.client.MlflowClient object at 0x101d55f30>
# Create a Model named "Unicorn"
client.create_registered_model(name="Unicorn")
<RegisteredModel: creation_timestamp=1679404160448, description=None,
last_updated_timestamp=1679404160448, latest_versions=[], name='Unicorn',
tags={}>


# Search for registered models
client.search_registered_models(filter_string=MY_FILTER_STRING)
= - bằng!= - khácLIKE - khớp mẫu phân biệt hoa thườngILIKE - khớp mẫu không phân biệt hoa thường# Filter string unicorn_filter_string = "name LIKE 'Unicorn%'"# Search models client.search_registered_models(filter_string=unicorn_filter_string)
[<RegisteredModel: creation_timestamp=1679404160448, description=None,
last_updated_timestamp=1679404160448, latest_versions=[], name='Unicorn',
tags={}>,
<RegisteredModel: creation_timestamp=1679404276745, description=None,
last_updated_timestamp=1679404276745, latest_versions=[], name='Unicorn 2.0',
tags={}>]
Giới thiệu về MLflow