# MAGIC # MAGIC You can add descriptions to registered models as well as model versions: # MAGIC * Model version descriptions are useful for detailing the unique attributes of a particular model version (e.g., the methodology and algorithm used to develop the model). # MAGIC * Registered model descriptions are useful for recording information that applies to multiple model versions (e.g., a general overview of the modeling problem and dataset). # COMMAND ---------- # MAGIC %md Add a high-level description to the registered model, including the machine learning problem and dataset. # COMMAND ---------- from mlflow.tracking.client import MlflowClient client = MlflowClient() client.update_registered_model( name=model_details.name, description="This model forecasts the power output of a wind farm based on weather data. The weather data consists of three features: wind speed, wind direction, and air temperature." ) # COMMAND ---------- # MAGIC %md Add a model version description with information about the model architecture and machine learning framework. # COMMAND ---------- client.update_model_version( name=model_details.name, version=model_details.version, description="This model version was built using Keras. It is a feed-forward neural network with one hidden layer." ) # COMMAND ----------
#latest_model = client.get_latest_versions(name = model_name, stages=[stage])[0]
latest_model = client.get_model_version(name = model_name, version = model_version)
#print(latest_model[0])
# COMMAND ----------
model_uri="runs:/{}/model".format(latest_model.run_id)
latest_sk_model = mlflow.sklearn.load_model(model_uri)
# COMMAND ----------
from mlflow.tracking.client import MlflowClient
client = MlflowClient()
client.update_registered_model(
name=model_name,
description="This model forecasts the wine quality based on the characteristics."
)
client.update_model_version(
name=model_name,
version=model_version,
description="This model version was built using sklearn."
)
# COMMAND ----------
client.transition_model_version_stage(
name=model_name,
version=model_version,
stage=stage,
archive_existing_versions=True
status = ModelVersionStatus.from_string(model_version_details.status)
print("Model status: %s" % ModelVersionStatus.to_string(status))
if status == ModelVersionStatus.READY:
break
time.sleep(1)
wait_until_ready(model_details.name, model_details.version)
# COMMAND ----------
# MAGIC %md ### Add model descriptions
# MAGIC
# MAGIC You can add descriptions to registered models as well as model versions:
# MAGIC * Model version descriptions are useful for detailing the unique attributes of a particular model version (e.g., the methodology and algorithm used to develop the model).
# MAGIC * Registered model descriptions are useful for recording information that applies to multiple model versions (e.g., a general overview of the modeling problem and dataset).
# COMMAND ----------
# MAGIC %md Add a high-level description to the registered model, including the machine learning problem and dataset.
# COMMAND ----------
from mlflow.tracking.client import MlflowClient
client = MlflowClient()
client.update_registered_model(name=model_details.name,
description="This model recognizes text.")
# COMMAND ----------
