# 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 ----------