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The Stream Client is used for real-time logging of model predictions, providing lower latency than batch logging. 
from arize.api import Client
client = Client(...)

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log()

The log() method migrates from client.log() to client.ml.log_stream().

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Parameter Reference

This table provides a complete mapping of all parameters between v7 and v8, including which parameters were removed, renamed, or remain unchanged.
Parameterv7v8Changes
space_idClient initRequired per callMust pass explicitly
model_idRequiredRequiredRenamed to model_name
model_typeRequiredRequired—
environmentRequiredRequired—
model_versionOptionalOptional—
prediction_idOptionalOptional—
prediction_timestampOptionalOptional—
prediction_labelOptionalOptional—
actual_labelOptionalOptional—
featuresOptionalOptional—
embedding_featuresOptionalOptional—
shap_valuesOptionalOptional—
tagsOptionalOptional—
batch_idOptionalOptional—
promptOptionalOptional—
responseOptionalOptional—
prompt_templateOptionalOptional—
prompt_template_versionOptionalOptional—
llm_model_nameOptionalOptional—
llm_paramsOptionalOptional—
llm_run_metadataOptionalOptional—
timeoutN/A✅ OptionalNew parameter for request timeout

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Side-by-Side Comparison

See the complete migration in action with this example showing both client initialization and streaming model predictions. 
from arize.api import Client
from arize.utils.types import Environments, ModelTypes

# Client initialization
client = Client(
    api_key="your-api-key",
    space_id="your-space-id"
)

# Streaming a prediction
future = client.log(
    model_id="my-model",
    model_type=ModelTypes.BINARY_CLASSIFICATION,
    environment=Environments.PRODUCTION,
    model_version="v1.0",
    prediction_id="pred-123",
    prediction_timestamp=1609459200,
    prediction_label=1,
    features={"feature1": 0.5, "feature2": "value"},
    tags={"user_id": "user-456"},
    batch_id="batch-789"
)

# Get the result (blocks until complete)
response = future.result()