1.46.0

1.46.0

New Features

Bug Fixes

Behavior Changes

• Registry: For Snowpark ML Pipeline models with explainability enabled, explanations now cover only the columns the
  final estimator was actually trained on (its input_cols). Untransformed passthrough columns (e.g. raw string
  columns left in place by an encoder using new output_cols, or the label column) are no longer included in the
  explain output. This also fixes a failure where such passthrough columns could reach the SHAP explainer and raise an
  error during log_model(..., enable_explainability=True).

Deprecations

1.45.0

1.45.0

New Features

• Feature Store: FeatureView now supports an initialization_warehouse that is used for the initial build and any
  subsequent reinitializations of the backing dynamic table (a full scan of the source data), while warehouse
  continues to drive the lighter incremental refreshes. This mirrors the dynamic table INITIALIZATION_WAREHOUSE
  knob, lets you pair a larger warehouse for initialization with a smaller one for steady-state refresh, and is also
  used for the one-time backfill of streaming feature views. It can be set at registration, changed via
  update_feature_view(initialization_warehouse=...), and is surfaced by list_feature_views(verbose=True).

draft_fv = FeatureView(
    name="F_TRIP",
    entities=[entity],
    feature_df=feature_df,
    refresh_freq="1d",
    warehouse="SMALL_WH",            # incremental refreshes
    initialization_warehouse="LARGE_WH",  # initial build / reinitialization
)
fv = fs.register_feature_view(draft_fv, version="1.0")

• Registry: LLM models deployed with the OpenAI chat signatures now support structured outputs
  through an optional response_format param matching the OpenAI Chat Completions API
  ({"type": "json_schema", "json_schema": {"name": "...", "schema": {...}}}), letting callers
  constrain model output to a JSON Schema.

from pydantic import BaseModel
import pandas as pd

class CityCountry(BaseModel):
    city: str
    country: str

response_format = {
    "type": "json_schema",
    "json_schema": {
        "name": "city_country",
        "schema": CityCountry.model_json_schema(),
    },
}

x_df = pd.DataFrame.from_records(
    [
        {
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "What is the capital of France?"},
                    ],
                },
            ],
        }
    ]
)

mv.run(
    X=x_df,
    params={"response_format": response_format},
    service_name=...,
)

Bug Fixes

Behavior Changes

Deprecations

1.44.0

1.44.0

New Features

Bug Fixes

Behavior Changes

Deprecations

1.43.0

1.43.0

New Features

• Registry: inference_engine_options["engine"] now accepts case-insensitive strings in addition to
  InferenceEngine enum members. Supported values are "vllm" and "python_generic" (for example,
  "vLLM", "VLLM", and "PYTHON_GENERIC" are all accepted). This applies to ModelVersion.create_service()
  and ModelVersion.run_batch().

from snowflake.ml.registry import Registry

registry = Registry(session)
mv = registry.get_model("my_model").version("v1")

mv.create_service(
    service_name="my_vllm_service",
    service_compute_pool="GPU_COMPUTE_POOL",
    gpu_requests="1",
    inference_engine_options={
        "engine": "vLLM",
        "engine_args_override": ["--max-model-len=4096"],
    },
)

job = mv.run_batch(
    compute_pool="GPU_COMPUTE_POOL",
    X=input_df,
    output_spec=output_spec,
    inference_engine_options={
        "engine": "python_generic",
    },
)

Bug Fixes

• Registry: Fixed explain() failing with type mismatch errors when passing a Snowpark DataFrame.
  Snowpark DataFrame columns are now explicitly cast to match model signature types before SQL
  generation. Table functions (used by explain) enforce stricter type coercion than scalar functions
  (used by predict), so columns that were implicitly coerced for predict would be rejected by
  explain.

Behavior Changes

• Registry: SentenceTransformers models no longer hard-code a default inference batch size of 32.
  When batch_size is not specified at log_model time and is not overridden at inference, the
  sentence-transformers library default is used. To pin a specific size, pass batch_size via
  log_model(..., options={"batch_size": N}) or as an inference-time parameter. All
  SentenceTransformers models logged with this release require a client/serving runtime on version
  1.43.0 or later (regardless of whether batch_size was specified).

Deprecations

1.42.0

1.42.0

New Features

• Registry: Support truncate_dim for SentenceTransformers models. Truncation set at
  construction (SentenceTransformer(..., truncate_dim=N)) is captured when the model is logged
  and restored on reload. On clients with sentence-transformers 5.0.0 or later, truncate_dim
  is also exposed as a runtime parameter in the model signature.

• Registry: log_model() now captures a representative row from sample_input_data and stores it alongside the model
  so inference code snippets shown in the model registry UI can be pre-filled with realistic values. Pass
  options={"capture_sample_input_data": False} to opt out (e.g., for sensitive data); generic placeholder values
  will be used in the snippets instead.

Bug Fixes

• Registry: Pin transformers<5 for additional HuggingFace pipeline tasks removed in
  transformers 5.x (summarization, text2text-generation, question-answering, and
  translation_*), preventing deployment failures from missing pipeline classes.

Behavior Changes

Deprecations

1.41.0

1.41.0

New Features

• Registry: Extended ParamSpec support to MLflow PyFunc, LightGBM, XGBoost, and CatBoost models.
  Parameters declared in the model signature can now be passed at inference time.

Bug Fixes

Behavior Changes

Deprecations

1.40.0

1.40.0

New Features

Bug Fixes

• Feature Store: get_feature_view() now correctly preserves online_config for feature views whose
  names require SQL quoting (mixed case or special characters such as a space). Previously the returned
  FeatureView reported online=False even when online was enabled at registration, causing
  read_feature_view(store_type=ONLINE) to fail with "Online store is not enabled".

Behavior Changes

Deprecations

1.39.0

1.39.0

New Features

Bug Fixes

• Feature Store: update_feature_view now correctly handles every transition between duration-based
  and CRON-based refresh_freq. Previously a CRON expression was forwarded to the Dynamic Table's
  TARGET_LAG (which Snowflake rejects), duration → cron failed because the companion Task did not
  yet exist, and cron → duration left the Task orphaned and continuing to fire on its old schedule.
  All four (old, new) transitions now correctly create, alter, or drop the Task as needed, with
  symmetric rollback on failure.

Behavior Changes

• Feature Store: For CRON-based feature views, get_feature_view(), list_feature_views(), and
  register_feature_view() now return the original cron expression as refresh_freq instead of
  "DOWNSTREAM". The underlying Dynamic Table still uses TARGET_LAG = 'DOWNSTREAM' with a companion
  Task — this is purely a display change so the round-trip preserves what the user passed in.

• Registry: When target_platforms includes WAREHOUSE and pip installs are needed without a user-supplied
  pip artifact repository, log_model injects snowflake.snowpark.pypi_shared_repository after
  verifying access. This applies to explicit pip_requirements and to pip-only packaging when there are
  no user conda dependencies. If pypi_shared_repository is inaccessible in the pip-only case, automatic packaging
  dependencies fall back to conda instead of forcing pip-only without an index.

Deprecations

1.38.0

1.38.0

New Features

Bug Fixes

Behavior Changes

• Registry: For HuggingFace text-generation pipelines whose tokenizer defines a chat template, the
  auto-inferred signature now matches the OpenAI Chat Completions API
  (_OPENAI_CHAT_SIGNATURE_WITH_PARAMS_SPEC). Inputs are a single messages column and inference
  controls (temperature, max_completion_tokens, stop, n, stream, top_p, frequency_penalty,
  presence_penalty) move from inputs to params with default values. Predictions return the OpenAI
  response shape (id, object, created, model, choices, usage); the generated text is at
  choices[0].message.content instead of outputs[0].generated_text.

• Registry: For HuggingFace image-text-to-text, video-text-to-text, and audio-text-to-text
  pipelines, the auto-inferred signature now uses _OPENAI_CHAT_SIGNATURE_WITH_PARAMS_SPEC instead of
  _OPENAI_CHAT_SIGNATURE_SPEC. The input column set narrows to just messages, and the inference
  controls move to params with default values; the output schema is unchanged.

Deprecations

1.37.0

1.37.0

New Features

• Experiment Tracking: end_run now accepts an optional status argument ("FINISHED" or "FAILED") to explicitly
  set the final run status. When a run's context manager exits with an exception, the status is automatically set to
  "FAILED".

Bug Fixes

• Registry: Fixed log_model() failing for some Snowpark ML Pipeline models with explainability when the
  full pipeline could not be converted to a native object; task inference now uses the final estimator instead.

• Registry: run_batch() now raises a clear ValueError when a partitioned model's output signature
  includes the partition column. Previously this produced duplicate columns in the output, causing
  cryptic Ray/Arrow errors (AttributeError or KeyError) deep in the batch inference pipeline.

Behavior Changes

Deprecations