diff --git a/src/deepsparse/pipeline.py b/src/deepsparse/pipeline.py
index 4ffca2af4a..6e210a721c 100644
--- a/src/deepsparse/pipeline.py
+++ b/src/deepsparse/pipeline.py
@@ -18,11 +18,13 @@
 """
 
 
+import os
 from abc import ABC, abstractmethod
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 import numpy
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from deepsparse import Engine, Scheduler
 from deepsparse.benchmark import ORTEngine
@@ -34,6 +36,7 @@
     "ORT_ENGINE",
     "SUPPORTED_PIPELINE_ENGINES",
     "Pipeline",
+    "PipelineConfig",
 ]
 
 
@@ -171,7 +174,7 @@ def create(
         input_shapes: List[List[int]] = None,
         alias: Optional[str] = None,
         **kwargs,
-    ):
+    ) -> "Pipeline":
         """
         :param task: name of task to create a pipeline for
         :param model_path: path on local system or SparseZoo stub to load the model
@@ -185,10 +188,10 @@ def create(
             Pass None for the default
         :param input_shapes: list of shapes to set ONNX the inputs to. Pass None
             to use model as-is. Default is None
-        :param kwargs: extra task specific kwargs to be passed to task Pipeline
-            implementation
         :param alias: optional name to give this pipeline instance, useful when
             inferencing with multiple models. Default is None
+        :param kwargs: extra task specific kwargs to be passed to task Pipeline
+            implementation
         :return: pipeline object initialized for the given task
         """
         task = task.lower().replace("-", "_")
@@ -264,6 +267,34 @@ def _register_pipeline_tasks_decorator(pipeline_class: Pipeline):
 
         return _register_pipeline_tasks_decorator
 
+    @classmethod
+    def from_config(cls, config: Union["PipelineConfig", str, Path]) -> "Pipeline":
+        """
+        :param config: PipelineConfig object, filepath to a json serialized
+            PipelineConfig, or raw string of a json serialized PipelineConfig
+        :return: loaded Pipeline object from the config
+        """
+        if isinstance(config, Path) or (
+            isinstance(config, str) and os.path.exists(config)
+        ):
+            if isinstance(config, str):
+                config = Path(config)
+            config = PipelineConfig.parse_file(config)
+        if isinstance(config, str):
+            config = PipelineConfig.parse_raw(config)
+
+        return cls.create(
+            task=config.task,
+            model_path=config.model_path,
+            engine_type=config.engine_type,
+            batch_size=config.batch_size,
+            num_cores=config.num_cores,
+            scheduler=config.scheduler,
+            input_shapes=config.input_shapes,
+            alias=config.alias,
+            **config.kwargs,
+        )
+
     @abstractmethod
     def setup_onnx_file_path(self) -> str:
         """
@@ -363,6 +394,37 @@ def onnx_file_path(self) -> str:
         """
         return self._onnx_file_path
 
+    def to_config(self) -> "PipelineConfig":
+        """
+        :return: PipelineConfig that can be used to reload this object
+        """
+
+        if not hasattr(self, "task"):
+            raise RuntimeError(
+                f"{self.__class__} instance has no attribute task. Pipeline objects "
+                "must have a task to be serialized to a config. Pipeline objects "
+                "must be declared with the Pipeline.register object to be assigned a "
+                "task"
+            )
+
+        # parse any additional properties as kwargs
+        kwargs = {}
+        for attr_name, attr in self.__class__.__dict__.items():
+            if isinstance(attr, property) and attr_name not in dir(PipelineConfig):
+                kwargs[attr_name] = getattr(self, attr_name)
+
+        return PipelineConfig(
+            task=self.task,
+            model_path=self.model_path_orig,
+            engine_type=self.engine_type,
+            batch_size=self.batch_size,
+            num_cores=self.num_cores,
+            scheduler=self.scheduler,
+            input_shapes=self.input_shapes,
+            alias=self.alias,
+            kwargs=kwargs,
+        )
+
     def _initialize_engine(self) -> Union[Engine, ORTEngine]:
         engine_type = self.engine_type.lower()
 
@@ -375,3 +437,65 @@ def _initialize_engine(self) -> Union[Engine, ORTEngine]:
                 f"Unknown engine_type {self.engine_type}. Supported values include: "
                 f"{SUPPORTED_PIPELINE_ENGINES}"
             )
+
+
+class PipelineConfig(BaseModel):
+    """
+    Configuration for creating a Pipeline object
+
+    Can be used to create a Pipeline from a config object or file with
+    Pipeline.from_config(), or used as a building block for other configs
+    such as for deepsparse.server
+    """
+
+    task: str = Field(
+        description="name of task to create a pipeline for",
+    )
+    model_path: str = Field(
+        description="path on local system or SparseZoo stub to load the model from",
+    )
+    engine_type: str = Field(
+        default=DEEPSPARSE_ENGINE,
+        description=(
+            "inference engine to use. Currently supported values include "
+            "'deepsparse' and 'onnxruntime'. Default is 'deepsparse'"
+        ),
+    )
+    batch_size: int = Field(
+        default=1,
+        description=("static batch size to use for inference. Default is 1"),
+    )
+    num_cores: int = Field(
+        default=None,
+        description=(
+            "number of CPU cores to allocate for inference engine. None"
+            "specifies all available cores. Default is None"
+        ),
+    )
+    scheduler: str = Field(
+        default="async",
+        description=(
+            "(deepsparse only) kind of scheduler to execute with. Defaults to async"
+        ),
+    )
+    input_shapes: List[List[int]] = Field(
+        default=None,
+        description=(
+            "list of shapes to set ONNX the inputs to. Pass None to use model as-is. "
+            "Default is None"
+        ),
+    )
+    alias: str = Field(
+        default=None,
+        description=(
+            "optional name to give this pipeline instance, useful when inferencing "
+            "with multiple models. Default is None"
+        ),
+    )
+    kwargs: Dict[str, Any] = Field(
+        default={},
+        description=(
+            "Additional arguments for inference with the model that will be passed "
+            "into the pipeline as kwargs"
+        ),
+    )