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diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py
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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from abc import ABCMeta, abstractmethod
+
+from pyspark.ml.param import Param, Params
+from pyspark.ml.util import inherit_doc
+
+
+__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel']
+
+
+@inherit_doc
+class Estimator(Params):
+    """
+    Abstract class for estimators that fit models to data.
+    """
+
+    __metaclass__ = ABCMeta
+
+    @abstractmethod
+    def fit(self, dataset, params={}):
+        """
+        Fits a model to the input dataset with optional parameters.
+
+        :param dataset: input dataset, which is an instance of
+                        :py:class:`pyspark.sql.SchemaRDD`
+        :param params: an optional param map that overwrites embedded
+                       params
+        :returns: fitted model
+        """
+        raise NotImplementedError()
+
+
+@inherit_doc
+class Transformer(Params):
+    """
+    Abstract class for transformers that transform one dataset into
+    another.
+    """
+
+    __metaclass__ = ABCMeta
+
+    @abstractmethod
+    def transform(self, dataset, params={}):
+        """
+        Transforms the input dataset with optional parameters.
+
+        :param dataset: input dataset, which is an instance of
+                        :py:class:`pyspark.sql.SchemaRDD`
+        :param params: an optional param map that overwrites embedded
+                       params
+        :returns: transformed dataset
+        """
+        raise NotImplementedError()
+
+
+@inherit_doc
+class Pipeline(Estimator):
+    """
+    A simple pipeline, which acts as an estimator. A Pipeline consists
+    of a sequence of stages, each of which is either an
+    :py:class:`Estimator` or a :py:class:`Transformer`. When
+    :py:meth:`Pipeline.fit` is called, the stages are executed in
+    order. If a stage is an :py:class:`Estimator`, its
+    :py:meth:`Estimator.fit` method will be called on the input
+    dataset to fit a model. Then the model, which is a transformer,
+    will be used to transform the dataset as the input to the next
+    stage. If a stage is a :py:class:`Transformer`, its
+    :py:meth:`Transformer.transform` method will be called to produce
+    the dataset for the next stage. The fitted model from a
+    :py:class:`Pipeline` is an :py:class:`PipelineModel`, which
+    consists of fitted models and transformers, corresponding to the
+    pipeline stages. If there are no stages, the pipeline acts as an
+    identity transformer.
+    """
+
+    def __init__(self):
+        super(Pipeline, self).__init__()
+        #: Param for pipeline stages.
+        self.stages = Param(self, "stages", "pipeline stages")
+
+    def setStages(self, value):
+        """
+        Set pipeline stages.
+        :param value: a list of transformers or estimators
+        :return: the pipeline instance
+        """
+        self.paramMap[self.stages] = value
+        return self
+
+    def getStages(self):
+        """
+        Get pipeline stages.
+        """
+        if self.stages in self.paramMap:
+            return self.paramMap[self.stages]
+
+    def fit(self, dataset, params={}):
+        paramMap = self._merge_params(params)
+        stages = paramMap[self.stages]
+        for stage in stages:
+            if not (isinstance(stage, Estimator) or isinstance(stage, Transformer)):
+                raise ValueError(
+                    "Cannot recognize a pipeline stage of type %s." % type(stage).__name__)
+        indexOfLastEstimator = -1
+        for i, stage in enumerate(stages):
+            if isinstance(stage, Estimator):
+                indexOfLastEstimator = i
+        transformers = []
+        for i, stage in enumerate(stages):
+            if i <= indexOfLastEstimator:
+                if isinstance(stage, Transformer):
+                    transformers.append(stage)
+                    dataset = stage.transform(dataset, paramMap)
+                else:  # must be an Estimator
+                    model = stage.fit(dataset, paramMap)
+                    transformers.append(model)
+                    if i < indexOfLastEstimator:
+                        dataset = model.transform(dataset, paramMap)
+            else:
+                transformers.append(stage)
+        return PipelineModel(transformers)
+
+
+@inherit_doc
+class PipelineModel(Transformer):
+    """
+    Represents a compiled pipeline with transformers and fitted models.
+    """
+
+    def __init__(self, transformers):
+        super(PipelineModel, self).__init__()
+        self.transformers = transformers
+
+    def transform(self, dataset, params={}):
+        paramMap = self._merge_params(params)
+        for t in self.transformers:
+            dataset = t.transform(dataset, paramMap)
+        return dataset