diff --git a/.github/workflows/instrumentations_0.yml b/.github/workflows/instrumentations_0.yml
index d54cb50119..69c07bfacc 100644
--- a/.github/workflows/instrumentations_0.yml
+++ b/.github/workflows/instrumentations_0.yml
@@ -66,7 +66,6 @@ jobs:
           - "redis"
           - "remoulade"
           - "requests"
-          - "sklearn"
           - "sqlalchemy"
           - "sqlite3"
           - "starlette"
@@ -75,14 +74,6 @@ jobs:
           - "tortoiseorm"
         os: [ubuntu-20.04]
         exclude:
-          - python-version: py39
-            package: "sklearn"
-          - python-version: py310
-            package: "sklearn"
-          - python-version: py311
-            package: "sklearn"
-          - python-version: py312
-            package: "sklearn"
           - python-version: py312
             package: "boto"
           - python-version: py312
@@ -103,8 +94,6 @@ jobs:
             package: "remoulade"
           - python-version: pypy3
             package: "requests"
-          - python-version: pypy3
-            package: "sklearn"
           - python-version: pypy3
             package: "confluent-kafka"
           - python-version: pypy3
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index c156f7d942..29cd5cac16 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -93,31 +93,3 @@ jobs:
           key: v7-build-tox-cache-${{ matrix.package }}-${{ hashFiles('tox.ini', 'gen-requirements.txt', 'dev-requirements.txt') }}
       - name: run tox
         run: tox -e lint-${{ matrix.package }}
-
-  lint-3_8:
-    strategy:
-      fail-fast: false  # ensures the entire test matrix is run, even if one permutation fails
-      matrix:
-        package:
-          - "instrumentation-sklearn"
-        os: [ubuntu-20.04]
-    runs-on: ubuntu-20.04
-    steps:
-      - name: Checkout Contrib Repo @ SHA - ${{ github.sha }}
-        uses: actions/checkout@v4
-      - name: Set up Python 3.8
-        uses: actions/setup-python@v5
-        with:
-          python-version: 3.8
-      - name: Install tox
-        run: pip install tox
-      - name: Cache tox environment
-        # Preserves .tox directory between runs for faster installs
-        uses: actions/cache@v4
-        with:
-          path: |
-            .tox
-            ~/.cache/pip
-          key: v7-build-tox-cache-${{ matrix.package }}-${{ hashFiles('tox.ini', 'gen-requirements.txt', 'dev-requirements.txt') }}
-      - name: run tox
-        run: tox -e lint-${{ matrix.package }}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 57447ccf97..2de76b2736 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- `opentelemetry-instrumentation-sklearn` Deprecated the sklearn instrumentation
+  ([#2708](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2708))
 - `opentelemetry-instrumentation-pyramid` Record exceptions raised when serving a request
   ([#2622](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2622))
 - `opentelemetry-sdk-extension-aws` Add AwsXrayLambdaPropagator
diff --git a/eachdist.ini b/eachdist.ini
index 7f170e4947..b698eaf299 100644
--- a/eachdist.ini
+++ b/eachdist.ini
@@ -54,7 +54,6 @@ packages=
 [lintroots]
 extraroots=examples/*,scripts/
 subglob=*.py,tests/,test/,src/*,examples/*
-ignore=sklearn
 
 [testroots]
 extraroots=examples/*,tests/
diff --git a/instrumentation/README.md b/instrumentation/README.md
index bbc5d8d75c..989ba529cb 100644
--- a/instrumentation/README.md
+++ b/instrumentation/README.md
@@ -38,7 +38,6 @@
 | [opentelemetry-instrumentation-redis](./opentelemetry-instrumentation-redis) | redis >= 2.6 | No | experimental
 | [opentelemetry-instrumentation-remoulade](./opentelemetry-instrumentation-remoulade) | remoulade >= 0.50 | No | experimental
 | [opentelemetry-instrumentation-requests](./opentelemetry-instrumentation-requests) | requests ~= 2.0 | Yes | migration
-| [opentelemetry-instrumentation-sklearn](./opentelemetry-instrumentation-sklearn) | scikit-learn ~= 0.24.0 | No | experimental
 | [opentelemetry-instrumentation-sqlalchemy](./opentelemetry-instrumentation-sqlalchemy) | sqlalchemy | Yes | experimental
 | [opentelemetry-instrumentation-sqlite3](./opentelemetry-instrumentation-sqlite3) | sqlite3 | No | experimental
 | [opentelemetry-instrumentation-starlette](./opentelemetry-instrumentation-starlette) | starlette ~= 0.13.0 | Yes | experimental
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/LICENSE b/instrumentation/opentelemetry-instrumentation-sklearn/LICENSE
deleted file mode 100644
index 261eeb9e9f..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
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-      direction or management of such entity, whether by contract or
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-      outstanding shares, or (iii) beneficial ownership of such entity.
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-       http://www.apache.org/licenses/LICENSE-2.0
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-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
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-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/README.rst b/instrumentation/opentelemetry-instrumentation-sklearn/README.rst
deleted file mode 100644
index 9a3300c4f2..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/README.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-OpenTelemetry Scikit-Learn Instrumentation
-==========================================
-
-|pypi|
-
-.. |pypi| image:: https://badge.fury.io/py/opentelemetry-instrumentation-sklearn.svg
-   :target: https://pypi.org/project/opentelemetry-instrumentation-sklearn/
-
-This library allows tracing HTTP requests made by the
-`scikit-learn <https://scikit-learn.org/stable/>`_ library.
-
-Installation
-------------
-
-::
-
-     pip install opentelemetry-instrumentation-sklearn
-
-References
-----------
-
-* `OpenTelemetry sklearn Instrumentation <https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/sklearn/sklearn.html>`_
-* `OpenTelemetry Project <https://opentelemetry.io/>`_
-* `OpenTelemetry Python Examples <https://github.com/open-telemetry/opentelemetry-python/tree/main/docs/examples>`_
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/pyproject.toml b/instrumentation/opentelemetry-instrumentation-sklearn/pyproject.toml
deleted file mode 100644
index 6e57529ed1..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/pyproject.toml
+++ /dev/null
@@ -1,49 +0,0 @@
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[project]
-name = "opentelemetry-instrumentation-sklearn"
-dynamic = ["version"]
-description = "OpenTelemetry sklearn instrumentation"
-readme = "README.rst"
-license = "Apache-2.0"
-requires-python = ">=3.8"
-authors = [
-  { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" },
-]
-classifiers = [
-  "Development Status :: 4 - Beta",
-  "Intended Audience :: Developers",
-  "License :: OSI Approved :: Apache Software License",
-  "Programming Language :: Python",
-  "Programming Language :: Python :: 3",
-  "Programming Language :: Python :: 3.8",
-]
-dependencies = [
-  "opentelemetry-api ~= 1.12",
-  "opentelemetry-instrumentation == 0.47b0.dev",
-]
-
-[project.optional-dependencies]
-instruments = [
-  "scikit-learn ~= 0.24.0",
-]
-
-[project.entry-points.opentelemetry_instrumentor]
-sklearn = "opentelemetry.instrumentation.sklearn:SklearnInstrumentor"
-
-[project.urls]
-Homepage = "https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-sklearn"
-
-[tool.hatch.version]
-path = "src/opentelemetry/instrumentation/sklearn/version.py"
-
-[tool.hatch.build.targets.sdist]
-include = [
-  "/src",
-  "/tests",
-]
-
-[tool.hatch.build.targets.wheel]
-packages = ["src/opentelemetry"]
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/__init__.py b/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/__init__.py
deleted file mode 100644
index a67bfa6ef4..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/__init__.py
+++ /dev/null
@@ -1,792 +0,0 @@
-# Copyright 2020, OpenTelemetry Authors
-#
-# Licensed 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.
-"""
-The integration with sklearn supports the scikit-learn compatible libraries,
-it can be enabled by using ``SklearnInstrumentor``.
-
-.. sklearn: https://github.com/scikit-learn/scikit-learn
-
-Usage
------
-
-Package instrumentation example:
-
-.. code-block:: python
-
-    from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
-
-    # instrument the sklearn library
-    SklearnInstrumentor().instrument()
-
-    # instrument sklearn and other libraries
-    SklearnInstrumentor(
-        packages=["sklearn", "lightgbm", "xgboost"]
-    ).instrument()
-
-
-Model instrumentation example:
-
-.. code-block:: python
-
-    from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
-    from sklearn.datasets import load_iris
-    from sklearn.ensemble import RandomForestClassifier
-    from sklearn.model_selection import train_test_split
-    from sklearn.pipeline import Pipeline
-
-    X, y = load_iris(return_X_y=True)
-    X_train, X_test, y_train, y_test = train_test_split(X, y)
-
-    model = Pipeline(
-        [
-            ("class", RandomForestClassifier(n_estimators=10)),
-        ]
-    )
-
-    model.fit(X_train, y_train)
-
-    SklearnInstrumentor().instrument_estimator(model)
-
-"""
-import logging
-import os
-from functools import wraps
-from importlib import import_module
-from inspect import isclass
-from pkgutil import iter_modules
-from typing import (
-    Callable,
-    Collection,
-    Dict,
-    List,
-    MutableMapping,
-    Sequence,
-    Type,
-    Union,
-)
-
-from sklearn.base import BaseEstimator
-from sklearn.pipeline import FeatureUnion, Pipeline
-from sklearn.tree import BaseDecisionTree
-from sklearn.utils.metaestimators import _IffHasAttrDescriptor
-
-from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
-
-# pylint: disable=no-name-in-module
-from opentelemetry.instrumentation.sklearn.package import _instruments
-from opentelemetry.instrumentation.sklearn.version import __version__
-from opentelemetry.trace import get_tracer
-from opentelemetry.util.types import Attributes
-
-logger = logging.getLogger(__name__)
-
-
-def implement_span_estimator(
-    func: Callable,
-    estimator: Union[BaseEstimator, Type[BaseEstimator]],
-    attributes: Attributes = None,
-):
-    """Wrap the method call with a span.
-
-    Args:
-        func: A callable to be wrapped in a span
-        estimator: An instance or class of an estimator
-        attributes: Attributes to apply to the span
-
-    Returns:
-        The passed function wrapped in a span.
-    """
-    if isclass(estimator):
-        name = estimator.__name__
-    else:
-        name = estimator.__class__.__name__
-    logger.debug("Instrumenting: %s.%s", name, func.__name__)
-    attributes = attributes or {}
-    name = f"{name}.{func.__name__}"
-    return implement_span_function(func, name, attributes)
-
-
-def implement_span_function(func: Callable, name: str, attributes: Attributes):
-    """Wrap the function with a span.
-
-    Args:
-        func: A callable to be wrapped in a span
-        name: The name of the span
-        attributes: Attributes to apply to the span
-
-    Returns:
-        The passed function wrapped in a span.
-    """
-
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        with get_tracer(
-            __name__,
-            __version__,
-            schema_url="https://opentelemetry.io/schemas/1.11.0",
-        ).start_as_current_span(name=name) as span:
-            if span.is_recording():
-                for key, val in attributes.items():
-                    span.set_attribute(key, val)
-            return func(*args, **kwargs)
-
-    return wrapper
-
-
-def implement_span_delegator(
-    obj: _IffHasAttrDescriptor, attributes: Attributes = None
-):
-    """Wrap the descriptor's fn with a span.
-
-    Args:
-        obj: An instance of _IffHasAttrDescriptor
-        attributes: Attributes to apply to the span
-    """
-    # Don't instrument inherited delegators
-    if hasattr(obj, "_otel_original_fn"):
-        logger.debug("Already instrumented: %s", obj.fn.__qualname__)
-        return
-    logger.debug("Instrumenting: %s", obj.fn.__qualname__)
-    attributes = attributes or {}
-    setattr(obj, "_otel_original_fn", getattr(obj, "fn"))
-    setattr(
-        obj,
-        "fn",
-        implement_span_function(obj.fn, obj.fn.__qualname__, attributes),
-    )
-
-
-def get_delegator(
-    estimator: Type[BaseEstimator], method_name: str
-) -> Union[_IffHasAttrDescriptor, None]:
-    """Get the delegator from a class method or None.
-
-    Args:
-        estimator: A class derived from ``sklearn``'s ``BaseEstimator``.
-        method_name (str): The method name of the estimator on which to
-          check for delegation.
-
-    Returns:
-        The delegator, if one exists, otherwise None.
-    """
-    class_attr = getattr(estimator, method_name)
-    if getattr(class_attr, "__closure__", None) is not None:
-        for cell in class_attr.__closure__:
-            if isinstance(cell.cell_contents, _IffHasAttrDescriptor):
-                return cell.cell_contents
-    return None
-
-
-def get_base_estimators(packages: List[str]) -> Dict[str, Type[BaseEstimator]]:
-    """Walk package hierarchies to get BaseEstimator-derived classes.
-
-    Args:
-        packages (list(str)): A list of package names to instrument.
-
-    Returns:
-        A dictionary of qualnames and classes inheriting from
-        ``BaseEstimator``.
-    """
-    klasses = {}
-    for package_name in packages:
-        lib = import_module(package_name)
-        package_dir = os.path.dirname(lib.__file__)
-        for _, module_name, _ in iter_modules([package_dir]):
-            # import the module and iterate through its attributes
-            try:
-                module = import_module(package_name + "." + module_name)
-            except ImportError:
-                logger.warning(
-                    "Unable to import %s.%s", package_name, module_name
-                )
-                continue
-            for attribute_name in dir(module):
-                attrib = getattr(module, attribute_name)
-                if isclass(attrib) and issubclass(attrib, BaseEstimator):
-                    klasses[
-                        ".".join([package_name, module_name, attribute_name])
-                    ] = attrib
-    return klasses
-
-
-# Methods on which spans should be applied.
-DEFAULT_METHODS = [
-    "fit",
-    "transform",
-    "predict",
-    "predict_proba",
-    "_fit",
-    "_transform",
-    "_predict",
-    "_predict_proba",
-]
-
-# Classes and their attributes which contain a list of tupled estimators
-# through which we should walk recursively for estimators.
-DEFAULT_NAMEDTUPLE_ATTRIBS = {
-    Pipeline: ["steps"],
-    FeatureUnion: ["transformer_list"],
-}
-
-# Classes and their attributes which contain an estimator or sequence of
-# estimators through which we should walk recursively for estimators.
-DEFAULT_ATTRIBS = {}
-
-# Classes (including children) explicitly excluded from autoinstrumentation
-DEFAULT_EXCLUDE_CLASSES = [BaseDecisionTree]
-
-# Default packages for autoinstrumentation
-DEFAULT_PACKAGES = ["sklearn"]
-
-
-class SklearnInstrumentor(BaseInstrumentor):
-    """Instrument a fitted sklearn model with opentelemetry spans.
-
-    Instrument methods of ``BaseEstimator``-derived components in a sklearn
-    model.  The assumption is that a machine learning model ``Pipeline`` (or
-    class descendent) is being instrumented with opentelemetry. Within a
-    ``Pipeline`` is some hierarchy of estimators and transformers.
-
-    The ``instrument_estimator`` method walks this hierarchy of estimators,
-    implementing each of the defined methods with its own span.
-
-    Certain estimators in the sklearn ecosystem contain other estimators as
-    instance attributes. Support for walking this embedded sub-hierarchy is
-    supported with ``recurse_attribs``. This argument is a dictionary
-    with classes as keys, and a list of attributes representing embedded
-    estimators as values. By default, ``recurse_attribs`` is empty.
-
-    Similar to Pipelines, there are also estimators which have class attributes
-    as a list of 2-tuples; for instance, the ``FeatureUnion`` and its attribute
-    ``transformer_list``. Instrumenting estimators like this is also
-    supported through the ``recurse_namedtuple_attribs`` argument. This
-    argument is a dictionary with classes as keys, and a list of attribute
-    names representing the namedtuple list(s). By default, the
-    ``recurse_namedtuple_attribs`` dictionary supports
-    ``Pipeline`` with ``steps``, and ``FeatureUnion`` with
-    ``transformer_list``.
-
-    Note that spans will not be generated for any child transformer whose
-    parent transformer has ``n_jobs`` parameter set to anything besides
-    ``None`` or ``1``.
-
-    Package instrumentation example:
-
-    .. code-block:: python
-
-        from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
-
-        # instrument the sklearn library
-        SklearnInstrumentor().instrument()
-
-        # instrument several sklearn-compatible libraries
-        packages = ["sklearn", "lightgbm", "xgboost"]
-        SklearnInstrumentor(packages=packages).instrument()
-
-
-    Model instrumentation example:
-
-    .. code-block:: python
-
-        from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
-        from sklearn.datasets import load_iris
-        from sklearn.ensemble import RandomForestClassifier
-        from sklearn.model_selection import train_test_split
-        from sklearn.pipeline import Pipeline
-
-        X, y = load_iris(return_X_y=True)
-        X_train, X_test, y_train, y_test = train_test_split(X, y)
-
-        model = Pipeline(
-            [
-                ("class", RandomForestClassifier(n_estimators=10)),
-            ]
-        )
-
-        model.fit(X_train, y_train)
-
-        SklearnInstrumentor().instrument_estimator(model)
-
-    Args:
-        methods (list): A list of method names on which to instrument a span.
-          This list of methods will be checked on all estimators in the model
-          hierarchy. Used in package and model instrumentation
-        recurse_attribs (dict): A dictionary of ``BaseEstimator``-derived
-          sklearn classes as keys, with values being a list of attributes. Each
-          attribute represents either an estimator or list of estimators on
-          which to also implement spans. An example is
-          ``RandomForestClassifier`` and its attribute ``estimators_``. Used
-          in model instrumentation only.
-        recurse_namedtuple_attribs (dict): A dictionary of ``BaseEstimator``-
-          derived sklearn types as keys, with values being a list of
-          attribute names. Each attribute represents a list of 2-tuples in
-          which the first element is the estimator name, and the second
-          element is the estimator. Defaults include sklearn's ``Pipeline``
-          and its attribute ``steps``, and the ``FeatureUnion`` and its
-          attribute ``transformer_list``. Used in model instrumentation only.
-        packages: A list of sklearn-compatible packages to
-          instrument. Used with package instrumentation only.
-        exclude_classes: A list of classes to exclude from instrumentation.
-          Child classes are also excluded. Default is sklearn's
-          ``[BaseDecisionTree]``.
-    """
-
-    def __new__(cls, *args, **kwargs):
-        """Override new.
-
-        The base class' new method passes args and kwargs. We override because
-        we init the class with configuration and Python raises TypeError when
-        additional arguments are passed to the object.__new__() method.
-        """
-        if cls._instance is None:
-            cls._instance = object.__new__(cls)
-
-        return cls._instance
-
-    def __init__(
-        self,
-        methods: List[str] = None,
-        recurse_attribs: Dict[Type[BaseEstimator], List[str]] = None,
-        recurse_namedtuple_attribs: Dict[
-            Type[BaseEstimator], List[str]
-        ] = None,
-        packages: List[str] = None,
-        exclude_classes: List[Type] = None,
-    ):
-        self.methods = methods or DEFAULT_METHODS
-        self.recurse_attribs = recurse_attribs or DEFAULT_ATTRIBS
-        self.recurse_namedtuple_attribs = (
-            recurse_namedtuple_attribs or DEFAULT_NAMEDTUPLE_ATTRIBS
-        )
-        self.packages = packages or DEFAULT_PACKAGES
-        if exclude_classes is None:
-            self.exclude_classes = tuple(DEFAULT_EXCLUDE_CLASSES)
-        else:
-            self.exclude_classes = tuple(exclude_classes)
-
-    def instrumentation_dependencies(self) -> Collection[str]:
-        return _instruments
-
-    def _instrument(self, **kwargs):
-        """Instrument the library, and any additional specified on init."""
-        klasses = get_base_estimators(packages=self.packages)
-        attributes = kwargs.get("attributes")
-        for _, klass in klasses.items():
-            if issubclass(klass, self.exclude_classes):
-                logger.debug("Not instrumenting (excluded): %s", str(klass))
-            else:
-                logger.debug("Instrumenting: %s", str(klass))
-                for method_name in self.methods:
-                    if hasattr(klass, method_name):
-                        self._instrument_class_method(
-                            estimator=klass,
-                            method_name=method_name,
-                            attributes=attributes,
-                        )
-
-    def _uninstrument(self, **kwargs):
-        """Uninstrument the library"""
-        klasses = get_base_estimators(packages=self.packages)
-        for _, klass in klasses.items():
-            logger.debug("Uninstrumenting: %s", str(klass))
-            for method_name in self.methods:
-                if hasattr(klass, method_name):
-                    self._uninstrument_class_method(
-                        estimator=klass, method_name=method_name
-                    )
-
-    def instrument_estimator(
-        self, estimator: BaseEstimator, attributes: Attributes = None
-    ):
-        """Instrument a fitted estimator and its hierarchy where configured.
-
-        Args:
-            estimator (sklearn.base.BaseEstimator): A fitted ``sklearn``
-              estimator, typically a ``Pipeline`` instance.
-            attributes (dict): Attributes to attach to the spans.
-        """
-        if isinstance(estimator, self.exclude_classes):
-            logger.debug(
-                "Not instrumenting (excluded): %s",
-                estimator.__class__.__name__,
-            )
-            return
-
-        if isinstance(
-            estimator, tuple(self.recurse_namedtuple_attribs.keys())
-        ):
-            self._instrument_estimator_namedtuple(
-                estimator=estimator, attributes=attributes
-            )
-
-        if isinstance(estimator, tuple(self.recurse_attribs.keys())):
-            self._instrument_estimator_attribute(
-                estimator=estimator, attributes=attributes
-            )
-
-        for method_name in self.methods:
-            if hasattr(estimator, method_name):
-                self._instrument_instance_method(
-                    estimator=estimator,
-                    method_name=method_name,
-                    attributes=attributes,
-                )
-
-    def uninstrument_estimator(self, estimator: BaseEstimator):
-        """Uninstrument a fitted estimator and its hierarchy where configured.
-
-        Args:
-            estimator (sklearn.base.BaseEstimator): A fitted ``sklearn``
-              estimator, typically a ``Pipeline`` instance.
-        """
-        if isinstance(estimator, self.exclude_classes):
-            logger.debug(
-                "Not uninstrumenting (excluded): %s",
-                estimator.__class__.__name__,
-            )
-            return
-
-        if isinstance(
-            estimator, tuple(self.recurse_namedtuple_attribs.keys())
-        ):
-            self._uninstrument_estimator_namedtuple(estimator=estimator)
-
-        if isinstance(estimator, tuple(self.recurse_attribs.keys())):
-            self._uninstrument_estimator_attribute(estimator=estimator)
-
-        for method_name in self.methods:
-            if hasattr(estimator, method_name):
-                self._uninstrument_instance_method(
-                    estimator=estimator, method_name=method_name
-                )
-
-    def _check_instrumented(
-        self,
-        estimator: Union[BaseEstimator, Type[BaseEstimator]],
-        method_name: str,
-    ) -> bool:
-        """Check an estimator-method is instrumented.
-
-        Args:
-            estimator (BaseEstimator): A class or instance of an ``sklearn``
-              estimator.
-            method_name (str): The method name of the estimator on which to
-              check for instrumentation.
-        """
-        orig_method_name = "_otel_original_" + method_name
-        has_original = hasattr(estimator, orig_method_name)
-        orig_class, orig_method = getattr(
-            estimator, orig_method_name, (None, None)
-        )
-        same_class = orig_class == estimator
-        if has_original and same_class:
-            class_method = self._unwrap_function(
-                getattr(estimator, method_name)
-            )
-            # if they match then the subclass doesn't override
-            # if they don't then the overridden method needs instrumentation
-            if class_method.__name__ == orig_method.__name__:
-                return True
-        return False
-
-    def _uninstrument_class_method(
-        self, estimator: Type[BaseEstimator], method_name: str
-    ):
-        """Uninstrument a class method.
-
-        Replaces the patched method with the original, and deletes the
-        attribute which stored the original method.
-
-        Args:
-            estimator (BaseEstimator): A class or instance of an ``sklearn``
-              estimator.
-            method_name (str): The method name of the estimator on which to
-              apply a span.
-        """
-        orig_method_name = "_otel_original_" + method_name
-        if isclass(estimator):
-            qualname = estimator.__qualname__
-        else:
-            qualname = estimator.__class__.__qualname__
-        delegator = get_delegator(estimator, method_name)
-        if self._check_instrumented(estimator, method_name):
-            logger.debug(
-                "Uninstrumenting: %s.%s",
-                qualname,
-                method_name,
-            )
-            _, orig_method = getattr(estimator, orig_method_name)
-            setattr(
-                estimator,
-                method_name,
-                orig_method,
-            )
-            delattr(estimator, orig_method_name)
-        elif delegator is not None:
-            if not hasattr(delegator, "_otel_original_fn"):
-                logger.debug(
-                    "Already uninstrumented: %s.%s",
-                    qualname,
-                    method_name,
-                )
-                return
-            setattr(
-                delegator,
-                "fn",
-                getattr(delegator, "_otel_original_fn"),
-            )
-            delattr(delegator, "_otel_original_fn")
-        else:
-            logger.debug(
-                "Already uninstrumented: %s.%s",
-                qualname,
-                method_name,
-            )
-
-    def _uninstrument_instance_method(
-        self, estimator: BaseEstimator, method_name: str
-    ):
-        """Uninstrument an instance method.
-
-        Replaces the patched method with the original, and deletes the
-        attribute which stored the original method.
-
-        Args:
-            estimator (BaseEstimator): A class or instance of an ``sklearn``
-              estimator.
-            method_name (str): The method name of the estimator on which to
-              apply a span.
-        """
-        orig_method_name = "_otel_original_" + method_name
-        if isclass(estimator):
-            qualname = estimator.__qualname__
-        else:
-            qualname = estimator.__class__.__qualname__
-        if self._check_instrumented(estimator, method_name):
-            logger.debug(
-                "Uninstrumenting: %s.%s",
-                qualname,
-                method_name,
-            )
-            _, orig_method = getattr(estimator, orig_method_name)
-            setattr(
-                estimator,
-                method_name,
-                orig_method,
-            )
-            delattr(estimator, orig_method_name)
-        else:
-            logger.debug(
-                "Already uninstrumented: %s.%s",
-                qualname,
-                method_name,
-            )
-
-    def _instrument_class_method(
-        self,
-        estimator: Type[BaseEstimator],
-        method_name: str,
-        attributes: Attributes = None,
-    ):
-        """Instrument an estimator method with a span.
-
-        When instrumenting we attach a tuple of (Class, method) to the
-        attribute ``_otel_original_<method_name>`` for each method. This allows
-        us to replace the patched with the original in uninstrumentation, but
-        also allows proper instrumentation of child classes without
-        instrumenting inherited methods twice.
-
-        Args:
-            estimator (BaseEstimator): A ``BaseEstimator``-derived
-              class
-            method_name (str): The method name of the estimator on which to
-              apply a span.
-            attributes (dict): Attributes to attach to the spans.
-        """
-        if self._check_instrumented(estimator, method_name):
-            logger.debug(
-                "Already instrumented: %s.%s",
-                estimator.__qualname__,
-                method_name,
-            )
-            return
-        class_attr = getattr(estimator, method_name)
-        delegator = get_delegator(estimator, method_name)
-        if isinstance(class_attr, property):
-            logger.debug(
-                "Not instrumenting found property: %s.%s",
-                estimator.__qualname__,
-                method_name,
-            )
-        elif delegator is not None:
-            implement_span_delegator(delegator)
-        else:
-            setattr(
-                estimator,
-                "_otel_original_" + method_name,
-                (estimator, class_attr),
-            )
-            setattr(
-                estimator,
-                method_name,
-                implement_span_estimator(class_attr, estimator, attributes),
-            )
-
-    def _unwrap_function(self, function):
-        """Fetch the function underlying any decorators"""
-        if hasattr(function, "__wrapped__"):
-            return self._unwrap_function(function.__wrapped__)
-        return function
-
-    def _instrument_instance_method(
-        self,
-        estimator: BaseEstimator,
-        method_name: str,
-        attributes: Attributes = None,
-    ):
-        """Instrument an estimator instance method with a span.
-
-        When instrumenting we attach a tuple of (Class, method) to the
-        attribute ``_otel_original_<method_name>`` for each method. This allows
-        us to replace the patched with the original in unstrumentation.
-
-        Args:
-            estimator (BaseEstimator): A fitted ``sklearn`` estimator.
-            method_name (str): The method name of the estimator on which to
-              apply a span.
-            attributes (dict): Attributes to attach to the spans.
-        """
-        if self._check_instrumented(estimator, method_name):
-            logger.debug(
-                "Already instrumented: %s.%s",
-                estimator.__class__.__qualname__,
-                method_name,
-            )
-            return
-
-        class_attr = getattr(type(estimator), method_name, None)
-        if isinstance(class_attr, property):
-            logger.debug(
-                "Not instrumenting found property: %s.%s",
-                estimator.__class__.__qualname__,
-                method_name,
-            )
-        else:
-            method = getattr(estimator, method_name)
-            setattr(
-                estimator, "_otel_original_" + method_name, (estimator, method)
-            )
-            setattr(
-                estimator,
-                method_name,
-                implement_span_estimator(method, estimator, attributes),
-            )
-
-    def _instrument_estimator_attribute(
-        self, estimator: BaseEstimator, attributes: Attributes = None
-    ):
-        """Instrument instance attributes which also contain estimators.
-
-        Handle instance attributes which are also estimators, are a list
-        (Sequence) of estimators, or are mappings (dictionary) in which
-        the values are estimators.
-
-        Examples include ``RandomForestClassifier`` and
-        ``MultiOutputRegressor`` instances which have attributes
-        ``estimators_`` attributes.
-
-        Args:
-            estimator (BaseEstimator): A fitted ``sklearn`` estimator, with an
-              attribute which also contains an estimator or collection of
-              estimators.
-            attributes (dict): Attributes to attach to the spans.
-        """
-        attribs = self.recurse_attribs.get(estimator.__class__, [])
-        for attrib in attribs:
-            attrib_value = getattr(estimator, attrib)
-            if isinstance(attrib_value, Sequence):
-                for value in attrib_value:
-                    self.instrument_estimator(
-                        estimator=value, attributes=attributes
-                    )
-            elif isinstance(attrib_value, MutableMapping):
-                for value in attrib_value.values():
-                    self.instrument_estimator(
-                        estimator=value, attributes=attributes
-                    )
-            else:
-                self.instrument_estimator(
-                    estimator=attrib_value, attributes=attributes
-                )
-
-    def _instrument_estimator_namedtuple(
-        self, estimator: BaseEstimator, attributes: Attributes = None
-    ):
-        """Instrument attributes with (name, estimator) tupled components.
-
-        Examples include Pipeline and FeatureUnion instances which
-        have attributes steps and transformer_list, respectively.
-
-        Args:
-            estimator: A fitted sklearn estimator, with an attribute which also
-              contains an estimator or collection of estimators.
-            attributes (dict): Attributes to attach to the spans.
-        """
-        attribs = self.recurse_namedtuple_attribs.get(estimator.__class__, [])
-        for attrib in attribs:
-            for _, est in getattr(estimator, attrib):
-                self.instrument_estimator(estimator=est, attributes=attributes)
-
-    def _uninstrument_estimator_attribute(self, estimator: BaseEstimator):
-        """Uninstrument instance attributes which also contain estimators.
-
-        Handle instance attributes which are also estimators, are a list
-        (Sequence) of estimators, or are mappings (dictionary) in which
-        the values are estimators.
-
-        Examples include ``RandomForestClassifier`` and
-        ``MultiOutputRegressor`` instances which have attributes
-        ``estimators_`` attributes.
-
-        Args:
-            estimator (BaseEstimator): A fitted ``sklearn`` estimator, with an
-              attribute which also contains an estimator or collection of
-              estimators.
-        """
-        attribs = self.recurse_attribs.get(estimator.__class__, [])
-        for attrib in attribs:
-            attrib_value = getattr(estimator, attrib)
-            if isinstance(attrib_value, Sequence):
-                for value in attrib_value:
-                    self.uninstrument_estimator(estimator=value)
-            elif isinstance(attrib_value, MutableMapping):
-                for value in attrib_value.values():
-                    self.uninstrument_estimator(estimator=value)
-            else:
-                self.uninstrument_estimator(estimator=attrib_value)
-
-    def _uninstrument_estimator_namedtuple(self, estimator: BaseEstimator):
-        """Uninstrument attributes with (name, estimator) tupled components.
-
-        Examples include Pipeline and FeatureUnion instances which
-        have attributes steps and transformer_list, respectively.
-
-        Args:
-            estimator: A fitted sklearn estimator, with an attribute which also
-              contains an estimator or collection of estimators.
-        """
-        attribs = self.recurse_namedtuple_attribs.get(estimator.__class__, [])
-        for attrib in attribs:
-            for _, est in getattr(estimator, attrib):
-                self.uninstrument_estimator(estimator=est)
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/package.py b/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/package.py
deleted file mode 100644
index 41db461453..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/package.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Copyright 2020, OpenTelemetry Authors
-#
-# Licensed 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.
-
-
-_instruments = ("scikit-learn ~= 0.24.0",)
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/version.py b/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/version.py
deleted file mode 100644
index deef26c62f..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/version.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright 2020, OpenTelemetry Authors
-#
-# Licensed 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.
-
-__version__ = "0.47b0.dev"
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/test-requirements.txt b/instrumentation/opentelemetry-instrumentation-sklearn/test-requirements.txt
deleted file mode 100644
index af341e16ed..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/test-requirements.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-asgiref==3.7.2
-Deprecated==1.2.14
-importlib-metadata==6.11.0
-iniconfig==2.0.0
-joblib==1.3.2
-numpy==1.24.4
-packaging==24.0
-pluggy==1.5.0
-py-cpuinfo==9.0.0
-pytest==7.4.4
-scikit-learn==0.24.2
-scipy==1.10.1
-threadpoolctl==3.3.0
-tomli==2.0.1
-typing_extensions==4.10.0
-wrapt==1.16.0
-zipp==3.19.2
--e opentelemetry-instrumentation
--e instrumentation/opentelemetry-instrumentation-sklearn
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/tests/__init__.py b/instrumentation/opentelemetry-instrumentation-sklearn/tests/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/tests/fixtures.py b/instrumentation/opentelemetry-instrumentation-sklearn/tests/fixtures.py
deleted file mode 100644
index cf26c0fcf2..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/tests/fixtures.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright 2020, OpenTelemetry Authors
-#
-# Licensed 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.
-
-import numpy as np
-from sklearn.datasets import load_iris
-from sklearn.decomposition import PCA, TruncatedSVD
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split
-from sklearn.pipeline import FeatureUnion, Pipeline
-from sklearn.preprocessing import Normalizer, StandardScaler
-
-X, y = load_iris(return_X_y=True)
-X_train, X_test, y_train, y_test = train_test_split(X, y)
-
-
-def pipeline():
-    """A dummy model that has a bunch of components that we can test."""
-    model = Pipeline(
-        [
-            ("scaler", StandardScaler()),
-            ("normal", Normalizer()),
-            (
-                "union",
-                FeatureUnion(
-                    [
-                        ("pca", PCA(n_components=1)),
-                        ("svd", TruncatedSVD(n_components=2)),
-                    ],
-                    n_jobs=1,  # parallelized components won't generate spans
-                ),
-            ),
-            ("class", RandomForestClassifier(n_estimators=10)),
-        ]
-    )
-    model.fit(X_train, y_train)
-    return model
-
-
-def random_input():
-    """A random record from the feature set."""
-    rows = X.shape[0]
-    random_row = np.random.choice(rows, size=1)
-    return X[random_row, :]
diff --git a/instrumentation/opentelemetry-instrumentation-sklearn/tests/test_sklearn.py b/instrumentation/opentelemetry-instrumentation-sklearn/tests/test_sklearn.py
deleted file mode 100644
index db69761ece..0000000000
--- a/instrumentation/opentelemetry-instrumentation-sklearn/tests/test_sklearn.py
+++ /dev/null
@@ -1,190 +0,0 @@
-# Copyright 2020, OpenTelemetry Authors
-#
-# Licensed 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 sklearn.ensemble import RandomForestClassifier
-
-# pylint: disable=no-name-in-module
-from opentelemetry.instrumentation.sklearn import (
-    DEFAULT_EXCLUDE_CLASSES,
-    DEFAULT_METHODS,
-    SklearnInstrumentor,
-    get_base_estimators,
-    get_delegator,
-)
-from opentelemetry.test.test_base import TestBase
-from opentelemetry.trace import SpanKind
-
-from .fixtures import pipeline, random_input
-
-
-def assert_instrumented(base_estimators):
-    for _, estimator in base_estimators.items():
-        for method_name in DEFAULT_METHODS:
-            original_method_name = "_otel_original_" + method_name
-            if issubclass(estimator, tuple(DEFAULT_EXCLUDE_CLASSES)):
-                assert not hasattr(estimator, original_method_name)
-                continue
-            class_attr = getattr(estimator, method_name, None)
-            if isinstance(class_attr, property):
-                assert not hasattr(estimator, original_method_name)
-                continue
-            delegator = None
-            if hasattr(estimator, method_name):
-                delegator = get_delegator(estimator, method_name)
-            if delegator is not None:
-                assert hasattr(delegator, "_otel_original_fn")
-            elif hasattr(estimator, method_name):
-                assert hasattr(estimator, original_method_name)
-
-
-def assert_uninstrumented(base_estimators):
-    for _, estimator in base_estimators.items():
-        for method_name in DEFAULT_METHODS:
-            original_method_name = "_otel_original_" + method_name
-            if issubclass(estimator, tuple(DEFAULT_EXCLUDE_CLASSES)):
-                assert not hasattr(estimator, original_method_name)
-                continue
-            class_attr = getattr(estimator, method_name, None)
-            if isinstance(class_attr, property):
-                assert not hasattr(estimator, original_method_name)
-                continue
-            delegator = None
-            if hasattr(estimator, method_name):
-                delegator = get_delegator(estimator, method_name)
-            if delegator is not None:
-                assert not hasattr(delegator, "_otel_original_fn")
-            elif hasattr(estimator, method_name):
-                assert not hasattr(estimator, original_method_name)
-
-
-class TestSklearn(TestBase):
-    def test_package_instrumentation(self):
-        ski = SklearnInstrumentor()
-
-        base_estimators = get_base_estimators(packages=["sklearn"])
-
-        model = pipeline()
-
-        ski.instrument()
-        assert_instrumented(base_estimators)
-
-        x_test = random_input()
-
-        model.predict(x_test)
-
-        spans = self.memory_exporter.get_finished_spans()
-        self.assertEqual(len(spans), 8)
-        self.memory_exporter.clear()
-
-        ski.uninstrument()
-        assert_uninstrumented(base_estimators)
-
-        model = pipeline()
-        x_test = random_input()
-
-        model.predict(x_test)
-
-        spans = self.memory_exporter.get_finished_spans()
-        self.assertEqual(len(spans), 0)
-
-    def test_span_properties(self):
-        """Test that we get all of the spans we expect."""
-        model = pipeline()
-        ski = SklearnInstrumentor()
-        ski.instrument_estimator(estimator=model)
-
-        x_test = random_input()
-
-        model.predict(x_test)
-
-        spans = self.memory_exporter.get_finished_spans()
-        self.assertEqual(len(spans), 8)
-        span = spans[0]
-        self.assertEqual(span.name, "StandardScaler.transform")
-        self.assertEqual(span.kind, SpanKind.INTERNAL)
-        self.assertEqual(span.parent.span_id, spans[-1].context.span_id)
-        span = spans[1]
-        self.assertEqual(span.name, "Normalizer.transform")
-        self.assertEqual(span.kind, SpanKind.INTERNAL)
-        self.assertEqual(span.parent.span_id, spans[-1].context.span_id)
-        span = spans[2]
-        self.assertEqual(span.name, "PCA.transform")
-        self.assertEqual(span.kind, SpanKind.INTERNAL)
-        self.assertEqual(span.parent.span_id, spans[4].context.span_id)
-        span = spans[3]
-        self.assertEqual(span.name, "TruncatedSVD.transform")
-        self.assertEqual(span.kind, SpanKind.INTERNAL)
-        self.assertEqual(span.parent.span_id, spans[4].context.span_id)
-        span = spans[4]
-        self.assertEqual(span.name, "FeatureUnion.transform")
-        self.assertEqual(span.kind, SpanKind.INTERNAL)
-        self.assertEqual(span.parent.span_id, spans[-1].context.span_id)
-        span = spans[5]
-        self.assertEqual(span.name, "RandomForestClassifier.predict_proba")
-        self.assertEqual(span.kind, SpanKind.INTERNAL)
-        self.assertEqual(span.parent.span_id, spans[6].context.span_id)
-        span = spans[6]
-        self.assertEqual(span.name, "RandomForestClassifier.predict")
-        self.assertEqual(span.kind, SpanKind.INTERNAL)
-        self.assertEqual(span.parent.span_id, spans[-1].context.span_id)
-        span = spans[7]
-        self.assertEqual(span.name, "Pipeline.predict")
-        self.assertEqual(span.kind, SpanKind.INTERNAL)
-
-        self.memory_exporter.clear()
-
-        # uninstrument
-        ski.uninstrument_estimator(estimator=model)
-        x_test = random_input()
-        model.predict(x_test)
-        spans = self.memory_exporter.get_finished_spans()
-        self.assertEqual(len(spans), 0)
-
-    def test_attrib_config(self):
-        """Test that the attribute config makes spans on the decision trees."""
-        model = pipeline()
-        attrib_config = {RandomForestClassifier: ["estimators_"]}
-        ski = SklearnInstrumentor(
-            recurse_attribs=attrib_config,
-            exclude_classes=[],  # decision trees excluded by default
-        )
-        ski.instrument_estimator(estimator=model)
-
-        x_test = random_input()
-        model.predict(x_test)
-
-        spans = self.memory_exporter.get_finished_spans()
-        self.assertEqual(len(spans), 8 + model.steps[-1][-1].n_estimators)
-
-        self.memory_exporter.clear()
-
-        ski.uninstrument_estimator(estimator=model)
-        x_test = random_input()
-        model.predict(x_test)
-        spans = self.memory_exporter.get_finished_spans()
-        self.assertEqual(len(spans), 0)
-
-    def test_span_attributes(self):
-        model = pipeline()
-        attributes = {"model_name": "random_forest_model"}
-        ski = SklearnInstrumentor()
-        ski.instrument_estimator(estimator=model, attributes=attributes)
-
-        x_test = random_input()
-
-        model.predict(x_test)
-
-        spans = self.memory_exporter.get_finished_spans()
-        for span in spans:
-            assert span.attributes["model_name"] == "random_forest_model"
diff --git a/opentelemetry-contrib-instrumentations/pyproject.toml b/opentelemetry-contrib-instrumentations/pyproject.toml
index 74c28f38cf..f83b9d5ee5 100644
--- a/opentelemetry-contrib-instrumentations/pyproject.toml
+++ b/opentelemetry-contrib-instrumentations/pyproject.toml
@@ -66,7 +66,6 @@ dependencies = [
     "opentelemetry-instrumentation-redis==0.47b0.dev",
     "opentelemetry-instrumentation-remoulade==0.47b0.dev",
     "opentelemetry-instrumentation-requests==0.47b0.dev",
-    "opentelemetry-instrumentation-sklearn==0.47b0.dev",
     "opentelemetry-instrumentation-sqlalchemy==0.47b0.dev",
     "opentelemetry-instrumentation-sqlite3==0.47b0.dev",
     "opentelemetry-instrumentation-starlette==0.47b0.dev",
diff --git a/opentelemetry-instrumentation/src/opentelemetry/instrumentation/bootstrap_gen.py b/opentelemetry-instrumentation/src/opentelemetry/instrumentation/bootstrap_gen.py
index 3dfd97e0b2..3f4c78862f 100644
--- a/opentelemetry-instrumentation/src/opentelemetry/instrumentation/bootstrap_gen.py
+++ b/opentelemetry-instrumentation/src/opentelemetry/instrumentation/bootstrap_gen.py
@@ -152,10 +152,6 @@
         "library": "requests ~= 2.0",
         "instrumentation": "opentelemetry-instrumentation-requests==0.47b0.dev",
     },
-    {
-        "library": "scikit-learn ~= 0.24.0",
-        "instrumentation": "opentelemetry-instrumentation-sklearn==0.47b0.dev",
-    },
     {
         "library": "sqlalchemy",
         "instrumentation": "opentelemetry-instrumentation-sqlalchemy==0.47b0.dev",
diff --git a/tox.ini b/tox.ini
index 529e2a76c0..aac890ed7c 100644
--- a/tox.ini
+++ b/tox.ini
@@ -273,10 +273,6 @@ envlist =
     pypy3-test-instrumentation-celery
     lint-instrumentation-celery
 
-    ; opentelemetry-instrumentation-sklearn
-    py3{8}-test-instrumentation-sklearn
-    lint-instrumentation-sklearn
-
     ; opentelemetry-instrumentation-system-metrics
     py3{8,9,10,11,12}-test-instrumentation-system-metrics
     pypy3-test-instrumentation-system-metrics
@@ -701,12 +697,6 @@ commands_pre =
   prometheus: pip install opentelemetry-test-utils@{env:CORE_REPO}\#egg=opentelemetry-test-utils&subdirectory=tests/opentelemetry-test-utils
   prometheus: pip install -r {toxinidir}/exporter/opentelemetry-exporter-prometheus-remote-write/test-requirements.txt
 
-  sklearn: pip install opentelemetry-api@{env:CORE_REPO}\#egg=opentelemetry-api&subdirectory=opentelemetry-api
-  sklearn: pip install opentelemetry-semantic-conventions@{env:CORE_REPO}\#egg=opentelemetry-semantic-conventions&subdirectory=opentelemetry-semantic-conventions
-  sklearn: pip install opentelemetry-sdk@{env:CORE_REPO}\#egg=opentelemetry-sdk&subdirectory=opentelemetry-sdk
-  sklearn: pip install opentelemetry-test-utils@{env:CORE_REPO}\#egg=opentelemetry-test-utils&subdirectory=tests/opentelemetry-test-utils
-  sklearn: pip install -r {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn/test-requirements.txt
-
   sqlalchemy: pip install opentelemetry-api@{env:CORE_REPO}\#egg=opentelemetry-api&subdirectory=opentelemetry-api
   sqlalchemy: pip install opentelemetry-semantic-conventions@{env:CORE_REPO}\#egg=opentelemetry-semantic-conventions&subdirectory=opentelemetry-semantic-conventions
   sqlalchemy: pip install opentelemetry-sdk@{env:CORE_REPO}\#egg=opentelemetry-sdk&subdirectory=opentelemetry-sdk
@@ -1021,12 +1011,6 @@ commands =
   lint-instrumentation-requests: flake8 --config {toxinidir}/.flake8 {toxinidir}/instrumentation/opentelemetry-instrumentation-requests
   lint-instrumentation-requests: sh -c "cd instrumentation && pylint --rcfile ../.pylintrc opentelemetry-instrumentation-requests"
 
-  test-instrumentation-sklearn: pytest {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn/tests {posargs}
-  lint-instrumentation-sklearn: black --diff --check --config {toxinidir}/pyproject.toml {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn
-  lint-instrumentation-sklearn: isort --diff --check-only --settings-path {toxinidir}/.isort.cfg {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn
-  lint-instrumentation-sklearn: flake8 --config {toxinidir}/.flake8 {toxinidir}/instrumentation/opentelemetry-instrumentation-sklearn
-  lint-instrumentation-sklearn: sh -c "cd instrumentation && pylint --rcfile ../.pylintrc opentelemetry-instrumentation-sklearn"
-
   test-instrumentation-sqlalchemy: pytest {toxinidir}/instrumentation/opentelemetry-instrumentation-sqlalchemy/tests {posargs}
   lint-instrumentation-sqlalchemy: black --diff --check --config {toxinidir}/pyproject.toml {toxinidir}/instrumentation/opentelemetry-instrumentation-sqlalchemy
   lint-instrumentation-sqlalchemy: isort --diff --check-only --settings-path {toxinidir}/.isort.cfg {toxinidir}/instrumentation/opentelemetry-instrumentation-sqlalchemy