diff --git a/docs/_templates/autosummary/base.rst b/docs/_templates/autosummary/base.rst
new file mode 100644
index 000000000..3fe9858d7
--- /dev/null
+++ b/docs/_templates/autosummary/base.rst
@@ -0,0 +1,5 @@
+{{ objname | escape | underline }}
+
+.. currentmodule:: {{ module }}
+
+.. auto{{ objtype }}:: {{ objname }}
diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst
new file mode 100644
index 000000000..574b853a5
--- /dev/null
+++ b/docs/_templates/autosummary/class.rst
@@ -0,0 +1,29 @@
+{{ objname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+   {% block methods %}
+   .. automethod:: __init__
+
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/docs/api_reference.rst b/docs/api_reference.rst
index b47e912fa..2d85c3ab4 100644
--- a/docs/api_reference.rst
+++ b/docs/api_reference.rst
@@ -4,23 +4,50 @@ API Reference
 This reference provides detailed documentation for all modules, classes, and
 methods in the current release of PyMC experimental.
 
+.. currentmodule:: pymc_experimental
+.. autosummary::
+   :toctree: generated/
 
-.. toctree::
-  :maxdepth: 2
+   marginal_model.MarginalModel
+   model_builder.ModelBuilder
 
+Inference
+=========
 
-:mod:`pymc_experimental.distributions`
-=============================
+.. currentmodule:: pymc_experimental.inference
+.. autosummary::
+   :toctree: generated/
 
-.. automodule:: pymc_experimental.distributions.histogram_utils
-   :members: histogram_approximation
+   fit
 
 
-:mod:`pymc_experimental.utils`
-=============================
+Distributions
+=============
 
-.. automodule:: pymc_experimental.utils.spline
-   :members: bspline_interpolation
+.. currentmodule:: pymc_experimental.distributions
+.. autosummary::
+   :toctree: generated/
 
-.. automodule:: pymc_experimental.utils.prior
-   :members: prior_from_idata
+   GenExtreme
+   histogram_utils.histogram_approximation
+
+
+Gaussian Processess
+===================
+
+.. currentmodule:: pymc_experimental.gp
+.. autosummary::
+   :toctree: generated/
+
+   latent_approx.HSGP
+
+
+Utils
+=====
+
+.. currentmodule:: pymc_experimental.utils
+.. autosummary::
+   :toctree: generated/
+
+   spline.bspline_interpolation
+   prior.prior_from_idata
diff --git a/docs/conf.py b/docs/conf.py
index f45b3407c..e831458f2 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -56,16 +56,18 @@
 # ones.
 extensions = [
     "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
     "sphinx.ext.viewcode",
     "sphinx.ext.napoleon",
     "sphinx.ext.mathjax",
     "nbsphinx",
+    "matplotlib.sphinxext.plot_directive",
 ]
 
 nbsphinx_execute = "never"
 
 # Add any paths that contain templates here, relative to this directory.
-# templates_path = ["_templates"]
+templates_path = ["_templates"]
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
@@ -81,7 +83,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+# language = None
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -97,7 +99,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "pydata_sphinx_theme"
+html_theme = "pymc_sphinx_theme"
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -110,14 +112,17 @@
     "show_toc_level": 2,
     "navigation_depth": 4,
     "search_bar_text": "Search the docs...",
-    "icon_links": [
-        {
-            "name": "GitHub",
-            "url": "https://github.com/pymc-devs/pymc-experimental",
-            "icon": "fab fa-github-square",
-        },
-    ],
+    "use_search_override": False,
+    "logo": {"text": project},
 }
+html_context = {
+    "github_user": "pymc-devs",
+    "github_repo": "pymc-experimental",
+    "github_version": "main",
+    "doc_path": "docs",
+    "default_mode": "light",
+}
+
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
diff --git a/docs/index.rst b/docs/index.rst
index b5dfef6fd..28cf4c9a3 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -38,16 +38,7 @@ Contributors
 ============
 See the `GitHub contributor page <https://github.com/pymc-devs/pymc-experimental/graphs/contributors>`_.
 
-Contents
-========
-
 .. toctree::
-   :maxdepth: 4
+   :hidden:
 
    api_reference
-
-Indices
-=======
-
-* :ref:`genindex`
-* :ref:`modindex`
diff --git a/pymc_experimental/distributions/continuous.py b/pymc_experimental/distributions/continuous.py
index e214f0081..2de72c769 100644
--- a/pymc_experimental/distributions/continuous.py
+++ b/pymc_experimental/distributions/continuous.py
@@ -60,7 +60,7 @@ def rng_fn(
 
 class GenExtreme(Continuous):
     r"""
-        Univariate Generalized Extreme Value log-likelihood
+    Univariate Generalized Extreme Value log-likelihood
 
     The cdf of this distribution is
 
@@ -119,13 +119,13 @@ class GenExtreme(Continuous):
 
     Parameters
     ----------
-    mu: float
+    mu : float
         Location parameter.
-    sigma: float
+    sigma : float
         Scale parameter (sigma > 0).
-    xi: float
+    xi : float
         Shape parameter
-    scipy: bool
+    scipy : bool
         Whether or not to use the Scipy interpretation of the shape parameter
         (defaults to `False`).
 
diff --git a/pymc_experimental/marginal_model.py b/pymc_experimental/marginal_model.py
index aed93adb5..599509bfb 100644
--- a/pymc_experimental/marginal_model.py
+++ b/pymc_experimental/marginal_model.py
@@ -54,6 +54,7 @@ class MarginalModel(Model):
     Marginalize over a single variable
 
     .. code-block:: python
+
         import pymc as pm
         from pymc_experimental import MarginalModel
 
diff --git a/pymc_experimental/model_builder.py b/pymc_experimental/model_builder.py
index 6d2513349..191b1b1e7 100644
--- a/pymc_experimental/model_builder.py
+++ b/pymc_experimental/model_builder.py
@@ -15,6 +15,7 @@
 
 import hashlib
 import json
+from abc import abstractmethod
 from pathlib import Path
 from typing import Dict, Union
 
@@ -24,12 +25,10 @@
 import pymc as pm
 
 
-class ModelBuilder(pm.Model):
+class ModelBuilder:
     """
     ModelBuilder can be used to provide an easy-to-use API (similar to scikit-learn) for models
     and help with deployment.
-
-    Extends the pymc.Model class.
     """
 
     _model_type = "BaseClass"
@@ -38,8 +37,8 @@ class ModelBuilder(pm.Model):
     def __init__(
         self,
         model_config: Dict,
-        sampler_config: Dict,
-        data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None,
+        data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]],
+        sampler_config: Dict = None,
     ):
         """
         Initializes model configuration and sampler configuration for the model
@@ -48,10 +47,10 @@ def __init__(
         ----------
         model_config : Dictionary
             dictionary of parameters that initialise model configuration. Generated by the user defined create_sample_input method.
-        sampler_config : Dictionary
-            dictionary of parameters that initialise sampler configuration. Generated by the user defined create_sample_input method.
         data : Dictionary
             It is the data we need to train the model on.
+        sampler_config : Dictionary
+            dictionary of parameters that initialise sampler configuration. Generated by the user defined create_sample_input method.
         Examples
         --------
         >>> class LinearModel(ModelBuilder):
@@ -60,20 +59,23 @@ def __init__(
         """
 
         super().__init__()
+        if sampler_config is None:
+            sampler_config = {}
         self.model_config = model_config  # parameters for priors etc.
-        self.sample_config = sampler_config  # parameters for sampling
-        self.idata = None  # inference data object
+        self.sampler_config = sampler_config  # parameters for sampling
         self.data = data
-        self.build()
+        self.idata = (
+            None  # inference data object placeholder, idata is generated during build execution
+        )
 
-    def build(self):
+    def build(self) -> None:
         """
         Builds the defined model.
         """
 
-        with self:
-            self.build_model(self.model_config, self.data)
+        self.build_model(self, self.model_config, self.data)
 
+    @abstractmethod
     def _data_setter(
         self, data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]], x_only: bool = True
     ):
@@ -100,8 +102,10 @@ def _data_setter(
 
         raise NotImplementedError
 
-    @classmethod
-    def create_sample_input(cls):
+    # need a discussion if it's really needed.
+    @staticmethod
+    @abstractmethod
+    def create_sample_input():
         """
         Needs to be implemented by the user in the inherited class.
         Returns examples for data, model_config, sampler_config.
@@ -135,7 +139,7 @@ def create_sample_input(cls):
 
         raise NotImplementedError
 
-    def save(self, fname):
+    def save(self, fname: str) -> None:
         """
         Saves inference data of the model.
 
@@ -159,8 +163,9 @@ def save(self, fname):
         self.idata.to_netcdf(file)
 
     @classmethod
-    def load(cls, fname):
+    def load(cls, fname: str):
         """
+        Creates a ModelBuilder instance from a file,
         Loads inference data for the model.
 
         Parameters
@@ -170,7 +175,7 @@ def load(cls, fname):
 
         Returns
         -------
-        Returns the inference data that is loaded from local system.
+        Returns an instance of ModelBuilder.
 
         Raises
         ------
@@ -187,22 +192,29 @@ def load(cls, fname):
 
         filepath = Path(str(fname))
         idata = az.from_netcdf(filepath)
-        self = cls(
-            json.loads(idata.attrs["model_config"]),
-            json.loads(idata.attrs["sampler_config"]),
-            idata.fit_data.to_dataframe(),
+        if "sampler_config" in idata.attrs:
+            sampler_config = json.loads(idata.attrs["sampler_config"])
+        else:
+            sampler_config = {}
+        model_builder = cls(
+            model_config=json.loads(idata.attrs["model_config"]),
+            sampler_config=sampler_config,
+            data=idata.fit_data.to_dataframe(),
         )
-        self.idata = idata
-        if self.id != idata.attrs["id"]:
+        model_builder.idata = idata
+        model_builder.build()
+        if model_builder.id != idata.attrs["id"]:
             raise ValueError(
-                f"The file '{fname}' does not contain an inference data of the same model or configuration as '{self._model_type}'"
+                f"The file '{fname}' does not contain an inference data of the same model or configuration as '{cls._model_type}'"
             )
 
-        return self
+        return model_builder
 
-    def fit(self, data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None):
+    def fit(
+        self, data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None
+    ) -> az.InferenceData:
         """
-        As the name suggests fit can be used to fit a model using the data that is passed as a parameter.
+        Fit a model using the data passed as a parameter.
         Sets attrs to inference data of the model.
 
         Parameter
@@ -225,20 +237,22 @@ def fit(self, data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None
 
         if data is not None:
             self.data = data
-            self._data_setter(data)
-
-        if self.basic_RVs == []:
             self.build()
+            self._data_setter(data)
 
-        with self:
-            self.idata = pm.sample(**self.sample_config)
+        with self.model:
+            if self.sampler_config:
+                self.idata = pm.sample(**self.sampler_config)
+            else:
+                self.idata = pm.sample()
             self.idata.extend(pm.sample_prior_predictive())
             self.idata.extend(pm.sample_posterior_predictive(self.idata))
 
         self.idata.attrs["id"] = self.id
         self.idata.attrs["model_type"] = self._model_type
         self.idata.attrs["version"] = self.version
-        self.idata.attrs["sampler_config"] = json.dumps(self.sample_config)
+        if self.sampler_config:
+            self.idata.attrs["sampler_config"] = json.dumps(self.sampler_config)
         self.idata.attrs["model_config"] = json.dumps(self.model_config)
         self.idata.add_groups(fit_data=self.data.to_xarray())
         return self.idata
@@ -246,7 +260,8 @@ def fit(self, data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None
     def predict(
         self,
         data_prediction: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None,
-    ):
+        extend_idata: bool = True,
+    ) -> dict:
         """
         Uses model to predict on unseen data and return point prediction of all the samples
 
@@ -254,6 +269,8 @@ def predict(
         ---------
         data_prediction : Dictionary of string and either of numpy array, pandas dataframe or pandas Series
             It is the data we need to make prediction on using the model.
+        extend_idata : Boolean determining whether the predictions should be added to inference data object.
+            Defaults to True.
 
         Returns
         -------
@@ -275,7 +292,8 @@ def predict(
 
         with self.model:  # sample with new input data
             post_pred = pm.sample_posterior_predictive(self.idata)
-
+            if extend_idata:
+                self.idata.extend(post_pred)
         # reshape output
         post_pred = self._extract_samples(post_pred)
         for key in post_pred:
@@ -286,7 +304,8 @@ def predict(
     def predict_posterior(
         self,
         data_prediction: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]] = None,
-    ):
+        extend_idata: bool = True,
+    ) -> Dict[str, np.array]:
         """
         Uses model to predict samples on unseen data.
 
@@ -294,8 +313,8 @@ def predict_posterior(
         ---------
         data_prediction : Dictionary of string and either of numpy array, pandas dataframe or pandas Series
             It is the data we need to make prediction on using the model.
-        point_estimate : bool
-            Adds point like estimate used as mean passed as
+        extend_idata : Boolean determining whether the predictions should be added to inference data object.
+            Defaults to True.
 
         Returns
         -------
@@ -317,6 +336,8 @@ def predict_posterior(
 
         with self.model:  # sample with new input data
             post_pred = pm.sample_posterior_predictive(self.idata)
+            if extend_idata:
+                self.idata.extend(post_pred)
 
         # reshape output
         post_pred = self._extract_samples(post_pred)
@@ -357,5 +378,4 @@ def id(self) -> str:
         hasher.update(str(self.model_config.values()).encode())
         hasher.update(self.version.encode())
         hasher.update(self._model_type.encode())
-        # hasher.update(str(self.sample_config.values()).encode())
         return hasher.hexdigest()[:16]
diff --git a/pymc_experimental/tests/test_model_builder.py b/pymc_experimental/tests/test_model_builder.py
index 1dd67e621..21c6890d2 100644
--- a/pymc_experimental/tests/test_model_builder.py
+++ b/pymc_experimental/tests/test_model_builder.py
@@ -12,7 +12,6 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-
 import hashlib
 import sys
 import tempfile
@@ -29,26 +28,39 @@ class test_ModelBuilder(ModelBuilder):
     _model_type = "LinearModel"
     version = "0.1"
 
-    def build_model(self, model_config, data=None):
-        if data is not None:
-            x = pm.MutableData("x", data["input"].values)
-            y_data = pm.MutableData("y_data", data["output"].values)
-
-        # prior parameters
-        a_loc = model_config["a_loc"]
-        a_scale = model_config["a_scale"]
-        b_loc = model_config["b_loc"]
-        b_scale = model_config["b_scale"]
-        obs_error = model_config["obs_error"]
-
-        # priors
-        a = pm.Normal("a", a_loc, sigma=a_scale)
-        b = pm.Normal("b", b_loc, sigma=b_scale)
-        obs_error = pm.HalfNormal("σ_model_fmc", obs_error)
-
-        # observed data
-        if data is not None:
-            y_model = pm.Normal("y_model", a + b * x, obs_error, shape=x.shape, observed=y_data)
+    def build_model(
+        self,
+        model_instance: ModelBuilder,
+        model_config: dict,
+        data: dict = None,
+        sampler_config: dict = None,
+    ):
+        model_instance.model_config = model_config
+        model_instance.data = data
+        self.model_config = model_config
+        self.sampler_config = sampler_config
+        self.data = data
+
+        with pm.Model() as model_instance.model:
+            if data is not None:
+                x = pm.MutableData("x", data["input"].values)
+                y_data = pm.MutableData("y_data", data["output"].values)
+
+            # prior parameters
+            a_loc = model_config["a_loc"]
+            a_scale = model_config["a_scale"]
+            b_loc = model_config["b_loc"]
+            b_scale = model_config["b_scale"]
+            obs_error = model_config["obs_error"]
+
+            # priors
+            a = pm.Normal("a", a_loc, sigma=a_scale)
+            b = pm.Normal("b", b_loc, sigma=b_scale)
+            obs_error = pm.HalfNormal("σ_model_fmc", obs_error)
+
+            # observed data
+            if data is not None:
+                y_model = pm.Normal("y_model", a + b * x, obs_error, shape=x.shape, observed=y_data)
 
     def _data_setter(self, data: pd.DataFrame):
         with self.model:
@@ -57,7 +69,7 @@ def _data_setter(self, data: pd.DataFrame):
                 pm.set_data({"y_data": data["output"].values})
 
     @classmethod
-    def create_sample_input(cls):
+    def create_sample_input(self):
         x = np.linspace(start=0, stop=1, num=100)
         y = 5 * x + 3
         y = y + np.random.normal(0, 1, len(x))
@@ -81,14 +93,14 @@ def create_sample_input(cls):
         return data, model_config, sampler_config
 
     @staticmethod
-    def initial_build_and_fit(check_idata=True):
+    def initial_build_and_fit(check_idata=True) -> ModelBuilder:
         data, model_config, sampler_config = test_ModelBuilder.create_sample_input()
-        model = test_ModelBuilder(model_config, sampler_config, data)
-        model.fit()
+        model_builder = test_ModelBuilder(model_config, sampler_config, data)
+        model_builder.idata = model_builder.fit(data=data)
         if check_idata:
-            assert model.idata is not None
-            assert "posterior" in model.idata.groups()
-        return model
+            assert model_builder.idata is not None
+            assert "posterior" in model_builder.idata.groups()
+        return model_builder
 
 
 def test_fit():
@@ -105,16 +117,16 @@ def test_fit():
     sys.platform == "win32", reason="Permissions for temp files not granted on windows CI."
 )
 def test_save_load():
-    model = test_ModelBuilder.initial_build_and_fit(False)
+    test_builder = test_ModelBuilder.initial_build_and_fit()
     temp = tempfile.NamedTemporaryFile(mode="w", encoding="utf-8", delete=False)
-    model.save(temp.name)
-    model2 = test_ModelBuilder.load(temp.name)
-    assert model.idata.groups() == model2.idata.groups()
+    test_builder.save(temp.name)
+    test_builder2 = test_ModelBuilder.load(temp.name)
+    assert test_builder.idata.groups() == test_builder2.idata.groups()
 
     x_pred = np.random.uniform(low=0, high=1, size=100)
     prediction_data = pd.DataFrame({"input": x_pred})
-    pred1 = model.predict(prediction_data)
-    pred2 = model2.predict(prediction_data)
+    pred1 = test_builder.predict(prediction_data)
+    pred2 = test_builder2.predict(prediction_data)
     assert pred1["y_model"].shape == pred2["y_model"].shape
     temp.close()
 
diff --git a/requirements-docs.txt b/requirements-docs.txt
index 0b024fea9..185288a8b 100644
--- a/requirements-docs.txt
+++ b/requirements-docs.txt
@@ -1,3 +1,4 @@
 nbsphinx>=0.4.2
 pydata-sphinx-theme>=0.6.3
 sphinx>=4
+git+https://github.com/pymc-devs/pymc-sphinx-theme