diff --git a/examples/examples.ipynb b/examples/examples.ipynb
index faa8664d1..71ef47e65 100644
--- a/examples/examples.ipynb
+++ b/examples/examples.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Yellowbrick Examples \n",
     "\n",
-    "Ths notebook is a sample of the examples that yellowbrick provids."
+    "Ths notebook is a sample of the examples that yellowbrick provides."
    ]
   },
   {
diff --git a/examples/figures/pipeline_prototype.png b/examples/figures/pipeline_prototype.png
new file mode 100644
index 000000000..bffa05fc3
Binary files /dev/null and b/examples/figures/pipeline_prototype.png differ
diff --git a/examples/pipeline.ipynb b/examples/pipeline.ipynb
new file mode 100644
index 000000000..a2c8e5b35
--- /dev/null
+++ b/examples/pipeline.ipynb
@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visual Pipelines \n",
+    "\n",
+    "This notebook demonstrates a proof of concept for a visual pipeline for analytics. \n",
+    "\n",
+    "![Yellowbrick Prototype Pipeline Objects](figures/pipeline_prototype.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "import sys \n",
+    "\n",
+    "# Modify the path \n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "import pandas as pd\n",
+    "import yellowbrick as yb \n",
+    "import matplotlib as mpl \n",
+    "import matplotlib.pyplot as plt "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Datasets \n",
+    "\n",
+    "Note that if datasets do not exist, please see the `download.py` located in this directory. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "FIXTURES  = os.path.join(os.getcwd(), \"data\")\n",
+    "credit    = pd.read_excel(os.path.join(FIXTURES, \"credit.xls\"), header=1)\n",
+    "concrete  = pd.read_excel(os.path.join(FIXTURES, \"concrete.xls\"))\n",
+    "occupancy = pd.read_csv(os.path.join('data','occupancy','datatraining.txt'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Rename the columns of the datasets for ease of use. \n",
+    "credit.columns = [\n",
+    "    'id', 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',\n",
+    "    'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',\n",
+    "    'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',\n",
+    "    'jul_pay', 'aug_pay', 'sep_pay', 'default'\n",
+    "]\n",
+    "\n",
+    "concrete.columns = [\n",
+    "    'cement', 'slag', 'ash', 'water', 'splast',\n",
+    "    'coarse', 'fine', 'age', 'strength'\n",
+    "]\n",
+    "\n",
+    "occupancy.columns = [\n",
+    "    'date', 'temp', 'humid', 'light', 'co2', 'hratio', 'occupied'\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
+       " ('model', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
+       "       intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
+       "       multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
+       "       verbose=0))]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.preprocessing import StandardScaler \n",
+    "\n",
+    "model = Pipeline([\n",
+    "    ('scale', StandardScaler()), \n",
+    "    ('model', LinearSVC())\n",
+    "])\n",
+    "\n",
+    "model.steps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluation Visualization Prototype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "\n",
+    "\n",
+    "class VisualPipeline(Pipeline):\n",
+    "    \n",
+    "    def draw(self):\n",
+    "        \"\"\"\n",
+    "        Calls the draw method on every visual transformer/estimator  \n",
+    "        \"\"\"\n",
+    "        for name, estimator in self.steps:\n",
+    "            try:\n",
+    "                estimator.draw()\n",
+    "            except AttributeError:\n",
+    "                continue \n",
+    "\n",
+    "                \n",
+    "class ClassifierEvaluation(object):\n",
+    "    \n",
+    "    def draw(self):\n",
+    "        yb.crplot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/rank2d.ipynb b/examples/rank2d.ipynb
new file mode 100644
index 000000000..3d12f322c
--- /dev/null
+++ b/examples/rank2d.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "%matplotlib inline "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sliders Example \n",
+    "\n",
+    "This is an example of interactive iPython workbook that uses widgets to meaningfully interact with visualization. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Imports \n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from collections import OrderedDict\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import Imputer \n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error as mse\n",
+    "from ipywidgets import interact, interactive, fixed\n",
+    "\n",
+    "import ipywidgets as widgets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Data Loading \n",
+    "columns = OrderedDict([\n",
+    "    (\"DAY\", \"the day of data collection\"),\n",
+    "    (\"Q-E\", \"input flow to plant\"),\n",
+    "    (\"ZN-E\", \"input Zinc to plant\"),\n",
+    "    (\"PH-E\", \"input pH to plant\"),\n",
+    "    (\"DBO-E\", \"input Biological demand of oxygen to plant\"),\n",
+    "    (\"DQO-E\", \"input chemical demand of oxygen to plant\"),\n",
+    "    (\"SS-E\", \"input suspended solids to plant\"),\n",
+    "    (\"SSV-E\", \"input volatile supended solids to plant\"),\n",
+    "    (\"SED-E\", \"input sediments to plant\"),\n",
+    "    (\"COND-E\", \"input conductivity to plant\"),\n",
+    "    (\"PH-P\", \"input pH to primary settler\"),\n",
+    "    (\"DBO-P\", \"input Biological demand of oxygen to primary settler\"),\n",
+    "    (\"SS-P\", \"input suspended solids to primary settler\"),\n",
+    "    (\"SSV-P\", \"input volatile supended solids to primary settler\"),\n",
+    "    (\"SED-P\", \"input sediments to primary settler\"),\n",
+    "    (\"COND-P\", \"input conductivity to primary settler\"),\n",
+    "    (\"PH-D\", \"input pH to secondary settler\"),\n",
+    "    (\"DBO-D\", \"input Biological demand of oxygen to secondary settler\"),\n",
+    "    (\"DQO-D\", \"input chemical demand of oxygen to secondary settler\"),\n",
+    "    (\"SS-D\", \"input suspended solids to secondary settler\"),\n",
+    "    (\"SSV-D\", \"input volatile supended solids to secondary settler\"),\n",
+    "    (\"SED-D\", \"input sediments to secondary settler\"),\n",
+    "    (\"COND-S\", \"input conductivity to secondary settler\"),\n",
+    "    (\"PH-S\", \"output pH\"),\n",
+    "    (\"DBO-S\", \"output Biological demand of oxygen\"),\n",
+    "    (\"DQO-S\", \"output chemical demand of oxygen\"),\n",
+    "    (\"SS-S\", \"output suspended solids\"),\n",
+    "    (\"SSV-S\", \"output volatile supended solids\"),\n",
+    "    (\"SED-S\", \"output sediments\"),\n",
+    "    (\"COND-\", \"output conductivity\"),\n",
+    "    (\"RD-DB-P\", \"performance input Biological demand of oxygen in primary settler\"),\n",
+    "    (\"RD-SSP\", \"performance input suspended solids to primary settler\"),\n",
+    "    (\"RD-SE-P\", \"performance input sediments to primary settler\"),\n",
+    "    (\"RD-DB-S\", \"performance input Biological demand of oxygen to secondary settler\"),\n",
+    "    (\"RD-DQ-S\", \"performance input chemical demand of oxygen to secondary settler\"),\n",
+    "    (\"RD-DB-G\", \"global performance input Biological demand of oxygen\"),\n",
+    "    (\"RD-DQ-G\", \"global performance input chemical demand of oxygen\"),\n",
+    "    (\"RD-SSG\", \"global performance input suspended solids\"),\n",
+    "    (\"RD-SED-G\", \"global performance input sediments\"),\n",
+    "])\n",
+    "\n",
+    "data = pd.read_csv(\"data/water-treatment.data\", names=columns.keys())\n",
+    "data = data.replace('?', np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Capture only the numeric columns in the data set. \n",
+    "numeric_columns = columns.keys()\n",
+    "numeric_columns.remove(\"DAY\")\n",
+    "data = data[numeric_columns].apply(pd.to_numeric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2D Rank Features "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def apply_column_pairs(func):\n",
+    "    \"\"\"\n",
+    "    Applies a function to a pair of columns and returns a new \n",
+    "    dataframe that contains the result of the function as a matrix\n",
+    "    of each pair of columns. \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    def inner(df):\n",
+    "        cols = pd.DataFrame([\n",
+    "            [\n",
+    "                func(df[acol], df[bcol]) for bcol in df.columns\n",
+    "            ] for acol in df.columns\n",
+    "        ])\n",
+    "\n",
+    "        cols.columns = df.columns\n",
+    "        cols.index = df.columns \n",
+    "        return cols\n",
+    "\n",
+    "    return inner \n",
+    "\n",
+    "\n",
+    "@apply_column_pairs\n",
+    "def least_square_error(cola, colb):\n",
+    "    \"\"\"\n",
+    "    Computes the Root Mean Squared Error of a linear regression \n",
+    "    between two columns of data. \n",
+    "    \"\"\"\n",
+    "    x = cola.fillna(np.nanmean(cola))\n",
+    "    y = colb.fillna(np.nanmean(colb))\n",
+    "    \n",
+    "    m, b = np.polyfit(x, y, 1)\n",
+    "    yh  = (x * m) + b \n",
+    "    return ((y-yh) ** 2).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAOCRoJzztGfPHsXFxRVbZhiGDh486NWkAABA8AvQ2sm8eHr//fd9lQcAAKhg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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b017690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labeled_metrics = {\n",
+    "    'Pearson': 'pearson', \n",
+    "    'Kendall Tao': 'kendall', \n",
+    "    'Spearman': 'spearman', \n",
+    "    'Pairwise Covariance': 'covariance',\n",
+    "    'Least Squares Error': 'lse', \n",
+    "}\n",
+    "\n",
+    "@interact(metric=labeled_metrics, data=fixed(data))\n",
+    "def rank2d(data, metric='pearson'):\n",
+    "    \"\"\"\n",
+    "    Creates a visualization of pairwise ranking by column in the data. \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # The different rank by 2d metrics. \n",
+    "    metrics = {\n",
+    "        \"pearson\": lambda df: df.corr('pearson'), \n",
+    "        \"kendall\": lambda df: df.corr('kendall'), \n",
+    "        \"spearman\": lambda df: df.corr('spearman'), \n",
+    "        \"covariance\": lambda df: df.cov(), \n",
+    "        \"lse\": least_square_error,\n",
+    "    }\n",
+    "    \n",
+    "    # Quick check to make sure a valid metric is passed in. \n",
+    "    if metric not in metrics:\n",
+    "        raise ValueError(\n",
+    "            \"'{}' not a valid metric, specify one of {}\".format(\n",
+    "                metric, \", \".join(metrics.keys())\n",
+    "            )\n",
+    "        )\n",
+    "    \n",
+    "    \n",
+    "    # Compute the correlation matrix\n",
+    "    corr = metrics[metric](data)\n",
+    "\n",
+    "    # Generate a mask for the upper triangle\n",
+    "    mask = np.zeros_like(corr, dtype=np.bool)\n",
+    "    mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "    # Set up the matplotlib figure\n",
+    "    f, ax = plt.subplots(figsize=(11, 9))\n",
+    "    ax.set_title(\"{} metric across {} features\".format(metric.title(), len(data.columns)))\n",
+    "    \n",
+    "    # Draw the heatmap with the mask and correct aspect ratio\n",
+    "    sns.heatmap(corr, mask=mask, vmax=.3,\n",
+    "                square=True, xticklabels=5, yticklabels=5,\n",
+    "                linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax)"
+   ]
+  }
+ ],
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