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+## Overview
+Experiment API is a set of API build to work with `SweepablePipeline`. Its aim is to make the interaction among `Tuner`, `Search Space` and `Sweepable Pipeline` transparent to external customers.
+
+## Problem
+Suppose that you have `sweepable pipeline`, `tuner` and `searchSpace`, and you want to optimize the  `sweepable pipeline` over given `searchSpace` using that `tuner`. In order to make that happen, without `Experiment API`, you would need to mannually interact with tuner and searchSpace, building mlnet training pipeline, train and evaluate model, and tracing the best model/parameter. Thus process expose too many details and would not be considered easy-to-use for users to start with.
+
+```csharp
+// this training process just expose too many details.
+var pipeline, tuner;
+
+// search space comes with pipeline.
+var searchSpace = pipeline.SearchSpace;
+
+tuner.SetSearchSpace(searchSPace);
+foreach(var parameter in tuner.Proposal())
+{
+    // construct ML.Net pipeline from parameter
+    var mlPipeline = pipeline.BuildTrainingPipeline(parameter);
+    
+    // evaluate
+    var model = mlPipeline.Fit(trainData);
+    var score = model.Evaluate(testData);
+
+    // code to save best model
+    if(score > bestScore)
+    {
+        bestScore = score;
+        bestModel = model;
+    }
+
+    // update tuner with score
+    tuner.Update(parameter, score);
+}
+```
+
+## Solution: Experiment API
+
+With Experiment api, we can make `pipeline`, `tuner` and `searchspace` transparent to users so they don't have to know how those parts work with each other. What replaces them is a higher-level concept: `Experiment`. `Experiment` will take the input from users, like training time, searching strategy, train/test/validation dataset, model saving strategy... After all input is given, experiment will take care the rest of training process.
+
+```csharp
+// Experiment api.
+var pipeline, tuner;
+
+var experiment = pipeline.CreateExperiment(trainTime = 100, trainDataset = "train.csv", split = "cv", folds = 10, metric = "AUC", tuner = tuner, monitor = monitor);
+
+// or fluent-style api
+experiment = pipeline.CreateExperiment();
+
+experiment.SetTrainingTime(100)
+    .SetDataset(trainDataset = "train.csv", split = "cv", fold = 10)
+    .SetEvaluationMetric(metric = "AUC") // or a lambda function which return a score
+    .SetTuner(tuner)
+    .SetMonitor(monitor);
+
+experiment.Run()
+monitor.ShowProgress();
+
+// trial 1: score ... parameter ...
+// trial 2: score ... parameter ...
+```
+
+### default classifiers and regressors
+
+It will be useful if we provide an API that returns a combination of all available trainers with default search spaces.
+
+```csharp
+var featurizePipeline
+
+// regression
+var pipeline = featurizePipeline.Append(context.AutoML().Regressors(labelColumn = "label", useLgbm = true, useFastTree = false, ...));
+
+// binary classification
+var pipeline = featurizePipeline.Append(context.AutoML().BinaryClassification(labelColumn = "label", useLgbm = true, useFastTree = false, ...));
+
+// multi-class classification
+var pipeline = featurizePipeline.Append(context.AutoML().MultiClassification(labelColumn = "label", useLgbm = true, useFastTree = false, ...));
+
+// univariant forecasting
+var pipeline = featurizePipeline.Append(context.AutoML().Forcasting(labelColumn = "label", horizon ...));
+
+// create Experiment
+var exp = pipeline.CreateExperiment();
+...
+
+```
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