diff --git a/src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs b/src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs
index bbf4ef6531..831d86f8bb 100644
--- a/src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs
+++ b/src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs
@@ -9,6 +9,7 @@
 using System.Threading;
 using System.Threading.Tasks;
 using Microsoft.Extensions.DependencyInjection;
+using Microsoft.ML.AutoML.Tuner;
 using Microsoft.ML.Data;
 using Microsoft.ML.Runtime;
 using Microsoft.ML.Trainers;
@@ -35,6 +36,11 @@ public sealed class BinaryExperimentSettings : ExperimentSettings
         /// <value>The default value is a collection auto-populated with all possible trainers (all values of <see cref="BinaryClassificationTrainer" />).</value>
         public ICollection<BinaryClassificationTrainer> Trainers { get; }
 
+        /// <summary>
+        /// Set if use <see cref="AutoZeroTuner"/> for hyper-parameter optimization, default to false.
+        /// </summary>
+        public bool UseAutoZeroTuner { get; set; }
+
         /// <summary>
         /// Initializes a new instance of <see cref="BinaryExperimentSettings"/>.
         /// </summary>
@@ -42,6 +48,7 @@ public BinaryExperimentSettings()
         {
             OptimizingMetric = BinaryClassificationMetric.Accuracy;
             Trainers = Enum.GetValues(typeof(BinaryClassificationTrainer)).OfType<BinaryClassificationTrainer>().ToList();
+            UseAutoZeroTuner = false;
         }
     }
 
@@ -133,7 +140,7 @@ public enum BinaryClassificationTrainer
     /// </example>
     public sealed class BinaryClassificationExperiment : ExperimentBase<BinaryClassificationMetrics, BinaryExperimentSettings>
     {
-        private readonly AutoMLExperiment _experiment;
+        private AutoMLExperiment _experiment;
         private const string Features = "__Features__";
         private SweepablePipeline _pipeline;
 
@@ -151,13 +158,13 @@ internal BinaryClassificationExperiment(MLContext context, BinaryExperimentSetti
                 _experiment.SetMaximumMemoryUsageInMegaByte(d);
             }
             _experiment.SetMaxModelToExplore(settings.MaxModels);
+            _experiment.SetTrainingTimeInSeconds(settings.MaxExperimentTimeInSeconds);
         }
 
         public override ExperimentResult<BinaryClassificationMetrics> Execute(IDataView trainData, ColumnInformation columnInformation, IEstimator<ITransformer> preFeaturizer = null, IProgress<RunDetail<BinaryClassificationMetrics>> progressHandler = null)
         {
             var label = columnInformation.LabelColumnName;
             _experiment.SetBinaryClassificationMetric(Settings.OptimizingMetric, label);
-            _experiment.SetTrainingTimeInSeconds(Settings.MaxExperimentTimeInSeconds);
 
             // Cross val threshold for # of dataset rows --
             // If dataset has < threshold # of rows, use cross val.
@@ -194,7 +201,7 @@ public override ExperimentResult<BinaryClassificationMetrics> Execute(IDataView
 
                 return monitor;
             });
-            _experiment.SetTrialRunner<BinaryClassificationRunner>();
+            _experiment = PostConfigureAutoMLExperiment(_experiment);
             _experiment.Run();
 
             var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToRunDetail(Context, e, _pipeline));
@@ -208,7 +215,6 @@ public override ExperimentResult<BinaryClassificationMetrics> Execute(IDataView
         {
             var label = columnInformation.LabelColumnName;
             _experiment.SetBinaryClassificationMetric(Settings.OptimizingMetric, label);
-            _experiment.SetTrainingTimeInSeconds(Settings.MaxExperimentTimeInSeconds);
             _experiment.SetDataset(trainData, validationData);
             _pipeline = CreateBinaryClassificationPipeline(trainData, columnInformation, preFeaturizer);
             _experiment.SetPipeline(_pipeline);
@@ -228,7 +234,7 @@ public override ExperimentResult<BinaryClassificationMetrics> Execute(IDataView
 
                 return monitor;
             });
-            _experiment.SetTrialRunner<BinaryClassificationRunner>();
+            _experiment = PostConfigureAutoMLExperiment(_experiment);
             _experiment.Run();
 
             var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToRunDetail(Context, e, _pipeline));
@@ -263,7 +269,6 @@ public override CrossValidationExperimentResult<BinaryClassificationMetrics> Exe
         {
             var label = columnInformation.LabelColumnName;
             _experiment.SetBinaryClassificationMetric(Settings.OptimizingMetric, label);
-            _experiment.SetTrainingTimeInSeconds(Settings.MaxExperimentTimeInSeconds);
             _experiment.SetDataset(trainData, (int)numberOfCVFolds);
             _pipeline = CreateBinaryClassificationPipeline(trainData, columnInformation, preFeaturizer);
             _experiment.SetPipeline(_pipeline);
@@ -284,7 +289,7 @@ public override CrossValidationExperimentResult<BinaryClassificationMetrics> Exe
                 return monitor;
             });
 
-            _experiment.SetTrialRunner<BinaryClassificationRunner>();
+            _experiment = PostConfigureAutoMLExperiment(_experiment);
             _experiment.Run();
 
             var runDetails = monitor.RunDetails.Select(e => BestResultUtil.ToCrossValidationRunDetail(Context, e, _pipeline));
@@ -335,6 +340,17 @@ private SweepablePipeline CreateBinaryClassificationPipeline(IDataView trainData
                            .Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdcaLogisticRegression: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsLogisticRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
             }
         }
+
+        private AutoMLExperiment PostConfigureAutoMLExperiment(AutoMLExperiment experiment)
+        {
+            experiment.SetTrialRunner<BinaryClassificationRunner>();
+            if (Settings.UseAutoZeroTuner)
+            {
+                experiment.SetTuner<AutoZeroTuner>();
+            }
+
+            return experiment;
+        }
     }
 
     internal class BinaryClassificationRunner : ITrialRunner
diff --git a/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj b/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj
index 562ee0410e..2a4279c27b 100644
--- a/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj
+++ b/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj
@@ -68,6 +68,12 @@
     <AdditionalFiles Include="CodeGen\*-estimators.json" />
   </ItemGroup>
 
+  <ItemGroup>
+    <EmbeddedResource Include="Tuner\Portfolios.json">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </EmbeddedResource>
+  </ItemGroup>
+
   <Target DependsOnTargets="ResolveReferences" Name="CopyProjectReferencesToPackage">
     <ItemGroup>
       <!--Include DLLs of Project References-->
diff --git a/src/Microsoft.ML.AutoML/Tuner/AutoZeroTuner.cs b/src/Microsoft.ML.AutoML/Tuner/AutoZeroTuner.cs
new file mode 100644
index 0000000000..1a2161c99c
--- /dev/null
+++ b/src/Microsoft.ML.AutoML/Tuner/AutoZeroTuner.cs
@@ -0,0 +1,142 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using System;
+using System.Collections.Generic;
+using System.Globalization;
+using System.IO;
+using System.Linq;
+using System.Reflection;
+using System.Text;
+using System.Text.Json;
+using Microsoft.ML.AutoML.CodeGen;
+using Microsoft.ML.SearchSpace;
+
+namespace Microsoft.ML.AutoML.Tuner
+{
+    internal class AutoZeroTuner : ITuner
+    {
+        private readonly List<Config> _configs = new List<Config>();
+        private readonly IEnumerator<Config> _configsEnumerator;
+        private readonly Dictionary<string, string> _pipelineStrings;
+        private readonly SweepablePipeline _sweepablePipeline;
+        private readonly Dictionary<int, Config> _configLookBook = new Dictionary<int, Config>();
+        private readonly string _metricName;
+
+        public AutoZeroTuner(SweepablePipeline pipeline, AggregateTrainingStopManager aggregateTrainingStopManager, IEvaluateMetricManager evaluateMetricManager, AutoMLExperiment.AutoMLExperimentSettings settings)
+        {
+            _configs = LoadConfigsFromJson();
+            _sweepablePipeline = pipeline;
+            _pipelineStrings = _sweepablePipeline.Schema.ToTerms().Select(t => new
+            {
+                schema = t.ToString(),
+                pipelineString = string.Join("=>", t.ValueEntities().Select(e => _sweepablePipeline.Estimators[e.ToString()].EstimatorType)),
+            }).ToDictionary(kv => kv.schema, kv => kv.pipelineString);
+
+            // todo
+            // filter configs on trainers
+            var trainerEstimators = _sweepablePipeline.Estimators.Where(e => e.Value.EstimatorType.IsTrainer()).Select(e => e.Value.EstimatorType.ToString()).ToList();
+            _configs = evaluateMetricManager switch
+            {
+                BinaryMetricManager => _configs.Where(c => c.Task == "binary-classification" && trainerEstimators.Contains(c.Trainer)).ToList(),
+                MultiClassMetricManager => _configs.Where(c => c.Task == "multi-classification" && trainerEstimators.Contains(c.Trainer)).ToList(),
+                RegressionMetricManager => _configs.Where(c => c.Task == "regression" && trainerEstimators.Contains(c.Trainer)).ToList(),
+                _ => throw new Exception(),
+            };
+            _metricName = evaluateMetricManager switch
+            {
+                BinaryMetricManager bm => bm.Metric.ToString(),
+                MultiClassMetricManager mm => mm.Metric.ToString(),
+                RegressionMetricManager rm => rm.Metric.ToString(),
+                _ => throw new Exception(),
+            };
+
+            if (_configs.Count == 0)
+            {
+                throw new ArgumentException($"Fail to find available configs for given trainers: {string.Join(",", trainerEstimators)}");
+            }
+
+            _configsEnumerator = _configs.GetEnumerator();
+            aggregateTrainingStopManager.AddTrainingStopManager(new MaxModelStopManager(_configs.Count, null));
+        }
+
+        private List<Config> LoadConfigsFromJson()
+        {
+            var assembly = Assembly.GetExecutingAssembly();
+            var resourceName = "Microsoft.ML.AutoML.Tuner.Portfolios.json";
+
+            using (Stream stream = assembly.GetManifestResourceStream(resourceName))
+            using (StreamReader reader = new StreamReader(stream))
+            {
+                var json = reader.ReadToEnd();
+                var res = JsonSerializer.Deserialize<List<Config>>(json);
+
+                return res;
+            }
+        }
+
+        public Parameter Propose(TrialSettings settings)
+        {
+            if (_configsEnumerator.MoveNext())
+            {
+                var config = _configsEnumerator.Current;
+                IEnumerable<KeyValuePair<string, string>> pipelineSchemas = default;
+                if (_pipelineStrings.Any(kv => kv.Value.Contains("OneHotHashEncoding") || kv.Value.Contains("OneHotEncoding")))
+                {
+                    pipelineSchemas = _pipelineStrings.Where(kv => kv.Value.Contains(config.CatalogTransformer));
+                }
+                else
+                {
+                    pipelineSchemas = _pipelineStrings;
+                }
+
+                pipelineSchemas = pipelineSchemas.Where(kv => kv.Value.Contains(config.Trainer));
+                var pipelineSchema = pipelineSchemas.First().Key;
+                var pipeline = _sweepablePipeline.BuildSweepableEstimatorPipeline(pipelineSchema);
+                var parameter = pipeline.SearchSpace.SampleFromFeatureSpace(pipeline.SearchSpace.Default);
+                var trainerEstimatorName = pipeline.Estimators.Where(kv => kv.Value.EstimatorType.IsTrainer()).First().Key;
+                var label = parameter[trainerEstimatorName]["LabelColumnName"].AsType<string>();
+                var feature = parameter[trainerEstimatorName]["FeatureColumnName"].AsType<string>();
+                parameter[trainerEstimatorName] = config.TrainerParameter;
+                parameter[trainerEstimatorName]["LabelColumnName"] = Parameter.FromString(label);
+                parameter[trainerEstimatorName]["FeatureColumnName"] = Parameter.FromString(feature);
+                settings.Parameter[AutoMLExperiment.PipelineSearchspaceName] = parameter;
+                _configLookBook[settings.TrialId] = config;
+                return settings.Parameter;
+            }
+
+            throw new OperationCanceledException();
+        }
+
+        public void Update(TrialResult result)
+        {
+        }
+
+        class Config
+        {
+            /// <summary>
+            /// one of OneHot, HashEncoding
+            /// </summary>
+            public string CatalogTransformer { get; set; }
+
+            /// <summary>
+            /// One of Lgbm, Sdca, FastTree,,,
+            /// </summary>
+            public string Trainer { get; set; }
+
+            public Parameter TrainerParameter { get; set; }
+
+            public string Task { get; set; }
+        }
+
+        class Rows
+        {
+            public string CustomDimensionsBestPipeline { get; set; }
+
+            public string CustomDimensionsOptionsTask { get; set; }
+
+            public Parameter CustomDimensionsParameter { get; set; }
+        }
+    }
+}
diff --git a/src/Microsoft.ML.AutoML/Tuner/Portfolios.json b/src/Microsoft.ML.AutoML/Tuner/Portfolios.json
new file mode 100644
index 0000000000..eb950503e9
--- /dev/null
+++ b/src/Microsoft.ML.AutoML/Tuner/Portfolios.json
@@ -0,0 +1,1813 @@
+[
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 87,
+      "MinimumExampleCountPerLeaf": 127,
+      "NumberOfTrees": 3650,
+      "MaximumBinCountPerFeature": 42,
+      "FeatureFraction": 0.80060378946837,
+      "LearningRate": 0.0035449318926440276
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 23,
+      "MinimumExampleCountPerLeaf": 71,
+      "NumberOfTrees": 207,
+      "MaximumBinCountPerFeature": 170,
+      "FeatureFraction": 0.99999999,
+      "LearningRate": 0.26904495597775546
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 2866,
+      "MinimumExampleCountPerLeaf": 75,
+      "NumberOfTrees": 20,
+      "MaximumBinCountPerFeature": 133,
+      "FeatureFraction": 0.99999999,
+      "LearningRate": 0.0034217757770038746
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotEncoding",
+    "Trainer": "LightGbmBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 24,
+      "MinimumExampleCountPerLeaf": 54,
+      "LearningRate": 0.8552033613780062,
+      "NumberOfTrees": 45,
+      "SubsampleFraction": 8.039913011326977E-05,
+      "MaximumBinCountPerFeature": 1023,
+      "FeatureFraction": 0.99999999,
+      "L1Regularization": 0.00020810911669089135,
+      "L2Regularization": 0.007597609681395737
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastForestBinary",
+    "TrainerParameter": {
+      "NumberOfTrees": 26,
+      "NumberOfLeaves": 3463,
+      "FeatureFraction": 1
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotEncoding",
+    "Trainer": "FastTreeOva",
+    "TrainerParameter": {
+      "NumberOfLeaves": 476,
+      "MinimumExampleCountPerLeaf": 33,
+      "NumberOfTrees": 37,
+      "MaximumBinCountPerFeature": 24,
+      "FeatureFraction": 0.7841884722574419,
+      "LearningRate": 0.12930004437797252
+
+    },
+    "Task": "multi-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 27926,
+      "MinimumExampleCountPerLeaf": 127,
+      "NumberOfTrees": 11,
+      "MaximumBinCountPerFeature": 54,
+      "FeatureFraction": 0.99999999,
+      "LearningRate": 0.16078907661831227
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "SdcaMaximumEntropyMulti",
+    "TrainerParameter": {
+      "L1Regularization": 0.03125,
+      "L2Regularization": 0.03125
+
+    },
+    "Task": "multi-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "LbfgsLogisticRegressionBinary",
+    "TrainerParameter": {
+      "L1Regularization": 1.544239,
+      "L2Regularization": 0.03125
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotEncoding",
+    "Trainer": "FastTreeBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 18564,
+      "MinimumExampleCountPerLeaf": 4,
+      "NumberOfTrees": 739,
+      "MaximumBinCountPerFeature": 35,
+      "FeatureFraction": 0.7191111861806592,
+      "LearningRate": 0.027880192420138245
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "LightGbmBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 4095,
+      "MinimumExampleCountPerLeaf": 28,
+      "LearningRate": 0.9999997766729865,
+      "NumberOfTrees": 4095,
+      "SubsampleFraction": 2E-10,
+      "MaximumBinCountPerFeature": 1023,
+      "FeatureFraction": 0.99999999,
+      "L1Regularization": 2E-10,
+      "L2Regularization": 0.1846310442449399
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 131,
+      "MinimumExampleCountPerLeaf": 21,
+      "NumberOfTrees": 125,
+      "MaximumBinCountPerFeature": 116,
+      "FeatureFraction": 0.7921180259906164,
+      "LearningRate": 0.9999997766729865
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastForestOva",
+    "TrainerParameter": {
+      "NumberOfTrees": 228,
+      "NumberOfLeaves": 91,
+      "FeatureFraction": 0.8420064
+
+    },
+    "Task": "multi-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeRegression",
+    "TrainerParameter": {
+      "NumberOfLeaves": 4,
+      "MinimumExampleCountPerLeaf": 19,
+      "NumberOfTrees": 4,
+      "MaximumBinCountPerFeature": 189,
+      "FeatureFraction": 0.9459816795478228,
+      "LearningRate": 0.10898711358107055
+
+    },
+    "Task": "regression"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "LightGbmRegression",
+    "TrainerParameter": {
+      "NumberOfLeaves": 16,
+      "MinimumExampleCountPerLeaf": 70,
+      "LearningRate": 0.2138828822356055,
+      "NumberOfTrees": 25,
+      "SubsampleFraction": 0.05380197471709659,
+      "MaximumBinCountPerFeature": 426,
+      "FeatureFraction": 0.6981585251308082,
+      "L1Regularization": 1.8832743738626092E-08,
+      "L2Regularization": 1.1634842271570635E-08
+
+    },
+    "Task": "regression"
+  },
+  {
+    "CatalogTransformer": "OneHotEncoding",
+    "Trainer": "LightGbmBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 7,
+      "MinimumExampleCountPerLeaf": 20,
+      "LearningRate": 0.1607069068239919,
+      "NumberOfTrees": 16,
+      "SubsampleFraction": 0.04856069948639365,
+      "MaximumBinCountPerFeature": 296,
+      "FeatureFraction": 0.9470492408617684,
+      "L1Regularization": 6.929644224890629E-08,
+      "L2Regularization": 0.0011264388186604647
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastForestRegression",
+    "TrainerParameter": {
+      "NumberOfTrees": 32767,
+      "NumberOfLeaves": 4,
+      "FeatureFraction": 1
+
+    },
+    "Task": "regression"
+  },
+  {
+    "CatalogTransformer": "OneHotEncoding",
+    "Trainer": "FastTreeRegression",
+    "TrainerParameter": {
+      "NumberOfLeaves": 5,
+      "MinimumExampleCountPerLeaf": 8,
+      "NumberOfTrees": 826,
+      "MaximumBinCountPerFeature": 312,
+      "FeatureFraction": 0.99999999,
+      "LearningRate": 0.24415311049241878
+
+    },
+    "Task": "regression"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastForestRegression",
+    "TrainerParameter": {
+      "NumberOfTrees": 4,
+      "NumberOfLeaves": 4,
+      "FeatureFraction": 0.39762402
+
+    },
+    "Task": "regression"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeOva",
+    "TrainerParameter": {
+      "NumberOfLeaves": 30,
+      "MinimumExampleCountPerLeaf": 2,
+      "NumberOfTrees": 7,
+      "MaximumBinCountPerFeature": 1023,
+      "FeatureFraction": 0.43132548851524677,
+      "LearningRate": 1.7715889795284222E-05
+
+    },
+    "Task": "multi-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotEncoding",
+    "Trainer": "LightGbmMulti",
+    "TrainerParameter": {
+      "NumberOfLeaves": 4,
+      "MinimumExampleCountPerLeaf": 24,
+      "LearningRate": 0.9999997766729865,
+      "NumberOfTrees": 5,
+      "SubsampleFraction": 0.8888612589023396,
+      "MaximumBinCountPerFeature": 174,
+      "FeatureFraction": 0.99999999,
+      "L1Regularization": 2.853856087345465E-10,
+      "L2Regularization": 0.9999997766729865
+
+    },
+    "Task": "multi-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "LightGbmRegression",
+    "TrainerParameter": {
+      "NumberOfLeaves": 36,
+      "MinimumExampleCountPerLeaf": 20,
+      "LearningRate": 0.6488080623332225,
+      "NumberOfTrees": 5,
+      "SubsampleFraction": 0.05377902547670367,
+      "MaximumBinCountPerFeature": 988,
+      "FeatureFraction": 0.8318215522085479,
+      "L1Regularization": 1.0693136058923942E-07,
+      "L2Regularization": 0.9999997766729865
+
+    },
+    "Task": "regression"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "LightGbmRegression",
+    "TrainerParameter": {
+      "NumberOfLeaves": 340,
+      "MinimumExampleCountPerLeaf": 67,
+      "LearningRate": 0.01760375575034171,
+      "NumberOfTrees": 1408,
+      "SubsampleFraction": 0.9999997766729865,
+      "MaximumBinCountPerFeature": 1023,
+      "FeatureFraction": 0.99999999,
+      "L1Regularization": 5.602593285766215E-10,
+      "L2Regularization": 0.0003496680724914571
+
+    },
+    "Task": "regression"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 29,
+      "MinimumExampleCountPerLeaf": 2,
+      "NumberOfTrees": 18,
+      "MaximumBinCountPerFeature": 281,
+      "FeatureFraction": 0.9844369303190436,
+      "LearningRate": 0.2689422276981913
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "LightGbmBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 4,
+      "MinimumExampleCountPerLeaf": 20,
+      "LearningRate": 1,
+      "NumberOfTrees": 4,
+      "SubsampleFraction": 1,
+      "MaximumBinCountPerFeature": 254,
+      "FeatureFraction": 1,
+      "L1Regularization": 2E-10,
+      "L2Regularization": 1
+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "FastTreeBinary",
+    "TrainerParameter": {
+      "NumberOfLeaves": 43,
+      "MinimumExampleCountPerLeaf": 22,
+      "NumberOfTrees": 35,
+      "MaximumBinCountPerFeature": 239,
+      "FeatureFraction": 0.99999999,
+      "LearningRate": 0.027071590576339927
+
+    },
+    "Task": "binary-classification"
+  },
+  {
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+
+    },
+    "Task": "binary-classification"
+  },
+  {
+    "CatalogTransformer": "OneHotHashEncoding",
+    "Trainer": "LightGbmMulti",
+    "TrainerParameter": {
+      "NumberOfLeaves": 75,
+      "MinimumExampleCountPerLeaf": 277,
+      "LearningRate": 0.36344733837824383,
+      "NumberOfTrees": 75,
+      "SubsampleFraction": 0.9999997766729865,
+      "MaximumBinCountPerFeature": 178,
+      "FeatureFraction": 0.8402457426124342,
+      "L1Regularization": 5.328274305679465E-08,
+      "L2Regularization": 0.9999997766729865
+
+    },
+    "Task": "multi-classification"
+  }
+]
diff --git a/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs b/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs
index 8899312dbb..c31cb32506 100644
--- a/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs
+++ b/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs
@@ -64,6 +64,33 @@ public void AutoFit_UCI_Adult_Test()
             Assert.NotNull(result.BestRun.TrainerName);
         }
 
+        [Fact]
+        public void AutoFit_UCI_Adult_AutoZero_Test()
+        {
+            var context = new MLContext(1);
+            var dataPath = DatasetUtil.GetUciAdultDataset();
+            var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel);
+            var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
+            var trainData = textLoader.Load(dataPath);
+            var settings = new BinaryExperimentSettings
+            {
+                MaxModels = 1,
+                UseAutoZeroTuner = true,
+            };
+
+            settings.Trainers.Remove(BinaryClassificationTrainer.LightGbm);
+            settings.Trainers.Remove(BinaryClassificationTrainer.SdcaLogisticRegression);
+            settings.Trainers.Remove(BinaryClassificationTrainer.LbfgsLogisticRegression);
+
+            var result = context.Auto()
+                .CreateBinaryClassificationExperiment(settings)
+                .Execute(trainData, new ColumnInformation() { LabelColumnName = DatasetUtil.UciAdultLabel });
+            result.BestRun.ValidationMetrics.Accuracy.Should().BeGreaterOrEqualTo(0.7);
+            Assert.NotNull(result.BestRun.Estimator);
+            Assert.NotNull(result.BestRun.Model);
+            Assert.NotNull(result.BestRun.TrainerName);
+        }
+
         [Fact]
         public void AutoFit_UCI_Adult_Train_Test_Split_Test()
         {