diff --git a/src/Microsoft.ML.Core/Utilities/Random.cs b/src/Microsoft.ML.Core/Utilities/Random.cs
index 88dd45209f..7b1b14c974 100644
--- a/src/Microsoft.ML.Core/Utilities/Random.cs
+++ b/src/Microsoft.ML.Core/Utilities/Random.cs
@@ -42,16 +42,6 @@ public interface IRandom
 
     public static class RandomUtils
     {
-        public static Single NextFloat(this IRandom rand)
-        {
-            return rand.NextSingle();
-        }
-
-        public static Single NextFloat(this Random rand)
-        {
-            return rand.NextDouble().ToFloat();
-        }
-
         public static TauswortheHybrid Create()
         {
             // Seed from a system random.
diff --git a/src/Microsoft.ML.Core/Utilities/Stats.cs b/src/Microsoft.ML.Core/Utilities/Stats.cs
index 6b048c4de3..33992950db 100644
--- a/src/Microsoft.ML.Core/Utilities/Stats.cs
+++ b/src/Microsoft.ML.Core/Utilities/Stats.cs
@@ -202,7 +202,7 @@ public static int SampleFromPoisson(IRandom rand, double lambda)
         // http://en.wikipedia.org/wiki/Laplace_distribution
         public static Float SampleFromLaplacian(IRandom rand, Float mean, Float scale)
         {
-            Float u = rand.NextFloat();
+            Float u = rand.NextSingle();
             u = u - 0.5f;
             Float ret = mean;
             if (u >= 0)
@@ -221,7 +221,7 @@ public static Float SampleFromLaplacian(IRandom rand, Float mean, Float scale)
         /// <returns></returns>
         public static Float SampleFromCauchy(IRandom rand)
         {
-            return (Float)Math.Tan(Math.PI * (rand.NextFloat() - 0.5));
+            return (Float)Math.Tan(Math.PI * (rand.NextSingle() - 0.5));
         }
 
         /// <summary>
diff --git a/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs b/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs
index 1b1fc0328d..f80589bdab 100644
--- a/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs
+++ b/src/Microsoft.ML.Data/Transforms/GenerateNumberTransform.cs
@@ -426,7 +426,7 @@ private void EnsureValue(ref long lastCounter, ref Float value, TauswortheHybrid
                 Ch.Assert(lastCounter <= Input.Position);
                 while (lastCounter < Input.Position)
                 {
-                    value = rng.NextFloat();
+                    value = rng.NextSingle();
                     lastCounter++;
                 }
             }
diff --git a/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs b/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs
index 87fd220cf8..b833278890 100644
--- a/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs
+++ b/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs
@@ -328,7 +328,7 @@ public static void Initialize(
                                 cumulativeWeight += probabilityWeight;
 
                                 if (probabilityWeight > Epsilon &&
-                                    host.Rand.NextFloat() < probabilityWeight / cumulativeWeight)
+                                    host.Rand.NextSingle() < probabilityWeight / cumulativeWeight)
                                 {
                                     // again, numerical error may cause selection of the same candidate twice, so ensure that the distance is non-trivially positive
                                     Utils.Swap(ref cursor.Features, ref candidate);
diff --git a/src/Microsoft.ML.PipelineInference/AutoMlEngines/UniformRandomEngine.cs b/src/Microsoft.ML.PipelineInference/AutoMlEngines/UniformRandomEngine.cs
index 2a4bb51836..9f304312c2 100644
--- a/src/Microsoft.ML.PipelineInference/AutoMlEngines/UniformRandomEngine.cs
+++ b/src/Microsoft.ML.PipelineInference/AutoMlEngines/UniformRandomEngine.cs
@@ -123,11 +123,11 @@ private void RandomlyPerturbSweepableHyperparameters(IEnumerable<TlcModule.Sweep
                         break;
                     case TlcModule.SweepableFloatParamAttribute floParam:
                         var fvg = AutoMlUtils.ToIValueGenerator(floParam);
-                        floParam.RawValue = ((IParameterValue<float>)fvg.CreateFromNormalized(Host.Rand.NextFloat())).Value;
+                        floParam.RawValue = ((IParameterValue<float>)fvg.CreateFromNormalized(Host.Rand.NextSingle())).Value;
                         break;
                     case TlcModule.SweepableLongParamAttribute lonParam:
                         var lvg = AutoMlUtils.ToIValueGenerator(lonParam);
-                        lonParam.RawValue = ((IParameterValue<long>)lvg.CreateFromNormalized(Host.Rand.NextFloat())).Value;
+                        lonParam.RawValue = ((IParameterValue<long>)lvg.CreateFromNormalized(Host.Rand.NextSingle())).Value;
                         break;
                     default:
                         throw new NotSupportedException($"Unknown type of sweepable parameter attribute: {param.GetType()}");
diff --git a/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LbfgsPredictorBase.cs b/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LbfgsPredictorBase.cs
index e4273d7ce9..89f4866228 100644
--- a/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LbfgsPredictorBase.cs
+++ b/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LbfgsPredictorBase.cs
@@ -201,7 +201,7 @@ protected virtual Optimizer InitializeOptimizer(IChannel ch, FloatLabelCursor.Fa
             {
                 Float[] initWeights = new Float[BiasCount + WeightCount];
                 for (int j = 0; j < initWeights.Length; j++)
-                    initWeights[j] = InitWtsDiameter * (Host.Rand.NextFloat() - (Float)0.5);
+                    initWeights[j] = InitWtsDiameter * (Host.Rand.NextSingle() - (Float)0.5);
                 init = new VBuffer<Float>(initWeights.Length, initWeights);
             }
             else if (SgdInitializationTolerance > 0)
diff --git a/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs b/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs
index 253800171d..bcd4b33d58 100644
--- a/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs
+++ b/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs
@@ -238,8 +238,8 @@ protected virtual void InitCore(IChannel ch, int numFeatures, LinearPredictor pr
             {
                 Weights = VBufferUtils.CreateDense<Float>(NumFeatures);
                 for (int i = 0; i < NumFeatures; i++)
-                    Weights.Values[i] = Args.InitWtsDiameter * (Host.Rand.NextFloat() - (Float)0.5);
-                Bias = Args.InitWtsDiameter * (Host.Rand.NextFloat() - (Float)0.5);
+                    Weights.Values[i] = Args.InitWtsDiameter * (Host.Rand.NextSingle() - (Float)0.5);
+                Bias = Args.InitWtsDiameter * (Host.Rand.NextSingle() - (Float)0.5);
             }
             else if (NumFeatures <= 1000)
                 Weights = VBufferUtils.CreateDense<Float>(NumFeatures);
diff --git a/src/Microsoft.ML.Transforms/RffTransform.cs b/src/Microsoft.ML.Transforms/RffTransform.cs
index 6063559923..b8f49b4dce 100644
--- a/src/Microsoft.ML.Transforms/RffTransform.cs
+++ b/src/Microsoft.ML.Transforms/RffTransform.cs
@@ -191,7 +191,7 @@ private void GetDDimensionalFeatureMapping(int rowSize)
             private void GetDRotationTerms(int colSize)
             {
                 for (int i = 0; i < colSize; ++i)
-                    RotationTerms[i] = (_rand.NextFloat() - (Float)0.5) * (Float)Math.PI;
+                    RotationTerms[i] = (_rand.NextSingle() - (Float)0.5) * (Float)Math.PI;
             }
 
             private void InitializeFourierCoefficients(int rowSize, int colSize)