diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index b486960..6545529 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -16,12 +16,11 @@ jobs:
       matrix:
         julia-version:
           - "1.0"
+          - "1.6"
           - "1"
           - "nightly"
         os:
           - ubuntu-latest
-          - macos-latest
-          - windows-latest
         julia-arch:
           - x64
     steps:
diff --git a/Project.toml b/Project.toml
index 2498d44..64a673d 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "MLJSerialization"
 uuid = "17bed46d-0ab5-4cd4-b792-a5c4b8547c6d"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "1.1.2"
+version = "1.1.3"
 
 [deps]
 IterationControl = "b3c1a2ee-3fec-4384-bf48-272ea71de57c"
@@ -10,9 +10,9 @@ MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 
 [compat]
-IterationControl = "0.4"
+IterationControl = "0.4,0.5"
 JLSO = "2.1"
-MLJBase = "0.18"
+MLJBase = "0.18,0.19"
 MLJModelInterface = "0.4, 1.0"
 julia = "1"
 
@@ -20,11 +20,10 @@ julia = "1"
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-MLJScientificTypes = "2e2323e0-db8b-457b-ae0d-bdfb3bc63afd"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["CategoricalArrays", "DecisionTree", "Distributions", "MLJScientificTypes", "Random", "StableRNGs", "Statistics", "Test"]
+test = ["CategoricalArrays", "DecisionTree", "Distributions", "Random", "StableRNGs", "Statistics", "Test"]
diff --git a/README.md b/README.md
index 2426135..d029baa 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 | Linux | Coverage |
 | :-----------: | :------: |
-| [![Build status](https://github.com/JuliaAI/MLJSerialization.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/MLJSerialization.jl/actions)| [![codecov.io](http://codecov.io/github/JuliaAI/MLJSerialization.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaAI/MLJSerialization.jl?branch=master) |
+| [![Build status](https://github.com/JuliaAI/MLJSerialization.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/MLJSerialization.jl/actions)| [![codecov.io](http://codecov.io/github/JuliaAI/MLJSerialization.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaAI/MLJSerialization.jl?branch=dev) |
 
 A package adding model serialization to the
 [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) machine
diff --git a/test/_dummy_model.jl b/test/_dummy_model.jl
index 49129ab..8286ca0 100644
--- a/test/_dummy_model.jl
+++ b/test/_dummy_model.jl
@@ -149,9 +149,9 @@ MMI.training_losses(::DummyIterativeModel, report) = report.training_losses
 
 MMI.supports_training_losses(::Type{<:DummyIterativeModel}) = true
 MMI.input_scitype(::Type{<:DummyIterativeModel}) =
-    AbstractVector{<:MMI.ScientificTypes.Finite}
+    AbstractVector{<:MMI.ScientificTypesBase.Finite}
 MMI.target_scitype(::Type{<:DummyIterativeModel}) =
-    AbstractVector{<:MMI.ScientificTypes.Continuous}
+    AbstractVector{<:MMI.ScientificTypesBase.Continuous}
 
 
 # # FOR SYTHESIZING DATA FOR USE WITH DUMMY MODEL
diff --git a/test/_models/DecisionTree.jl b/test/_models/DecisionTree.jl
index e8ebe83..f46d191 100644
--- a/test/_models/DecisionTree.jl
+++ b/test/_models/DecisionTree.jl
@@ -3,8 +3,6 @@ export DecisionTreeClassifier, DecisionTreeRegressor
 import MLJBase
 import MLJBase: @mlj_model, metadata_pkg, metadata_model
 
-using MLJScientificTypes
-
 using CategoricalArrays
 
 import DecisionTree
@@ -207,13 +205,13 @@ metadata_pkg.((DecisionTreeClassifier, DecisionTreeRegressor),
               is_wrapper=false)
 
 metadata_model(DecisionTreeClassifier,
-               input=MLJBase.Table(Continuous, Count, OrderedFactor),
-               target=AbstractVector{<:MLJBase.Finite},
+               input=MLJModelInterface.Table(Continuous, Count, OrderedFactor),
+               target=AbstractVector{<:MLJModelInterface.Finite},
                weights=false,
                descr=DTC_DESCR)
 
 metadata_model(DecisionTreeRegressor,
-               input=MLJBase.Table(Continuous, Count, OrderedFactor),
-               target=AbstractVector{MLJBase.Continuous},
+               input=MLJModelInterface.Table(Continuous, Count, OrderedFactor),
+               target=AbstractVector{MLJModelInterface.Continuous},
                weights=false,
                descr=DTR_DESCR)