diff --git a/src/MLJBase.jl b/src/MLJBase.jl
index 0d58635d..9842323d 100644
--- a/src/MLJBase.jl
+++ b/src/MLJBase.jl
@@ -248,7 +248,8 @@ export params
 # -------------------------------------------------------------------
 # exports from this module, MLJBase
 
-# computational_resources.jl:
+# get/set global constants:
+export default_logger
 export default_resource
 
 # one_dimensional_ranges.jl:
diff --git a/src/init.jl b/src/init.jl
index 947bdc03..d85fede1 100644
--- a/src/init.jl
+++ b/src/init.jl
@@ -3,6 +3,7 @@ function __init__()
     global DEFAULT_RESOURCE = Ref{AbstractResource}(CPU1())
     global DEFAULT_SCITYPE_CHECK_LEVEL = Ref{Int}(1)
     global SHOW_COLOR = Ref{Bool}(true)
+    global DEFAULT_LOGGER = Ref{Any}(nothing)
 
     # for testing asynchronous training of learning networks:
     global TESTING = parse(Bool, get(ENV, "TEST_MLJBASE", "false"))
diff --git a/src/machines.jl b/src/machines.jl
index 1a3f5388..8f5aa438 100644
--- a/src/machines.jl
+++ b/src/machines.jl
@@ -1088,6 +1088,25 @@ function save(file::Union{String,IO}, mach::Machine)
     serialize(file, smach)
 end
 
+const ERR_INVALID_DEFAULT_LOGGER = ArgumentError(
+    "You have attempted to save a machine to the default logger "*
+    "but `default_logger()` is currently `nothing`. "*
+    "Either specify an explicit logger, path or stream to save to, "*
+    "or use `default_logger(logger)` "*
+    "to change the default logger. "
+)
+
+"""
+    MLJ.save(mach)
+    MLJBase.save(mach)
+
+Save the current machine as an artifact at the location associated with
+`default_logger`](@ref).
+
+"""
+MLJBase.save(mach::Machine) = MLJBase.save(default_logger(), mach)
+MLJBase.save(::Nothing, ::Machine) = throw(ERR_INVALID_DEFAULT_LOGGER)
+
 report_for_serialization(mach) = mach.report
 
 # NOTE. there is also a specialization of `report_for_serialization` for `Composite`
diff --git a/src/resampling.jl b/src/resampling.jl
index 68ecc040..dd317092 100644
--- a/src/resampling.jl
+++ b/src/resampling.jl
@@ -1,4 +1,4 @@
-# # TYPE ALIASES
+ # TYPE ALIASES
 
 const AbstractRow = Union{AbstractVector{<:Integer}, Colon}
 const TrainTestPair = Tuple{AbstractRow,AbstractRow}
@@ -747,6 +747,64 @@ Base.show(io::IO, e::CompactPerformanceEvaluation) =
     print(io, "CompactPerformanceEvaluation$(_summary(e))")
 
 
+
+# ===============================================================
+## USER CONTROL OF DEFAULT LOGGING
+
+const DOC_DEFAULT_LOGGER =
+    """
+
+   The default logger is used in calls to [`evaluate!`](@ref) and [`evaluate`](@ref), and
+   in the constructors `TunedModel` and `IteratedModel`, unless the `logger` keyword is
+   explicitly specified.
+
+   !!! note
+
+   Prior to MLJ v0.20.7 (and MLJBase 1.5) the default logger was always `nothing`.
+
+"""
+
+"""
+    default_logger()
+
+Return the current value of the default logger for use with supported machine learning
+tracking platforms, such as [MLflow](https://mlflow.org/docs/latest/index.html).
+
+$DOC_DEFAULT_LOGGER
+
+ When MLJBase is first loaded, the default logger is `nothing`. To reset the logger, see
+ beow.
+
+"""
+default_logger() = DEFAULT_LOGGER[]
+
+"""
+    default_logger(logger)
+
+Reset the default logger.
+
+# Example
+
+Suppose an [MLflow](https://mlflow.org/docs/latest/index.html) tracking service is running
+on a local server at `http://127.0.0.1:500`. Then every in every `evaluate` call in which
+`logger` is not specified, as in the example below, the peformance evaluation is
+automatically logged to the service.
+
+```julia-repl
+using MLJ
+logger = MLJFlow.Logger("http://127.0.0.1:5000/api")
+default_logger(logger)
+
+X, y = make_moons()
+model = ConstantClassifier()
+evaluate(model, X, y, measures=[log_loss, accuracy)])
+
+"""
+function default_logger(logger)
+    DEFAULT_LOGGER[] = logger
+end
+
+
 # ===============================================================
 ## EVALUATION METHODS
 
@@ -1068,7 +1126,8 @@ Although `evaluate!` is mutating, `mach.model` and `mach.args` are not mutated.
   `false` the `per_observation` field of the returned object is populated with
   `missing`s. Setting to `false` may reduce compute time and allocations.
 
-- `logger` - a logger object (see [`MLJBase.log_evaluation`](@ref))
+- `logger=default_logger()` - a logger object for forwarding results to a machine learning
+  tracking platform; see [`default_logger`](@ref) for details.
 
 - `compact=false` - if `true`, the returned evaluation object excludes these fields:
   `fitted_params_per_fold`, `report_per_fold`, `train_test_rows`.
@@ -1093,7 +1152,7 @@ function evaluate!(
     check_measure=true,
     per_observation=true,
     verbosity=1,
-    logger=nothing,
+    logger=default_logger(),
     compact=false,
     )
 
@@ -1544,7 +1603,7 @@ end
         acceleration=default_resource(),
         check_measure=true,
         per_observation=true,
-        logger=nothing,
+        logger=default_logger(),
         compact=false,
     )
 
@@ -1624,7 +1683,7 @@ function Resampler(
     repeats=1,
     cache=true,
     per_observation=true,
-    logger=nothing,
+    logger=default_logger(),
     compact=false,
 )
     resampler = Resampler(
diff --git a/test/resampling.jl b/test/resampling.jl
index 33ee41ec..fbf26777 100644
--- a/test/resampling.jl
+++ b/test/resampling.jl
@@ -935,4 +935,21 @@ end
     end
 end
 
+# DUMMY LOGGER
+
+struct  DummyLogger end
+
+MLJBase.save(logger::DummyLogger, mach::Machine) = mach.model
+
+@testset "default logger" begin
+    @test isnothing(default_logger())
+    model = ConstantClassifier()
+    mach = machine(model, make_moons(10)...)
+    fit!(mach, verbosity=0)
+    @test_throws MLJBase.ERR_INVALID_DEFAULT_LOGGER MLJBase.save(mach)
+    default_logger(DummyLogger())
+    @test default_logger() == DummyLogger()
+    @test MLJBase.save(mach) == model
+end
+
 true