diff --git a/docs/en/ml/aggregations.asciidoc b/docs/en/ml/aggregations.asciidoc
index 99011ba99e1..7739dd142c0 100644
--- a/docs/en/ml/aggregations.asciidoc
+++ b/docs/en/ml/aggregations.asciidoc
@@ -33,7 +33,7 @@ PUT _xpack/ml/anomaly_detectors/farequote
       "field_name":"responsetime",
       "by_field_name":"airline"
     }],
-    "summary_count_field": "doc_count"
+    "summary_count_field_name": "doc_count"
   },
   "data_description": {
     "time_field":"time"
diff --git a/docs/en/ml/functions/count.asciidoc b/docs/en/ml/functions/count.asciidoc
index 7248021dce5..d47b366513d 100644
--- a/docs/en/ml/functions/count.asciidoc
+++ b/docs/en/ml/functions/count.asciidoc
@@ -89,7 +89,7 @@ compared to its past behavior.
 [source,js]
 --------------------------------------------------
 {
-  "summary_count_field" : "events_per_min",
+  "summary_count_field_name" : "events_per_min",
   "detectors" [
       { "function" : "count" }
    ]
@@ -98,7 +98,7 @@ compared to its past behavior.
 
 If you are analyzing an aggregated `events_per_min` field, do not use a sum
 function (for example, `sum(events_per_min)`). Instead, use the count function
-and the `summary_count_field` property.
+and the `summary_count_field_name` property.
 //TO-DO: For more information, see <<aggreggations.asciidoc>>.
 
 [float]
diff --git a/docs/en/ml/getting-started.asciidoc b/docs/en/ml/getting-started.asciidoc
index 978d9984d47..d582381da22 100644
--- a/docs/en/ml/getting-started.asciidoc
+++ b/docs/en/ml/getting-started.asciidoc
@@ -331,7 +331,7 @@ requests over time. It is therefore logical to start by creating a single metric
 job for this KPI.
 
 TIP: If you are using aggregated data, you can create an advanced job
-and configure it to use a `summary_count_field`. The {ml} algorithms will
+and configure it to use a `summary_count_field_name`. The {ml} algorithms will
 make the best possible use of summarized data in this case. For simplicity, in
 this tutorial we will not make use of that advanced functionality.