diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 4e8f4f28..29baf6db 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -5,7 +5,6 @@ on:
   push:
     branches:
       - draft
-      - draft-2.0
       - master
 jobs:
   deploy:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 407447dc..f0b68043 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,7 +16,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     - `filter_vector`
     - `flatten_dimensions`
     - `load_geojson`
+    - `load_ml_model`
     - `load_url`
+    - `ml_fit_class_random_forest`
+    - `ml_fit_regr_random_forest`
+    - `ml_predict`
+    - `save_ml_model`
     - `unflatten_dimension`
     - `vector_buffer`
     - `vector_reproject`
diff --git a/meta/subtype-schemas.json b/meta/subtype-schemas.json
index 347df234..83ce72ba 100644
--- a/meta/subtype-schemas.json
+++ b/meta/subtype-schemas.json
@@ -232,6 +232,12 @@
                 }
             }
         },
+        "ml-model": {
+            "type": "object",
+            "subtype": "ml-model",
+            "title": "Machine Learning Model",
+            "description": "A machine learning model, accompanied with STAC metadata that implements the the STAC ml-model extension."
+        },
         "output-format": {
             "type": "string",
             "subtype": "output-format",
diff --git a/proposals/aggregate_spatial_window.json b/proposals/aggregate_spatial_window.json
index 747db151..9e5dce4a 100644
--- a/proposals/aggregate_spatial_window.json
+++ b/proposals/aggregate_spatial_window.json
@@ -75,7 +75,7 @@
         },
         {
             "name": "boundary",
-            "description": "Behavior to apply if the number of values for the axes `x` and `y` is not a multiple of the corresponding value in the `size` parameter. Options are:\n\n- `pad` (default): pad the data cube with the no-data value `null` to fit the required window size.\n\n- `trim`: trim the data cube to fit the required window size.\n\nSet the parameter `align` to specifies to which corner the data is aligned to.",
+            "description": "Behavior to apply if the number of values for the axes `x` and `y` is not a multiple of the corresponding value in the `size` parameter. Options are:\n\n- `pad` (default): pad the data cube with the no-data value `null` to fit the required window size.\n\n- `trim`: trim the data cube to fit the required window size.\n\nUse the parameter `align` to align the data to the desired corner.",
             "schema": {
                 "type": "string",
                 "enum": [
diff --git a/proposals/load_ml_model.json b/proposals/load_ml_model.json
new file mode 100644
index 00000000..7fa86d89
--- /dev/null
+++ b/proposals/load_ml_model.json
@@ -0,0 +1,46 @@
+{
+    "id": "load_ml_model",
+    "summary": "Load a ML model",
+    "description": "Loads a machine learning model from a STAC Item.\n\nSuch a model could be trained and saved as part of a previous batch job with processes such as  ``ml_fit_regr_random_forest()`` and ``save_ml_model()``.",
+    "categories": [
+        "machine learning",
+        "import"
+    ],
+    "experimental": true,
+    "parameters": [
+        {
+            "name": "uri",
+            "description": "The STAC Item to load the machine learning model from. The STAC Item must implement the `ml-model` extension.",
+            "schema": [
+                {
+                    "title": "URL",
+                    "type": "string",
+                    "format": "uri",
+                    "subtype": "uri",
+                    "pattern": "^https?://"
+                },
+                {
+                    "title": "User-uploaded File",
+                    "type": "string",
+                    "subtype": "file-path",
+                    "pattern": "^[^\r\n\\:'\"]+$"
+                }
+            ]
+        }
+    ],
+    "returns": {
+        "description": "A machine learning model to be used with machine learning processes such as ``ml_predict()``.",
+        "schema": {
+            "type": "object",
+            "subtype": "ml-model"
+        }
+    },
+    "links": [
+        {
+            "href": "https://github.com/stac-extensions/ml-model",
+            "title": "STAC ml-model extension",
+            "type": "text/html",
+            "rel": "about"
+        }
+    ]
+}
diff --git a/proposals/ml_fit_class_random_forest.json b/proposals/ml_fit_class_random_forest.json
new file mode 100644
index 00000000..63da48a1
--- /dev/null
+++ b/proposals/ml_fit_class_random_forest.json
@@ -0,0 +1,110 @@
+{
+    "id": "ml_fit_class_random_forest",
+    "summary": "Train a random forest classification model",
+    "description": "Executes the fit of a random forest classification based on training data. The process does not include a separate split of the data in test, validation and training data. The Random Forest classification model is based on the approach by Breiman (2001).",
+    "categories": [
+        "machine learning"
+    ],
+    "experimental": true,
+    "parameters": [
+        {
+            "name": "predictors",
+            "description": "The predictors for the classification model as a vector data cube. Aggregated to the features (vectors) of the target input variable.",
+            "schema": [
+                {
+                    "type": "object",
+                    "subtype": "datacube",
+                    "dimensions": [
+                        {
+                            "type": "geometry"
+                        },
+                        {
+                            "type": "bands"
+                        }
+                    ]
+                },
+                {
+                    "type": "object",
+                    "subtype": "datacube",
+                    "dimensions": [
+                        {
+                            "type": "geometry"
+                        },
+                        {
+                            "type": "other"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "name": "target",
+            "description": "The training sites for the classification model as a vector data cube. This is associated with the target variable for the Random Forest model. The geometry has to associated with a value to predict (e.g. fractional forest canopy cover).",
+            "schema": {
+                "type": "object",
+                "subtype": "datacube",
+                "dimensions": [
+                    {
+                        "type": "geometry"
+                    }
+                ]
+            }
+        },
+        {
+            "name": "max_variables",
+            "description": "Specifies how many split variables will be used at a node.\n\nThe following options are available:\n\n- *integer*: The given number of variables are considered for each split.\n- `all`: All variables are considered for each split.\n- `log2`: The logarithm with base 2 of the number of variables are considered for each split.\n- `onethird`: A third of the number of variables are considered for each split.\n- `sqrt`: The square root of the number of variables are considered for each split. This is often the default for classification.",
+            "schema": [
+                {
+                    "type": "integer",
+                    "minimum": 1
+                },
+                {
+                    "type": "string",
+                    "enum": [
+                        "all",
+                        "log2",
+                        "onethird",
+                        "sqrt"
+                    ]
+                }
+            ]
+        },
+        {
+            "name": "num_trees",
+            "description": "The number of trees build within the Random Forest classification.",
+            "optional": true,
+            "default": 100,
+            "schema": {
+                "type": "integer",
+                "minimum": 1
+            }
+        },
+        {
+            "name": "seed",
+            "description": "A randomization seed to use for the random sampling in training. If not given or `null`, no seed is used and results may differ on subsequent use.",
+            "optional": true,
+            "default": null,
+            "schema": {
+                "type": [
+                    "integer",
+                    "null"
+                ]
+            }
+        }
+    ],
+    "returns": {
+        "description": "A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.",
+        "schema": {
+            "type": "object",
+            "subtype": "ml-model"
+        }
+    },
+    "links": [
+        {
+            "href": "https://doi.org/10.1023/A:1010933404324",
+            "title": "Breiman (2001): Random Forests",
+            "type": "text/html",
+            "rel": "about"
+        }
+    ]
+}
diff --git a/proposals/ml_fit_regr_random_forest.json b/proposals/ml_fit_regr_random_forest.json
new file mode 100644
index 00000000..39207324
--- /dev/null
+++ b/proposals/ml_fit_regr_random_forest.json
@@ -0,0 +1,110 @@
+{
+    "id": "ml_fit_regr_random_forest",
+    "summary": "Train a random forest regression model",
+    "description": "Executes the fit of a random forest regression based on training data. The process does not include a separate split of the data in test, validation and training data. The Random Forest regression model is based on the approach by Breiman (2001).",
+    "categories": [
+        "machine learning"
+    ],
+    "experimental": true,
+    "parameters": [
+        {
+            "name": "predictors",
+            "description": "The predictors for the regression model as a vector data cube. Aggregated to the features (vectors) of the target input variable.",
+            "schema": [
+                {
+                    "type": "object",
+                    "subtype": "datacube",
+                    "dimensions": [
+                        {
+                            "type": "geometry"
+                        },
+                        {
+                            "type": "bands"
+                        }
+                    ]
+                },
+                {
+                    "type": "object",
+                    "subtype": "datacube",
+                    "dimensions": [
+                        {
+                            "type": "geometry"
+                        },
+                        {
+                            "type": "other"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "name": "target",
+            "description": "The training sites for the regression model as a vector data cube. This is associated with the target variable for the Random Forest model. The geometry has to associated with a value to predict (e.g. fractional forest canopy cover).",
+            "schema": {
+                "type": "object",
+                "subtype": "datacube",
+                "dimensions": [
+                    {
+                        "type": "geometry"
+                    }
+                ]
+            }
+        },
+        {
+            "name": "max_variables",
+            "description": "Specifies how many split variables will be used at a node.\n\nThe following options are available:\n\n- *integer*: The given number of variables are considered for each split.\n- `all`: All variables are considered for each split.\n- `log2`: The logarithm with base 2 of the number of variables are considered for each split.\n- `onethird`: A third of the number of variables are considered for each split. This is often the default for regression.\n- `sqrt`: The square root of the number of variables are considered for each split.",
+            "schema": [
+                {
+                    "type": "integer",
+                    "minimum": 1
+                },
+                {
+                    "type": "string",
+                    "enum": [
+                        "all",
+                        "log2",
+                        "onethird",
+                        "sqrt"
+                    ]
+                }
+            ]
+        },
+        {
+            "name": "num_trees",
+            "description": "The number of trees build within the Random Forest regression.",
+            "optional": true,
+            "default": 100,
+            "schema": {
+                "type": "integer",
+                "minimum": 1
+            }
+        },
+        {
+            "name": "seed",
+            "description": "A randomization seed to use for the random sampling in training. If not given or `null`, no seed is used and results may differ on subsequent use.",
+            "optional": true,
+            "default": null,
+            "schema": {
+                "type": [
+                    "integer",
+                    "null"
+                ]
+            }
+        }
+    ],
+    "returns": {
+        "description": "A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.",
+        "schema": {
+            "type": "object",
+            "subtype": "ml-model"
+        }
+    },
+    "links": [
+        {
+            "href": "https://doi.org/10.1023/A:1010933404324",
+            "title": "Breiman (2001): Random Forests",
+            "type": "text/html",
+            "rel": "about"
+        }
+    ]
+}
diff --git a/proposals/ml_predict.json b/proposals/ml_predict.json
new file mode 100644
index 00000000..87cd2500
--- /dev/null
+++ b/proposals/ml_predict.json
@@ -0,0 +1,49 @@
+{
+    "id": "ml_predict",
+    "summary": "Predict using ML",
+    "description": "Applies a machine learning model to a data cube of input features and returns the predicted values.",
+    "categories": [
+        "machine learning"
+    ],
+    "experimental": true,
+    "parameters": [
+        {
+            "name": "data",
+            "description": "The data cube containing the input features.",
+            "schema": {
+                "type": "object",
+                "subtype": "datacube"
+            }
+        },
+        {
+            "name": "model",
+            "description": "A ML model that was trained with one of the ML training processes such as ``ml_fit_regr_random_forest()``.",
+            "schema": {
+                "type": "object",
+                "subtype": "ml-model"
+            }
+        },
+        {
+            "name": "dimensions",
+            "description": "Zero or more dimensions that will be reduced by the model. Fails with a `DimensionNotAvailable` exception if one of the specified dimensions does not exist.",
+            "schema": {
+                "type": "array",
+                "items": {
+                    "type": "string"
+                }
+            }
+        }
+    ],
+    "returns": {
+        "description": "A data cube with the predicted values. It removes the specified dimensions and adds new dimension for the predicted values. It has the name `predictions` and is of type `other`. If a single value is returned, the dimension has a single label with name `0`.",
+        "schema": {
+            "type": "object",
+            "subtype": "datacube",
+            "dimensions": [
+                {
+                    "type": "other"
+                }
+            ]
+        }
+    }
+}
diff --git a/proposals/predict_curve.json b/proposals/predict_curve.json
index 479b7fec..c4d78d99 100644
--- a/proposals/predict_curve.json
+++ b/proposals/predict_curve.json
@@ -1,7 +1,7 @@
 {
     "id": "predict_curve",
-    "summary": "Predict values",
-    "description": "Predict values using a model function and pre-computed parameters. The process is intended to compute values for new labels.",
+    "summary": "Predict values using a model function",
+    "description": "Predict values using a model function and pre-computed parameters. The process is primarily intended to compute values for new labels, but it can also fill gaps where existing labels contain no-data (`null`) values.",
     "categories": [
         "cubes",
         "math"
diff --git a/proposals/save_ml_model.json b/proposals/save_ml_model.json
new file mode 100644
index 00000000..5e9ea8b0
--- /dev/null
+++ b/proposals/save_ml_model.json
@@ -0,0 +1,44 @@
+{
+    "id": "save_ml_model",
+    "summary": "Save a ML model",
+    "description": "Saves a machine learning model as part of a batch job.\n\nThe model will be accompanied by a separate STAC Item that implements the [ml-model extension](https://github.com/stac-extensions/ml-model).",
+    "categories": [
+        "machine learning",
+        "import"
+    ],
+    "experimental": true,
+    "parameters": [
+        {
+            "name": "data",
+            "description": "The data to store as a machine learning model.",
+            "schema": {
+                "type": "object",
+                "subtype": "ml-model"
+            }
+        },
+        {
+            "name": "options",
+            "description": "Additional parameters to create the file(s).",
+            "schema": {
+                "type": "object",
+                "additionalParameters": false
+            },
+            "default": {},
+            "optional": true
+        }
+    ],
+    "returns": {
+        "description": "Returns `false` if the process failed to store the model, `true` otherwise.",
+        "schema": {
+            "type": "boolean"
+        }
+    },
+    "links": [
+        {
+            "href": "https://github.com/stac-extensions/ml-model",
+            "title": "STAC ml-model extension",
+            "type": "text/html",
+            "rel": "about"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/tests/docs.html b/tests/docs.html
index 23ef98e4..04b1c192 100644
--- a/tests/docs.html
+++ b/tests/docs.html
@@ -113,8 +113,8 @@
         props: {
           document: 'processes.json',
           categorize: true,
-          apiVersion: '1.1.0',
-          title: 'openEO processes (draft)',
+          apiVersion: '1.2.0',
+          title: 'openEO processes (2.0.0-rc.1)',
           notice: '**Note:** This is the list of all processes specified by the openEO project. Back-ends implement a varying set of processes. Thus, the processes you can use at a specific back-end may derive from the specification, may include non-standardized processes and may not implement all processes listed here. Please check each back-end individually for the processes they support. The client libraries usually have a function called `listProcesses` or `list_processes` for that.'
         }
       })
diff --git a/tests/package.json b/tests/package.json
index 15f6d2e8..1da8693f 100644
--- a/tests/package.json
+++ b/tests/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@openeo/processes",
-  "version": "2.0.0",
+  "version": "2.0.0-rc.1",
   "author": "openEO Consortium",
   "contributors": [
     {