diff --git a/mask-detection/3-automatic-pipeline.ipynb b/mask-detection/3-automatic-pipeline.ipynb
index fc44ec09..8dbfec06 100644
--- a/mask-detection/3-automatic-pipeline.ipynb
+++ b/mask-detection/3-automatic-pipeline.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Mask Detection Demo - Automatic Pipeline (3 / 3)\n",
     "\n",
-    "The following example demonstrates how to package a project and how to run an automatic pipeline for training, evaluating, optimizing and serving the mask detection model using our saved MLRun functions from the previous notebooks.\n",
+    "The following example demonstrates how to package a project and how to run an automatic pipeline to train, evaluate, optimize and serve the mask detection model using our saved MLRun functions from the previous notebooks.\n",
     "\n",
     "1. [Set up the project](#section_1)\n",
     "2. [Write and save the workflow](#section_2)\n",
@@ -322,7 +322,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that after running the cell above, the `workflow.py` file is created. Saving your workflow to file allows you to use it if run the project from a different environment.\n",
+    "Note that after running the cell above, the `workflow.py` file is created. Saving your workflow to file allows you to run the project from a different environment.\n",
     "\n",
     "In order to take this project with the functions we set and the workflow we saved over to a different environemnt, first set the workflow to the project. The workflow can be set using `project.set_workflow`. After setting it, we will save the project by calling `project.save`. When loaded, it can be run from another environment from both code and from cli. For more information regarding saving and loading a MLRun project, see the [documentation](https://docs.mlrun.org/en/latest/projects/overview.html)."
    ]
@@ -719,4 +719,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}