diff --git a/mask_detection.ipynb b/mask_detection.ipynb
index 46488ad..3eab5c5 100644
--- a/mask_detection.ipynb
+++ b/mask_detection.ipynb
@@ -6,15 +6,15 @@
    "source": [
     "# Mask detection demo\n",
     "The following example demonstrates an end to end data science workflow for building an image classifier model.<br/>\n",
-    "The model is trained on dataset holding images of people with or without masks.<br/>\n",
-    "The model is then deployed to a Nuclio function allowing users to send http request with an image and getting a respond\n",
-    "back with the probability of mask existence for the people in the picture.\n",
+    "The model is trained on a dataset containing images of people with or without masks.<br/>\n",
+    "The model is then deployed to a Nuclio function that allows users to send an http request with an image and receive a response\n",
+    " with the probability that the person in the picture is wearing a mask.\n",
     "\n",
-    "## key technologies\n",
-    "- **Tensorflow-Keras** for training the model\n",
-    "- **Horovod** for running a distributed training\n",
-    "- **Nuclio** for creating an high performance serverless Serving function\n",
-    "- **MLRun** for orchestrating it all together"
+    "## Key Technologies\n",
+    "- **Tensorflow-Keras** to train the model\n",
+    "- **Horovod** to run distributed training\n",
+    "- **Nuclio** to create a high-performance serverless Serving function\n",
+    "- **MLRun** to orchestrate the process"
    ]
   },
   {
@@ -69,7 +69,7 @@
    "source": [
     "### Import the dataset\n",
     "[The MLRun functions marketplace](https://github.com/mlrun/functions/) (a.k.a. \"the MLRun functions hub\") is a \n",
-    "centralized location for open-source contributions of function components that are commonly used in machine-learning \n",
+    "centralized location for open-source contributions of function components that are commonly used in machine learning \n",
     "development.\n",
     "\n",
     "This step uses the [Open archive marketplace function](https://github.com/mlrun/functions/tree/master/open_archive) \n",
@@ -91,9 +91,9 @@
    "source": [
     "**Running the function**\n",
     "\n",
-    "Note that we're setting the `local` param to `True` which means the function code will run locally inside the jupyter \n",
-    "kernel. We could as well set it to `False` (the default), then MLRun will automatically spawn a K8s Pod and run the \n",
-    "function inside it, allowing us to isolate the function running environment and using our k8s cluster resources.\n",
+    "Note that in this case we're setting the `local` param to `True` which means the function code will run locally inside the jupyter \n",
+    "kernel. Alternatively, we could set it to `False` (the default), in which case MLRun would automatically spawn a k8s pod and run the \n",
+    "function inside it, allowing us to isolate the function running environment and use our k8s cluster resources.\n",
     "\n",
     "MAKE THIS WORK - currently without local it fails because of a bug in the function code"
    ]
@@ -345,18 +345,18 @@
    "source": [
     "### Training the model\n",
     "The training code is basically the same as the code from [the kaggle project](https://www.kaggle.com/notadithyabhat/face-mask-detector/execution) \n",
-    "with slight changes to allow levaraging horovod for doing distributed training (done by following [horovod's usage instructions](https://github.com/horovod/horovod#usage)). \n",
-    "The original training code can be found in [here](training_code_original.py).\n",
+    "with slight changes to allow us to use horovod to do distributed training (which we can do by following [horovod's usage instructions](https://github.com/horovod/horovod#usage)). \n",
+    "The original training code can be found [here](training_code_original.py).\n",
     "\n",
-    "The changes done are roughly:\n",
+    "Roughly, the changes between the kaggle project and this one are:\n",
     "- Code at the beginning to extract job params from MLRun context\n",
     "- Code to initialize horovod and assign devices (CPU/GPU) before building the model\n",
     "- Replace to use the horovod optimizer\n",
     "- Code at the end to log the results to MLRun DB\n",
     "\n",
-    "In the code below we're starting to see **the value of MLRun** - in a matter of value change we can move our \n",
+    "In the code below we're starting to see **the value of MLRun** - with a simple value change we can move our \n",
     "**training** to be **distributed** across **several workers**, assign **GPUs** and more. No need to write dockerfiles, \n",
-    "no need to hassle with k8s yamls, MLRun does all of this for us.\n",
+    "no need to hassle with k8s yamls:/ MLRun does all of this for us.\n",
     "\n",
     "REPLACE IMAGE - using my image as I'm waiting for the images refactor PR to be merged - the PR will lower the image \n",
     "size + add a package (opencv) I'm missing for this demo"
@@ -1011,9 +1011,9 @@
     "from the hub.\n",
     "The serving function kind is using [Nuclio](https://github.com/nuclio/nuclio/) - an high-performance serverless event \n",
     "and data processing platform.\n",
-    "With several lines of code we were able to take our model, **expose** it with an **http endpoint** and deploy it on an \n",
-    "**high performance** infrastructure that can easily scale to serve on **production scale** (hundreds of thousands of \n",
-    "requests per second)\n",
+    "With several lines of code we were able to take our model, **expose** it with an **http endpoint** and deploy it on a \n",
+    "**high performance** infrastructure that can easily scale up to serve on **production scale** with hundreds of thousands of \n",
+    "requests per second\n",
     "\n",
     "CHANGE to actually make it use the hub function - currently hub function fits only classes (classification models) and \n",
     "not this one which just gives a number - probablility"
@@ -1088,8 +1088,8 @@
    "metadata": {},
    "source": [
     "### Using the model\n",
-    "Here we're demonstrating how to take an image, send it to our serving function API, and seeing the model prediction for \n",
-    "the probability the people in the image are wearing mask"
+    "Here we're demonstrating how to take an image, send it to our serving function API, and see the model's prediction of \n",
+    "the probability that the people in the image are wearing a mask."
    ]
   },
   {
@@ -1182,17 +1182,17 @@
    "metadata": {},
    "source": [
     "## Summary & conclusions:\n",
-    "- MLOps is a big thing should not be underestimated when planning a datascience project.\n",
-    "- Using the right frameworks, like MLRun, enables you to take an existing training code, and build a full data science \n",
-    "project out of it. \n",
-    "- In a matter of no more than 200 lines of code we were able to distribute our training, track our different trials, and\n",
-    " deploy our final model on a production scale infrastructure.\n",
+    "- MLOps is a critical factor big thing should not be underestimated when planning a datascience project.\n",
+    "- The right framework (like MLRun in this case) enables you to take an existing training code, and quickly build it into a full production-ready data science \n",
+    "project. \n",
+    "- With just 200 lines of code we were able to distribute our training, track our different trials, and\n",
+    " deploy our final model on production scale infrastructure.\n",
     "- MLRun has much more to offer (search for something like קצרה היריעה מלהכיל) - function versioning, feature store, \n",
     "automatic pipeline scheduling, model monitoring.\n",
     "\n",
     "\n",
     "### The author\n",
-    "Hedi Ingber is the main maintainer of MLRun, and working as a Backend engineer at Iguazio which offers the enterprise \n",
+    "Hedi Ingber is the main maintainer of MLRun. He's also a Backend Engineer at Iguazio, which offers the enterprise \n",
     "version of MLRun.\n",
     " "
    ]
@@ -1235,4 +1235,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}