Merge branch 'development' of github.com:v3io/tutorials

Author: Sharon-iguazio

README.md

@@ -125,8 +125,8 @@ If you're are a beginner, you might find the following ML guide useful — [
 One of the most important and challenging areas of managing a data science environment is the ability to track experiments.
 Data scientists need a simple way to track and view current and historical experiments along with the metadata that is associated with each experiment.
 This capability is critical for comparing different runs, and eventually helps to determine the best model and configuration for production deployment.
-The platform leverages the open-source [MLrun](https://github.com/mlrun/mlrun) library to help tackle these challenges.
-You can find examples of using MLrun in the [**experiment tracking**](experiment-tracking/01-mlrun-getting-started.ipynb) platform-tutorials directory.
+The platform leverages the open-source [MLRun](https://github.com/mlrun/mlrun) library to help tackle these challenges.
+You can find examples of using MLRun in the [**experiment tracking**](experiment-tracking/01-mlrun-getting-started.ipynb) platform-tutorials directory.
 
 <a id="deploying-models-to-production"></a>
 ### Deploying Models to Production

experiment-tracking/01-mlrun-getting-started.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Experiment Tracking with MLrun\n"
+    "# Experiment Tracking with MLRun\n"
    ]
   },
   {
@@ -15,10 +15,10 @@
     "inputs, and outputs of machine learning related tasks (executions). <br>\n",
     "This package tracks various elements and store them into a database, then the user can view all running jobs as well as historical jobs in a single report <br>\n",
     "The location of the database is configurable and users can run queries for searching specific jobs by various criterias <br>\n",
-    "Users can use MLrun on a local IDE or notebook and later on run the same code on a larger cluster using scale-out containers or function without changing the code <br>\n",
+    "Users can use MLRun on a local IDE or notebook and later on run the same code on a larger cluster using scale-out containers or function without changing the code <br>\n",
     "Note that the source code could be fetched from Github \n",
     "\n",
-    "For a full description of the MLrun library go to https://github.com/mlrun/mlrun"
+    "For a full description of the MLRun library go to https://github.com/mlrun/mlrun"
    ]
   },
   {
@@ -303,7 +303,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load Mlrun and set MLrun environment variables "
+    "## Load Mlrun and set MLRun environment variables "
    ]
   },
   {
@@ -340,7 +340,7 @@
    "metadata": {},
    "source": [
     "In this example we are running a sample \"training\" job that resides under this folder <br>\n",
-    "Once the job is done it displays the information that had been recorded in the MLrun tracking database <br>\n"
+    "Once the job is done it displays the information that had been recorded in the MLRun tracking database <br>\n"
    ]
   },
   {
@@ -593,7 +593,7 @@
     "Hyperparameters are important because they directly control the behaviour of the training algorithm and have a significant impact on the performance of the model is being trained.\n",
     "Data scientists are often want to run the same model with different parameters to figure out which configuration is the best fit for their model. <br>\n",
     "First, you need to create a template (see below) and then run it with hyper_params flag <br>\n",
-    "In this case MLrun tracks the information for each individual running instance making it easy to compare between the different runs<br> <br>\n",
+    "In this case MLRun tracks the information for each individual running instance making it easy to compare between the different runs<br> <br>\n",
     "Note that click on the artifact opens up a popup windown with detailed information"
    ]
   },

experiment-tracking/02-create-pipeline.ipynb

@@ -135,7 +135,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before running a pipeline job we need to either create an image with the relevant code and libraries we are using or use an existing one. Here is an example of creating an image using MLrun. We would use this image as a base image where we can run the code on top of it"
+    "Before running a pipeline job we need to either create an image with the relevant code and libraries we are using or use an existing one. Here is an example of creating an image using MLRun. We would use this image as a base image where we can run the code on top of it"
    ]
   },
   {

welcome.ipynb

@@ -161,8 +161,8 @@
     "One of the most important and challenging areas of managing a data science environment is the ability to track experiments.\n",
     "Data scientists need a simple way to track and view current and historical experiments along with the metadata that is associated with each experiment.\n",
     "This capability is critical for comparing different runs, and eventually helps to determine the best model and configuration for production deployment.\n",
-    "The platform leverages the open-source [MLrun](https://github.com/mlrun/mlrun) library to help tackle these challenges.\n",
-    "You can find examples of using MLrun in the [**experiment tracking**](experiment-tracking/01-mlrun-getting-started.ipynb) platform-tutorials directory."
+    "The platform leverages the open-source [MLRun](https://github.com/mlrun/mlrun) library to help tackle these challenges.\n",
+    "You can find examples of using MLRun in the [**experiment tracking**](experiment-tracking/01-mlrun-getting-started.ipynb) platform-tutorials directory."
    ]
   },
   {