Open MLOps -> OpenMLOps

tutorials/README.md

@@ -1,6 +1,6 @@
-# Open MLOps tutorials
+# OpenMLOps tutorials
 
-Here is a set of guides to get you started with Open MLOps.
+Here is a set of guides to get you started with OpenMLOps.
 
 * [Set up your local minikube machine learning architecture](./set-up-minikube-cluster.md) shows how to set up and configure the infrastructure on your local minikube.
 * [Set up your production machine learning architecture](./set-up-open-source-production-mlops-architecture-aws.md) shows how to set up and configure the infrastructure in your AWS account.

tutorials/basic-usage-of-jupyter-mlflow-and-prefect.md

@@ -1,12 +1,12 @@
-# Creating a basic machine learning system on Open MLOps
+# Creating a basic machine learning system on OpenMLOps
 
-Now that you've [set up your Open MLOps](https://github.com/datarevenue-berlin/OpenMLOps/blob/master/tutorials/set-up-open-source-production-mlops-architecture-aws.md) architecture, you'll want to build something with it to see how all the pieces fit together. Let's start with a simple machine learning model to predict wine quality ratings, based on the properties of the wine. Specifically, we will
+Now that you've [set up your OpenMLOps](https://github.com/datarevenue-berlin/OpenMLOps/blob/master/tutorials/set-up-open-source-production-mlops-architecture-aws.md) architecture, you'll want to build something with it to see how all the pieces fit together. Let's start with a simple machine learning model to predict wine quality ratings, based on the properties of the wine. Specifically, we will
 
 * Use JupyterHub to write code to fetch the data, process it, and train a model
 * Create an experiment on MLFlow so that we can keep track of different models and parameters
 * Set up Prefect tasks to fetch the data periodically, retrain the model, and register the results with MLFlow
 
-In a later tutorial, we will extend this project to use some of the other services that make up Open MLOps.
+In a later tutorial, we will extend this project to use some of the other services that make up OpenMLOps.
 
 ## Accessing the services
 
@@ -207,10 +207,10 @@ def train_model(data, mlflow_experiment_id, alpha=0.5, l1_ratio=0.5):
 Next, in a new cell, set up some variables so we can talk to Prefect more easily. You'll need to enter the domain and S3 bucket name that you used when setting up the infrastruture, as well as the username and password that you used in the registration step at the start of this article.
 
 ```python
-domain = "mlops.ritza-route53.com"  # the domain where you are hosting Open MLOps
-username = "[email protected]"        # the username you used to register on Open MLOps
-password = "DkguP5GsB9yiPk8"        # the password you used to register on Open MLOps
-s3_bucket = "another-mlops-bucket"  # the S3 bucket you specified when setting up Open MLOps
+domain = "mlops.ritza-route53.com"  # the domain where you are hosting OpenMLOps
+username = "[email protected]"        # the username you used to register on OpenMLOps
+password = "DkguP5GsB9yiPk8"        # the password you used to register on OpenMLOps
+s3_bucket = "another-mlops-bucket"  # the S3 bucket you specified when setting up OpenMLOps
 
 prefect_project_name = "wine-quality-project"         # you can use what you want here
 docker_image = "drtools/prefect:wine-classifier-3"    # any docker image that has the required Python dependencies

tutorials/deploy-model-seldon.md

@@ -105,10 +105,10 @@ This is very similar to the code we used to train the model in the previous tuto
 In a third cell, add the following code, substituting the domain and credential variables for your own.
 
 ```python
-domain = "mlops.example.com"  # the domain where you are hosting Open MLOps
-username = "[email protected]"  # the username you used to register on Open MLOps
-password = "yourstrongpassword"  # the password you used to register on Open MLOps
-s3_bucket = "your-s3-bucket"  # the S3 bucket you specified when setting up Open MLOps
+domain = "mlops.example.com"  # the domain where you are hosting OpenMLOps
+username = "[email protected]"  # the username you used to register on OpenMLOps
+password = "yourstrongpassword"  # the password you used to register on OpenMLOps
+s3_bucket = "your-s3-bucket"  # the S3 bucket you specified when setting up OpenMLOps
 
 prefect_project_name = "wine-quality-project"  # you can use what you want here
 docker_image = "drtools/prefect:wine-classifier-3"  # any docker image that has the required Python dependencies

tutorials/install-dependencies.md

@@ -1,4 +1,4 @@
-To set up the Open MLOps architecture, you need several dependencies on your local or client machine. If you've used Docker, Terraform, and Kubectl before, you'll likely have everything you need. If not, below are instructions for installing all the client-side dependencies on a fresh install of Ubuntu 20.04.
+To set up the OpenMLOps architecture, you need several dependencies on your local or client machine. If you've used Docker, Terraform, and Kubectl before, you'll likely have everything you need. If not, below are instructions for installing all the client-side dependencies on a fresh install of Ubuntu 20.04.
 
 ## Install Git
 

tutorials/set-up-minikube-cluster.md

@@ -2,7 +2,7 @@
 
 ## Step 1: Cloning the repositories
 
-On your local machine, create a directory called `openmlops` and clone the following three Open MLOps repositories into that directory.
+On your local machine, create a directory called `openmlops` and clone the following three OpenMLOps repositories into that directory.
 
 ```
 git clone [email protected]:datarevenue-berlin/OpenMLOps.git
@@ -111,7 +111,7 @@ You should look for the `EXTERNAL_IP` and `PORT` of the `LoadBalancer` services
 * `http://[EXTERNAL_IP_OF_PREFECT_UI]:8080` to configure workflow and dataflows
 * `http://[EXTERNAL_IP_OF_MLFLOW]:5000` to see and track your experiments
 
-Next, take a look at [our tutorial](./basic-usage-of-jupyter-mlflow-and-prefect.md) on creating a basic production machine learning system using the Open MLOps architecture.
+Next, take a look at [our tutorial](./basic-usage-of-jupyter-mlflow-and-prefect.md) on creating a basic production machine learning system using the OpenMLOps architecture.
 
 ## Tearing down the cluster
 

tutorials/set-up-open-source-production-mlops-architecture-aws.md

@@ -10,7 +10,7 @@ We had the same problem. We spent a lot of time and solved it. Instead of solvin
 
 This tutorial guides you through all the scripts that will automatically set up the fully equipped MLOps infrastructure for you.
 
-You can clone our repositories, change a few lines of configuration, run the scripts in your own AWS account, and have a replica of our **Open MLOps Architecture** running in a couple of hours.
+You can clone our repositories, change a few lines of configuration, run the scripts in your own AWS account, and have a replica of our **OpenMLOps Architecture** running in a couple of hours.
 
 ## The result of following this guide
 
@@ -37,7 +37,7 @@ And a little bit of patience for everything to spin up.
 
 ## Step 1: Cloning the repositories
 
-On your local machine, create a directory called `openmlops` and clone the following three Open MLOps repositories into that directory.
+On your local machine, create a directory called `openmlops` and clone the following three OpenMLOps repositories into that directory.
 
 ```
 git clone [email protected]:datarevenue-berlin/OpenMLOps.git
@@ -181,5 +181,5 @@ The first step is to visit `https://mlops.example.com/profile/auth/registration`
 * `https://prefect.mlops.example.com` to configure workflow and dataflows
 * `https://mlflow.mlops.example.com` to see and track your experiments
 
-Next, take a look at [our tutorial](./basic-usage-of-jupyter-mlflow-and-prefect.md) on creating a basic production machine learning system using the Open MLOps architecture.
+Next, take a look at [our tutorial](./basic-usage-of-jupyter-mlflow-and-prefect.md) on creating a basic production machine learning system using the OpenMLOps architecture.