diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0aa5c2f..479662f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -19,4 +19,5 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - [Feature] Simple feedforward neural network with MNIST dataset to map input images to their corresponding digit classes 
 - [Feature] CNN architecture training considering COCO dataset for image classification AI applications (**NOTE:** Compute and storage intensive. Read `Download the COCO dataset images` comments on preferred hardware specs)
 - [Feature] CD workflow for on-demand Azure Container Registry deployments in order to store internal Docker images.
-- [Feature] Dockerizing Python (pytorch or tensorflow) applications for ML training and inference
\ No newline at end of file
+- [Feature] Dockerizing Python (pytorch or tensorflow) applications for ML training and inference
+- [Feature] Installation of the [Training Operator for CRDs](https://github.com/kubeflow/training-operator) and applying sample [TFJob and PyTorchJob](https://www.kubeflow.org/docs/components/training/overview/) k8s manifest
\ No newline at end of file
diff --git a/README.md b/README.md
index e2f27f5..122c490 100644
--- a/README.md
+++ b/README.md
@@ -24,17 +24,18 @@ Repository showcasing ML Ops practices with kubeflow and mlflow
 - [x] CD workflow for on-demand AKS deployments and kubeflow operator or mlflow helm chart installations
 - [x] CD wofklow for on demand deployments of an Azure Storage Account Container **(For storing terraform state files)**
 - [x] CD workflow for on-demand Azure Container Registry deployments in order to store internal Docker images.
-- [ ] CI workflow for building internal docker images and uploading those to an Azure Container Resgitry
-- [ ] CD workflows for internal helm chart installations in deployed AKS clusters
+- [ ] ~~CI workflow for building internal docker images and uploading those to an Azure Container Resgitry~~
+- [ ] ~~CD workflows for internal helm chart installations in deployed AKS clusters~~
 - [x] Added `devcontainer.json` with necessary tooling for local development
 - [x] Python (pytorch or tensorflow) application for ML training and inference purposes and Jupyter notebooks
     - [x] Simple feedforward neural network with MNIST dataset to map input images to their corresponding digit classes 
     - [x] CNN architecture training and inference considering COCO dataset for image classification AI applications (**NOTE:** Compute and storage intensive. Read `Download the COCO dataset images` comments on preferred hardware specs)
     - [ ] ~~(**OPTIONAL**) Transformer architecture training considering pre-trained models for chatbot AI applications~~
 - [x] Dockerizing Python (pytorch or tensorflow) applications for ML training and inference
-- [ ] Helm charts with K8s manifests for ML jobs considering the [Training Operator for CRDs](https://github.com/kubeflow/training-operator)
-- [ ] Demonstration of model training and model deployment trough automation workflows
-- [ ] (**OPTIONAL**) mlflow experiments for the machine learning lifecycle
+- [ ] ~~Helm charts with K8s manifests for ML jobs considering the [Training Operator for CRDs](https://github.com/kubeflow/training-operator)~~
+- [x] Installation of the [Training Operator for CRDs](https://github.com/kubeflow/training-operator) and applying sample [TFJob and PyTorchJob](https://www.kubeflow.org/docs/components/training/overview/) k8s manifest
+- [x] Demonstration of model training and model deployment trough automation workflows~~
+- [ ] ~~(**OPTIONAL**) mlflow experiments for the machine learning lifecycle
 
 ## Getting started
 
@@ -72,7 +73,7 @@ and visit in a browser of choice `localhost:8080`.
 
 ![kubeflow-dashboard](./images/kubeflow-dashboard.PNG)
 
-#### CNN architecture training considering pre-trained models for image classification AI applications
+#### Jupyter notebooks
 
 When creating the Jupyter notebook instance consider the following data volume:
 
@@ -102,6 +103,10 @@ Execute a [Jupyter notebook](./notebooks/) to either train the model or perform
 
 ![Run jupyter notebook example](./images/run-jupyter-notebook-example.PNG)
 
+#### Applying TFJob or PyTorchJob k8s manifests 
+
+After successful installation of the Kubeflow Training Operator, apply some sample k8s ML training jobs, e.g. [for PyTorch](https://www.kubeflow.org/docs/components/training/user-guides/pytorch/) and [for Tensorflow](https://www.kubeflow.org/docs/components/training/user-guides/tensorflow/).
+
 ### mlflow
 
 To access the MLflow dashboard following the installation of the MLflow Helm chart, execute the following command: