Added subsection in documentation with suggested path to follow with examples

doc/source/workflow/README.md

@@ -1,37 +1,69 @@
 
 # Seldon Core Workflow
 
-There are 3 steps to using seldon-core.
+There are 4 steps to using seldon-core.
 
  1. Install seldon-core onto a Kubernetes cluster
  1. Wrap your components (usually runtime model servers) as Docker containers that respect the internal Seldon microservice API.
  1. Define your runtime service graph as a SeldonDeployment resource and deploy your model and serve predictions
+ 1. Deploy and request predictions
+
+At the end of this page you will find a set of suggested tutorials you can follow to get started with Seldon.
 
 ![steps](./steps.png)
 
-## Install Seldon Core
+## 1. Install Seldon Core
 
 To install seldon-core follow the [installation guide](install.md).
 
-## Wrap Your Model
+## 2. Wrap Your Model
 
 The components you want to run in production need to be wrapped as Docker containers that respect the [Seldon microservice API](../reference/apis/internal-api.md). You can create models that serve predictions, routers that decide on where requests go, such as A-B Tests, Combiners that combine responses and transformers that provide generic components that can transform requests and/or responses.
 
 To allow users to easily wrap machine learning components built using different languages and toolkits we provide wrappers that allow you easily to build a docker container from your code that can be run inside seldon-core. Our current recommended tool is RedHat's Source-to-Image. More detail can be found in [Wrapping your models docs](../wrappers/README.md).
 
-## Define Runtime Service Graph
+## 3. Define Runtime Service Graph
 
 To run your machine learning graph on Kubernetes you need to define how the components you created in the last step fit together to represent a service graph. This is defined inside a `SeldonDeployment` Kubernetes Custom resource. A [guide to constructing this inference graph is provided](../graph/inference-graph.md).
 
 ![graph](./graph.png)
 
-## Deploy and Serve Predictions
+## 4. Deploy and Serve Predictions
 
 You can use ```kubectl``` to deploy your ML service like any other Kubernetes resource. This is discussed [here](deploying.md). Once deployed ypu can get predictions by [calling the exposed API](serving.md).
 
 ## Next Steps
 
-Run a [notebook](../examples/helm_examples.html)  using Helm that illustrates using our Helm charts for launching various types of inference graphs.
+We have created a set of  [notebook step-by-step tutorials](../examples/helm_examples.html) which you can follow to get started with Seldon core.
+
+### Recommended starter tutorials
+
+* [Tensorflow Deep MNIST Tutorial](https://docs.seldon.io/projects/seldon-core/en/latest/examples/deep_mnist.html) (Try it also in [AWS](https://docs.seldon.io/projects/seldon-core/en/latest/examples/aws_eks_deep_mnist.html), [Azure](https://docs.seldon.io/projects/seldon-core/en/latest/examples/azure_aks_deep_mnist.html) and [GKE with GPU](https://github.com/SeldonIO/seldon-core/tree/master/examples/models/gpu_tensorflow_deep_mnist))
+* [SKlearn SpaCy Reddit Text Classification Tutorial](https://docs.seldon.io/projects/seldon-core/en/latest/examples/sklearn_spacy_text_classifier_example.html)
+* Deploy your R models with the [MNIST example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/r_mnist.html) and the [Iris example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/r_iris.html)
+* [Deploy your Java models with the H2O example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/h2o_mojo.html)
+
+### More complex deployments
+
+* [Example Seldon Core Deployments using Helm](https://docs.seldon.io/projects/seldon-core/en/latest/examples/helm_examples.html)
+* [Canary deployment with Seldon and Istio](https://docs.seldon.io/projects/seldon-core/en/latest/examples/istio_canary.html)
+* [Autoscaling Seldon Example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/autoscaling_example.html)
+* [Seldon Model with Custom Metrics](https://docs.seldon.io/projects/seldon-core/en/latest/examples/tmpl_model_with_metrics.html)
+
+### End-to-end / use-case tutorials
+
+* [End-to-end Reusable ML Pipeline with Seldon and Kubeflow](https://docs.seldon.io/projects/seldon-core/en/latest/examples/kubeflow_seldon_e2e_pipeline.html)
+* [Seldon Deployment of Income Classifier and Alibi Anchor Explainer](https://docs.seldon.io/projects/seldon-core/en/latest/examples/alibi_anchor_tabular.html)
+
+### Integration with other platforms
+
+* [Sagemaker (Seldon SKLearn integration example)](https://docs.seldon.io/projects/seldon-core/en/latest/examples/sagemaker_sklearn.html)
+* [Tensorflow Serving (TFServing) integration example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/tfserving_mnist.html)
+* [MLFlow integration example](https://docs.seldon.io/projects/seldon-core/en/latest/examples/mlflow.html)
+
+### Other sources
 
+* [Blogs](https://docs.seldon.io/projects/seldon-core/en/latest/tutorials/blogs.html)
+* [Videos](https://docs.seldon.io/projects/seldon-core/en/latest/tutorials/videos.html)