Updated documentation (#6)

Author: nfmoore

.github/docs/architecture-guide.md

@@ -1,6 +1,8 @@
 # Architecture Guide
 
-This example scenario demonstrates how to use Azure Databricks and Azure Kubernetes Service to develop an [ML Ops](https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment) platform for real-time model inference. This solution can manage the end-to-end machine learning life cycle and incorporates important [ML Ops](https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment) principles when developing, deploying, and monitoring machine learning models at scale.
+This repository illustrates an end-to-end proof-of-concept scenario that demonstrates how to use Azure Databricks and Azure Kubernetes Service to develop an [MLOps](https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment) platform for online inference workloads. This solution can manage the end-to-end machine learning life cycle and incorporates important [MLOps](https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment) principles when developing, deploying, and monitoring machine learning models at scale.
+
+This approach can easily be extended to address batch inference workloads and incorporate other useful services when managing APIs at scale such as [Azure API Management](https://docs.microsoft.com/en-us/azure/api-management/api-management-key-concepts).
 
 ## Potential use cases
 
@@ -12,20 +14,20 @@ This approach is best suited for:
 
 ## Architecture
 
+A holistic high-level architecture for an MLOps Platform based on the approach outlined in this repository is as follows.
+
 ![design](./images/architecture.png)
 
 At a high level, this solution design addresses each stage of the machine learning lifecycle:
 
-- Data Preparation: this includes sourcing, cleaning, and transforming the data for processing and analysis. Data can live in a data lake or data warehouse and be stored in a feature store after it's curated.
-- Model Development: this includes core components of the model development process such as experiment tracking and model registration using [MLflow](https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow/).
-- Model Deployment: this includes implementing a CI/CD pipeline to containerize machine learning models as API services. These services will be deployed to Azure Kubernetes clusters for end-users to consume.
-- Model Monitoring: this includes monitoring the API performance and model data drift by analyzing log telemetry with Azure Monitor.
+- **Data Preparation:** this includes sourcing, cleaning, and transforming the data for processing and analysis. Data can live in a data lake or data warehouse and be stored in a feature store after it's curated.
+- **Model Development:** this includes core components of the model development process such as experiment tracking and model registration using [MLFlow](https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow/).
+- **Model Deployment:** this includes implementing a CI/CD pipeline to containerize machine learning models as API services. These services will be deployed to Azure Kubernetes clusters for end-users to consume.
+- **Model Monitoring:** this includes monitoring the API performance and model data drift by analyzing log telemetry with Azure Monitor.
 
 > **NOTE:**
 >
->- When implementing a [CI/CD pipeline](https://docs.microsoft.com/en-us/azure/architecture/microservices/ci-cd) different tools such as Azure DevOps Pipelines or GitHub Actions can be used.
->- The services covered by this architecture are only a subset of a much larger family of Azure services.
->- Specific business requirements for your analytics use case could require the use of different services or features that are not considered in this design.
+> The proof-of-concept that is focused on in this repository and documented in the implementation guide only addresses online (or real-time) inference workloads depicted in the above high-level design. Batch inference workloads are not covered as part of this repository.
 
 ## Components
 
@@ -36,15 +38,21 @@ The following components are used as part of this design:
 - [Azure Container Registry](https://docs.microsoft.com/en-us/azure/container-registry/container-registry-intro): managed and private Docker registry service based on the open-source Docker.
 - [Azure Data Lake Gen 2](https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction): scalable solution optimized for storing massive amounts of unstructured data.
 - [Azure Monitor](https://docs.microsoft.com/en-us/azure/azure-monitor/overview): a comprehensive solution for collecting, analyzing, and acting on telemetry from your workloads.
-- [MLflow](https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow): open-source solution integrated within Databricks for managing the end-to-end machine learning life cycle.
+- [MLFlow](https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow): open-source solution integrated within Databricks for managing the end-to-end machine learning life cycle.
 - [Azure DevOps](https://azure.microsoft.com/solutions/devops/) or [GitHub](https://azure.microsoft.com/products/github/): solutions for implementing DevOps practices to enforce automation and compliance with your workload development and deployment pipelines.
 
+> **NOTE:**
+>
+>- When implementing a [CI/CD pipeline](https://docs.microsoft.com/en-us/azure/architecture/microservices/ci-cd) different tools such as Azure DevOps Pipelines or GitHub Actions can be used.
+>- The services covered in this design are only a subset of a much larger family of Azure services.
+>- Specific business requirements for your analytics use case could require the use of different services or features that are not considered in this design.
+
 ## Considerations
 
-Before implementing this solution some factors you might want to consider,  include:
+Before implementing this solution some factors you might want to consider, include:
 
 - This solution is designed for teams who require a high degree of customization and have extensive expertise deploying and managing Kubernetes workloads. If your data science team does not have this expertise consider deploying models to another service like [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning).
-- The [Machine Learning DevOps Guide](https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/ready/azure-best-practices/ai-machine-learning-mlops#machine-learning-devops-mlops-best-practices-with-azure-machine-learning) presents best practices and learnings on adopting ML operations (ML Ops) in the enterprise with Machine Learning.
+- The [Machine Learning DevOps Guide](https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/ready/azure-best-practices/ai-machine-learning-mlops#machine-learning-devops-mlops-best-practices-with-azure-machine-learning) presents best practices and learnings on adopting ML operations (MLOps) in the enterprise with Machine Learning.
 - Follow the recommendations and guidelines defined in the [Azure Well-Architected Framework](https://docs.microsoft.com/en-us/azure/architecture/framework) to improve the quality of your Azure solutions.
 - When implementing a [CI/CD pipeline](/azure/architecture/microservices/ci-cd) different tools such as Azure Pipelines or GitHub Actions can be used.
 - Specific business requirements for your analytics use case could require the use of different services or features that are not considered in this design.
@@ -55,9 +63,9 @@ All services deployed in this solution use a consumption-based pricing model. Th
 
 ## Deploy this scenario
 
-A proof-of-concept implementation of this scenario is available at the [ML Ops Platform using Databricks and Kubernetes](https://github.com/nfmoore/databricks-kubernetes-mlops-poc) repository. This sample illustrates:
+A proof-of-concept implementation of this scenario is available at the [MLOps Platform using Databricks and Kubernetes](https://github.com/nfmoore/databricks-kubernetes-mlops-poc) repository. This sample illustrates:
 
-- How an ML Flow model can be trained on Databricks.
+- How an MLFlow model can be trained on Databricks.
 - How to package models as a web service using open-source tools.
 - How to deploy to Kubernetes via CI/CD.
 - How to monitor API performance and model data drift.
@@ -69,3 +77,4 @@ You may also find these Architecture Center articles useful:
 - [Machine Learning Operations maturity model](https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model)
 - [Team Data Science Process for data scientists](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/overview)
 - [Modern analytics architecture with Azure Databricks](https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/azure-databricks-modern-analytics-architecture)
+- [Building A Clinical Data Drift Monitoring System With Azure DevOps, Azure Databricks, And MLflow](https://devblogs.microsoft.com/cse/2020/10/29/building-a-clinical-data-drift-monitoring-system-with-azure-devops-azure-databricks-and-mlflow/)