Addressing Eura's feedback.

Co-Authored-By: eurashin <[email protected]>

ops.qmd

@@ -33,7 +33,7 @@ With MLOps, there are protocols and tools in place to ensure that the model deve
 
 For the ridesharing company, implementing MLOps means their demand prediction model can be frequently retrained and deployed based on new incoming data. This keeps the model accurate despite changing rider behavior. MLOps also allows the company to experiment with new modeling techniques since models can be quickly tested and updated.
 
-Other MLOps benefits include enhanced model lineage tracking, reproducibility, and auditing. Cataloging ML workflows and standardizing artifacts enables deeper insight into model provenance. It also facilitates regulation compliance, which is especially critical in regulated industries like healthcare and finance.
+Other MLOps benefits include enhanced model lineage tracking, reproducibility, and auditing. Cataloging ML workflows and standardizing artifacts - such as logging model versions, tracking data lineage, and packaging models and parameters - enables deeper insight into model provenance. Standardizing these artifacts facilitates tracing a model back to its origins, replicating the model development process, and examining how a model version has changed over time. This also facilitates regulation compliance, which is especially critical in regulated industries like healthcare and finance where being able to audit and explain models is important.
 
 Major organizations adopt MLOps to boost productivity, increase collaboration, and accelerate ML outcomes. It provides the frameworks, tools, and best practices to manage ML systems throughout their lifecycle effectively. This results in better-performing models, faster time-to-value, and sustained competitive advantage. As we explore MLOps further, consider how implementing these practices can help address embedded ML challenges today and in the future.
 
@@ -43,38 +43,44 @@ MLOps has its roots in DevOps, which is a set of practices that combines softwar
 
 ### DevOps
 
-DevOps has its roots in the [Agile](https://agilemanifesto.org/) movement, which began in the early 2000s as a reaction to the limitations of traditional software development methodologies, such as the [Waterfall model](https://www.tutorialspoint.com/sdlc/sdlc_waterfall_model.htm). Agile emphasizes collaboration, customer feedback, and small, iterative releases, which are in stark contrast to the long, siloed development cycles and rigid structures of traditional methodologies. Agile provided the foundation for a more collaborative and responsive approach to software development.
+The term "DevOps" was first coined in 2009 by [Patrick Debois](https://www.jedi.be/), a consultant and Agile practitioner. Debois organized the first [DevOpsDays](https://www.devopsdays.org/) conference in Ghent, Belgium, in 2009, which brought together development and operations professionals to discuss ways to improve collaboration and automate processes. 
 
-As Agile methodologies became more popular, organizations realized the need for better collaboration and communication between development and operations teams. The siloed nature of development and operations teams often led to inefficiencies, conflicts, and delays in software delivery. This need for better collaboration and integration between development and operations teams led to the [DevOps](https://www.atlassian.com/devops) movement.
+DevOps has its roots in the [Agile](https://agilemanifesto.org/) movement, which began in the early 2000s. Agile provided the foundation for a more collaborative approach to software development and emphasized small, iterative releases. However, Agile primarily focused on collaboration between development teams. As Agile methodologies became more popular, organizations realized the need to extend this collaboration to operations teams as well. 
 
-The term "DevOps" was first coined in 2009 by [Patrick Debois](https://www.jedi.be/), a consultant and Agile practitioner. Debois organized the first [DevOpsDays](https://www.devopsdays.org/) conference in Ghent, Belgium, in 2009, which brought together development and operations professionals to discuss ways to improve collaboration and automate processes. The conference was a success, and the DevOps movement started to gain momentum.
+The siloed nature of development and operations teams often led to inefficiencies, conflicts, and delays in software delivery. This need for better collaboration and integration between these teams led to the [DevOps](https://www.atlassian.com/devops) movement. In a sense, DevOps can be seen as an extension of the Agile principles to include operations teams. 
 
-The key principles of DevOps include collaboration, automation, continuous integration and delivery, and feedback. These principles are aligned with the Agile methodology, which emphasizes collaboration, customer feedback, and iterative releases. DevOps extends the Agile principles to include operations teams and focuses on automating the entire software delivery pipeline, from development to deployment.
+The key principles of DevOps include collaboration, automation, continuous integration and delivery, and feedback. DevOps focuses on automating the entire software delivery pipeline, from development to deployment. It aims to improve the collaboration between development and operations teams, utilizing tools like [Jenkins](https://www.jenkins.io/), [Docker](https://www.docker.com/), and [Kubernetes](https://kubernetes.io/) to streamline the development lifecycle. 
 
-DevOps is now established as a set of practices and tools that aims to automate and integrate the processes between software development and IT teams to help build, test, and release software faster and more reliably. The main focus of DevOps is to improve the collaboration between development and operations teams, utilizing tools like [Jenkins](https://www.jenkins.io/), [Docker](https://www.docker.com/), and [Kubernetes](https://kubernetes.io/) to streamline the development lifecycle.
-Operations teams, automate software delivery and enhance the speed and quality of software releases.
+While Agile and DevOps share common principles around collaboration and feedback, DevOps specifically targets the integration of development and IT operations - expanding Agile beyond just development teams. It introduces practices and tools to automate software delivery and enhance the speed and quality of software releases.
 
 ### MLOps
 
-[MLOps](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning), on the other hand, stands for MLOps, and it extends the principles of DevOps to the ML lifecycle. MLOps aims to automate and streamline the end-to-end ML lifecycle, from data preparation and model development to deployment and monitoring. The main focus of MLOps is to facilitate collaboration between data scientists, data engineers, and IT operations, and to automate the deployment, monitoring, and management of ML models.
+[MLOps](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning), on the other hand, stands for MLOps, and it extends the principles of DevOps to the ML lifecycle. MLOps aims to automate and streamline the end-to-end ML lifecycle, from data preparation and model development to deployment and monitoring. The main focus of MLOps is to facilitate collaboration between data scientists, data engineers, and IT operations, and to automate the deployment, monitoring, and management of ML models. Some key factors led to the rise of MLOps.
 
-Some key factors led to the rise of MLOps:
+* **Data drift:** Data drift degrades model performance over time, motivating the need for rigorous monitoring and automated retraining procedures provided by MLOps.
+* **Reproducibility:** The lack of reproducibility in machine learning experiments motivated the need for MLOps systems to track code, data, and environment variables to enable reproducible ML workflows.
+* **Explainability:** The black box nature and lack of explainability of complex models motivated the need for MLOps capabilities to increase model transparency and explainability.
+* **Monitoring:** The inability to reliably monitor model performance post-deployment highlighted the need for MLOps solutions with robust model performance instrumentation and alerting.
+* **Friction:** The friction in manually retraining and deploying models motivated the need for MLOps systems that automate machine learning deployment pipelines.
+* **Optimization:** The complexity of configuring infrastructure for machine learning motivated the need for MLOps platforms with optimized, ready-made ML infrastructure.
 
-* Data drift causing models to degrade in production
-* Difficulty reproducing and explaining model behavior
-* Lack of visibility into model performance post-deployment
-* Painful retraining and deployment procedures
-* Infrastructure misconfigured for ML
+While both DevOps and MLOps share the common goal of automating and streamlining processes, they differ in their focus and challenges. DevOps primarily deals with the challenges of software development and IT operations. In contrast, MLOps deals with the additional complexities of managing ML models, such as [data versioning](https://dvc.org/), [model versioning](https://dvc.org/), and [model monitoring](https://www.fiddler.ai/). MLOps also requires collaboration between various stakeholders, including data scientists, data engineers, and IT operations.
 
-While both DevOps and MLOps share the common goal of automating and streamlining processes, they differ in their focus and challenges. DevOps primarily deals with the challenges of software development and IT operations. In contrast, MLOps deals with the additional complexities of managing ML models, such as [data versioning](https://dvc.org/), [model versioning](https://dvc.org/), and [model monitoring](https://www.fiddler.ai/).
+While DevOps and MLOps share similarities in their goals and principles, they differ in their focus and challenges. DevOps focuses on improving the collaboration between development and operations teams and automating software delivery. In contrast, MLOps focuses on streamlining and automating the ML lifecycle and facilitating collaboration between data scientists, data engineers, and IT operations.
 
-MLOps also requires collaboration between various stakeholders, including data scientists, data engineers, and IT operations.
+Here is a table that summarizes them side by side.
 
-While DevOps and MLOps share similarities in their goals and principles, they differ in their focus and challenges. DevOps focuses on improving the collaboration between development and operations teams and automating software delivery. In contrast, MLOps focuses on streamlining and automating the ML lifecycle and facilitating collaboration between data scientists, data engineers, and IT operations.
+| Aspect               | DevOps                           | MLOps                                |
+|----------------------|----------------------------------|--------------------------------------|
+| **Objective**        | Streamlining software development and operations processes | Optimizing the lifecycle of machine learning models |
+| **Methodology**      | Continuous Integration and Continuous Delivery (CI/CD) for software development | Similar to CI/CD but focuses on machine learning workflows |
+| **Primary Tools**    | Version control (Git), CI/CD tools (Jenkins, Travis CI), Configuration management (Ansible, Puppet) | Data versioning tools, Model training and deployment tools, CI/CD pipelines tailored for ML |
+| **Primary Concerns** | Code integration, Testing, Release management, Automation, Infrastructure as code | Data management, Model versioning, Experiment tracking, Model deployment, Scalability of ML workflows |
+| **Typical Outcomes** | Faster and more reliable software releases, Improved collaboration between development and operations teams | Efficient management and deployment of machine learning models, Enhanced collaboration between data scientists and engineers |
 
 ## Key Components of MLOps
 
-In this chapter, we will provide an overview of the core components of MLOps, an emerging set of practices that enables robust delivery and lifecycle management of ML models in production. While some MLOps elements like automation and monitoring were covered in previous chapters, we will integrate them into an integrated framework and expand on additional capabilities like governance. By the end, we hope that you will understand the end-to-end MLOps methodology that takes models from ideation to sustainable value creation within organizations.
+In this chapter, we will provide an overview of the core components of MLOps, an emerging set of practices that enables robust delivery and lifecycle management of ML models in production. While some MLOps elements like automation and monitoring were covered in previous chapters, we will integrate them into an integrated framework and expand on additional capabilities like governance. Additionally, we will describe and link to popular tools used within each component, such as [LabelStudio](https://labelstud.io/) for data labeling. By the end, we hope that you will understand the end-to-end MLOps methodology that takes models from ideation to sustainable value creation within organizations.
 
 ### Data Management