Introduction to Machine Learning Systems Design 
Now that we’ve walked through an overview of ML systems in the real world, we can get to the fun part of actually designing an ML system. To reiterate from the first chapter, ML systems design takes a system approach to MLOps, which means that we’ll consider an ML system holistically to ensure that all the components—the business requirements, the data stack, infrastructure, deployment, monitoring, etc.— and their stakeholders can work together to satisfy the specified objectives and requirements

We’ll start the chapter with a discussion on objectives. Before we develop an ML system, we must understand why this system is needed. If this system is built for a business, it must be driven by business objectives, which will need to be translated into ML objectives to guide the development of ML models

Once everyone is on board with the objectives for our ML system, we’ll need to set out some requirements to guide the development of this system. In this book, we’ll consider the four requirements: reliability, scalability, maintainability, and adaptabil‐ ity. We will then introduce the iterative process for designing systems to meet those requirements. 

You might wonder: with all these objectives, requirements, and processes in place, can I finally start building my ML model yet? Not so soon! Before using ML algo‐ rithms to solve your problem, you first need to frame your problem into a task that ML can solve. We’ll continue this chapter with how to frame your ML problems. The difficulty of your job can change significantly depending on how you frame your problem.

Because ML is a data-driven approach, a book on ML systems design will be amiss if it fails to discuss the importance of data in ML systems. The last part of this chapter touches on a debate that has consumed much of the ML literature in recent years: which is more important—data or intelligent algorithms? Let’s get started!

Business and ML Objectives We first need to consider the objectives of the proposed ML projects. When working on an ML project, data scientists tend to care about the ML objectives: the metrics they can measure about the performance of their ML models such as accuracy, F1 score, inference latency, etc. They get excited about improving their model’s accuracy from 94% to 94.2% and might spend a ton of resources—data, compute, and engi‐ neering time—to achieve that.

But the truth is: most companies don’t care about the fancy ML metrics. They don’t care about increasing a model’s accuracy from 94% to 94.2% unless it moves some business metrics. A pattern I see in many short-lived ML projects is that the data scientists become too focused on hacking ML metrics without paying attention to business metrics. Their managers, however, only care about business metrics and, after failing to see how an ML project can help push their business metrics, kill the projects prematurely (and possibly let go of the data science team involved).

*****************

So what metrics do companies care about? While most companies want to convince you otherwise, the sole purpose of businesses, according to the Nobel-winning econ‐ omist Milton Friedman, is to maximize profits for shareholders.2
—> most companies want to convince you  otherwise , the sole purpose of businesses , according to the Nobel-winning econ - omist milton Friedman , is to maximize profits for shareholders 

Imagine that you work for an ecommerce site that cares about purchase-through rate and you want to move your recommender system from batch prediction to online prediction.3 You might reason that online prediction will enable recommendations more relevant to users right now, which can lead to a higher purchase-through rate. You can even do an experiment to show that online prediction can improve your recommender system’s predictive accuracy by X% and, historically on your site, each percent increase in the recommender system’s predictive accuracy led to a certain increase in purchase-through rate.

One of the reasons why predicting ad click-through rates and fraud detection are among the most popular use cases for ML today is that it’s easy to map ML models’ performance to business metrics: every increase in click-through rate results in actual ad revenue, and every fraudulent transaction stopped results in actual money saved

Q- can i build a microservice of this e-commerce using click-through and Fraud detection as separate microservice from scratch yes or non ? 
If yes give me the full system looklike ?