Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.
Table of Contents (17 chapters)
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

ML life cycle

One of the first ML projects that I worked on was a sport predictive analytics problem for a major sports league brand. I was given a list of predictive analytics outcomes to think about to see if there were ML solutions for the problems. I was a casual viewer of the sports; I didn't know anything about the analytics to be generated, nor the rules of the games in detail. I was given some sample data, but I had no idea what to do with it.

The first thing I started to work on was to learn the sport. I studied things like how the games were played, the different player positions, and how to determine and identify certain events. Only after acquiring the relevant domain knowledge did the data start to make sense to me. I then discussed the impact of the different analytics outcomes with the stakeholders and assessed the modeling feasibility based on the data we had. We came up with a couple of top ML analytics with the most business impact to work on, decided how they would be integrated into the existing business workflow, and how they would be measured on their impacts.

I then started to inspect and explore the data in closer detail to understand what information was available and what was missing. I processed and prepared the dataset based on a couple of ML algorithms I was thinking about using and carried out different experiments. I did not have a tool to track the different experiment results, so I had to track what I have done manually. After some initial rounds of experimentation, I felt the existing data was not enough to train a high-performance model, and I needed to build a custom deep learning model to incorporate data of different modalities. The data owner was able to provide additional datasets I looked for, and after more experiments with custom algorithms and significant data preparations and feature engineering, I was able to train a model that met the business needs.

After that, the hard part came – to deploy and operationalize the model in production and integrate it into the existing business workflow and system architecture. We went through many architecture and engineering discussions and eventually built out a deployment architecture for the model.

As you can see from my personal experience, there are many different steps in taking a business idea or expected business outcome from ideation to production deployment. Now, let's formally review a typical life cycle of an ML project. A formal ML life cycle includes steps such as business understanding, data acquisition and understanding, data preparation, model building, model evaluation, and model deployment. Since a big component of the life cycle is experimentation with different datasets, features, and algorithms, the whole process can be highly iterative. In addition, there is no guarantee that a working model can be created at the end of the process. Factors such as the availability and quality of data, feature engineering techniques (the process of using domain knowledge to extract useful features from raw data), and the capability of the learning algorithms, among others, can all prevent a successful outcome.

The following figure shows the key steps in ML projects:

Figure 1.6 – ML life cycle

Figure 1.6 – ML life cycle

In the next few sections, we will discuss each of these steps in greater detail.

Business understanding and ML problem framing

The first step in the life cycle is the business understanding step. In this step, you would need to develop a clear understanding of the business goals and define the business performance metrics that can be used to measure the success of the ML project. The following are some examples of business goals:

  • Cost reduction for operational processes, such as document processing.
  • Mitigation of business or operational risks, such as fraud and compliance.
  • Product or service revenue improvements, such as better target marketing, new insight generation for better decision making, and increased customer satisfaction

Specific examples of business metrics for measurement could be the number of hours reduced in a business process, an increased number of true positive fraud instances detected, a conversion rate improvement from target marketing, or the extent of churn rate reductions. This is a very important step to get right to ensure there is sufficient justification for an ML project and that the outcome of the project can be successfully measured.

After the business goals and business metrics are defined, you then need to determine if the business problem can be solved using an ML solution. While ML has a wide scope of applications, it does not mean it can solve all business problems.

Data understanding and data preparation

There is a saying that data is the new oil, and this is especially true for ML. Without the required data, you cannot move forward with an ML project. That's why the next step in the ML life cycle is data acquisition, understanding, and preparation.

Based on the business problems and ML approach, you will need to gather and understand the available data to determine if you have the right data and data volume to solve the ML problem. For example, suppose the business problem to address is credit card fraud detection. In that case, you will need datasets such as historical credit card transaction data, customer demographics, account data, device usage data, and networking access data. Detailed data analysis is then needed to determine if the dataset features and quality are sufficient for the modeling tasks. You also need to decide if the data needs labeling, such as fraud or not-fraud. During this step, depending on the data quality, a significant amount of data wrangling might be performed to prepare and clean the data and to generate the dataset for model training and model evaluation.

Model training and evaluation

Using the training and validation datasets created, a data scientist will need to run a number of experiments using different ML algorithms and dataset features for feature selection and model development. This is a highly iterative process and could require a large number of data processing and model development runs to find the right algorithm and dataset combination for optimal model performance. In addition to model performance, you might also need to consider data bias and model explainability to meet regulatory requirements.

After the model is trained and before it is deployed into production, the model quality needs to be validated using the relevant technical metrics, such as the accuracy score. This is usually done using a holdout dataset, also known as a test dataset, to gauge how the model performs on unseen data. It is very important to understand what metrics to use for model validation, as it varies depending on the ML problems and the dataset used. For example, model accuracy would be a good validation metric for a document classification use case if the number of document types is relatively balanced. Model accuracy will not be a good metric to evaluate the model performance for a fraud detection use case – this is because if the number of frauds is small and the model predicts not-fraud all the time, the model accuracy could still be very high.

Model deployment

Once the model is fully trained and validated to meet the expected performance metric, it can be deployed into production and the business workflow. There are two main deployment concepts here. The first is the deployment of the model itself to be used by a client application to generate predictions. The second concept is to integrate this prediction workflow into a business workflow application. For example, deploying the credit fraud model would either host the model behind an API for real-time prediction or as a package that can be loaded dynamically to support batch predictions. Additionally, this prediction workflow also needs to be integrated into business workflow applications for fraud detection that might include the fraud detection of real-time transactions, decision automation based on prediction output, and fraud detection analytics for detailed fraud analysis.

Model monitoring

Model deployment is not the end of the ML life cycle. Unlike software, whose behavior is highly deterministic since developers explicitly code its logic, an ML model could behave differently in production from its behavior in model training and validation. This could be caused by changes in the production data characteristics, data distribution, or the potential manipulation of request data. Therefore, model monitoring is an important post-deployment step for detecting model drift or data drift.

Business metric tracking

The actual business impact should be tracked and measured as an ongoing process to ensure the model delivers the expected business benefits by comparing the business metrics before and after the model deployment, or A/B testing where a business metric is compared between workflows with or without the ML model. If the model does not deliver the expected benefits, it should be re-evaluated for improvement opportunities. This could also mean framing the business problem as a different ML problem. For example, if churn prediction does not help improve customer satisfaction, then consider a personalized product/service offering to solve the problem.

Now that we have talked about what is involved in an end-to-end ML life cycle, let's look at the ML challenges in the next section.