The majority of us who work in machine learning, analytics, and related disciplines do so for organizations with a variety of different structures and motives. These could be for for-profit corporations, not-for-profits, charities, or public sector organizations like the Government or Universities. In pretty much all of these cases, we do not do this work in a vacuum and not with an infinite budget of time or resources. It is important therefore that we consider some of the important aspects of doing this type of work in the real world.
The following is an extract from Andrew McMahon’s book, Machine Learning Engineering with Python, Second Edition.
First of all, the ultimate goal of your work is to generate value. This can be calculated and defined in a variety of ways, but fundamentally your work has to improve something for the company or their customers in a way that justifies the investment put in. This is why most companies will not be happy for you to take a year to play with new tools and then generate nothing concrete to show for it or to spend your days only reading the latest papers. Yes, these things are part of any job in technology, and they can definitely be super fun, but you have to be strategic about how you spend your time and always be aware of your value proposition.
Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. You will have to understand how the company works day to day, you will have to understand how the different pieces of the company fit together, and you will have to understand the people of the company and their roles. Most importantly, you have to understand the customer, both of the business and of your work. If you do not know the motivations, pains, and needs of the people you are building for, then how can you be expected to build the right thing?
Finally, and this may be controversial, the most important skill for you being a successful ML engineer in the real world is one that this book will not teach you, and that is the ability to communicate effectively. You will have to work in a team, with a manager, with the wider community and business, and, of course, with your customers, as mentioned above. If you can do this and you know the technology and techniques (many of which are discussed in this book), then what can stop you?
But what kind of problems can you solve with machine learning when you work in the real world? Well, let’s start with another potentially controversial statement: a lot of the time, machine learning is not the answer. This may seem strange given the title of this book, but it is just as important to know when not to apply machine learning as when to apply it. This will save you tons of expensive development time and resources.
Machine learning is ideal for cases when you want to do a semi-routine task faster, with more accuracy, or at a far larger scale than is possible with other solutions. Some typical examples are given in the following table, along with some discussion as to whether or not ML would be an appropriate tool for solving the problem:
Figure 1.1: Potential use cases for ML
As this table of simple examples hopefully starts to make clear, the cases where machine learning is the answer are ones that can usually be very well framed as a mathematical or statistical problem. After all, this is what machine learning really is; a series of algorithms rooted in mathematics that can iterate some internal parameters based on data. Where the lines start to blur in the modern world are through advances in areas such as deep learning or reinforcement learning, where problems that we previously thought would be very hard to phrase appropriately for standard ML algorithms can now be tackled.
The other tendency to watch out for in the real world (to go along with let’s use ML for everything) is the worry that people have that ML is coming for their job and should not be trusted. This is understandable: a report by PwC in 2018 suggested that 30% of UK jobs will be impacted by automation by the 2030s Will Robots Really Steal Our Jobs? What you have to try and make clear when working with your colleagues and customers is that what you are building is there to supplement and augment their capabilities, not to replace them.
Now that you understand some of the important points when using ML to solve business problems, let’s explore what these solutions can look like.
What does an ML solution look like?
When you think of machine learning engineering, you would be forgiven for defaulting to imagining working on voice assistance and visual recognition apps (I fell into this trap in previous pages, did you notice?). The power of ML, however, lies in the fact that wherever there is data and an appropriate problem, it can help and be integral to the solution.
Some examples might help make this clearer. When you type a text message and your phone suggests the next words, it can very often be using a natural language model under the hood. When you scroll any social media feed or watch a streaming service, recommendation algorithms are working double time. If you take a car journey and an app forecasts when you are likely to arrive at your destination, there is going to be some kind of regression at work. Your loan application often results in your characteristics and application details being passed through a classifier. These applications are not the ones shouted about on the news (perhaps with the exception of when they go horribly wrong), but they are all examples of brilliantly put-together machine learning engineering.
In this book, the examples we work through will be more like these; typical scenarios for machine learning encountered in products and businesses every day. These are solutions that, if you can build them confidently, will make you an asset to any organization.
We should start by considering the broad elements that should constitute any ML solution, as indicated in the following diagram:
Figure 1.2: Schematic of the general components or layers of any ML solution and what they are responsible for
Your storage layer constitutes the endpoint of the data engineering process and the beginning of the ML one. It includes your data for training, your results from running your models, your artifacts, and important metadata. We can also consider this as including your stored code. The compute layer is where the magic happens and where most of the focus of this book will be. It is where training, testing, prediction, and transformation all (mostly) happen. This book is all about making this layer as well-engineered as possible and interfacing with the other layers. You can blow this layer up to incorporate these pieces in the following workflow:
Figure 1.3: The key elements of the compute layer
The application layer is where you share your ML solution’s results with other systems. This could be through anything from application database insertion to API endpoints, to message queues, to visualization tools. This is the layer through which your customer eventually gets to use the results, so you must engineer your system to provide clean and understandable outputs.
Conclusion
In this, we have introduced the idea of machine learning engineering and how that fits within a modern team building valuable solutions based on data. There was a discussion of how the focus of machine learning engineering is complementary to the strengths of data science and data engineering and where these disciplines overlap.
About the Author
Andrew Peter (Andy) McMahon has spent years building high-impact ML products across a variety of industries. He is currently Head of MLOps for NatWest Group in the UK and has a PhD in theoretical condensed matter physics from Imperial College London. He is an active blogger, speaker, podcast guest, and leading voice in the MLOps community. He is co-host of the AI Right podcast and was named ‘Rising Star of the Year’ at the 2022 British Data Awards and ‘Data Scientist of the Year’ by the Data Science Foundation in 2019.
Ready to learn more about machine learning?
A lot goes into machine learning, and it can be hard to get started or pick out a niche. At the ODSC West 2023 machine learning track, you’ll have ample ways to learn about core machine learning skills, new approaches and frameworks, and unique ways of implementing ML algorithms in your organization or research. Here are some confirmed sessions:
- Neural Networks Make Stuff up. What Should We do About it?
- An Introduction to Data Labeling
- Data Science Applied to Manufacturing Problems
- Uncertainty Quantification: Approaches and Methods
- Machine Learning with XGBoost
- Idiomatic Pandas
- Data Morph: A Cautionary Tale of Summary Statistics
- Book Author: Hands-On Data Analysis with Pandas – Second Edition: A Python Data Science Handbook for Data Collection, Wrangling, Analysis, and Visualization
- Causal AI: from Data to Action
- Bridging the Interpretability Gap in Customer Segmentation
- Human Centered AI
- Representation Learning on Graphs and Networks
- Machine Learning Has Become Necromancy
- Anomaly Detection for CRM Production Data
- MLOps v LMOps – What’s Different?
- Battle Scars from the MLOps Trenches of the Robotaxi Industry
- Overview of Mojo🔥: Usability of Python, Performance of C
- Introduction to Math for Data Science
- Machine Learning for High-Risk Applications – Techniques for Responsible AI
- Missing Data: A Synthetic Data Approach for Missing Data Imputation
- Using Graphs for Large Feature Engineering Pipelines
- Statistic for Data Science
- Causality and LLMs
- No-Code and Low-Code AI: A Practical Project Driven Approach to ML
- Architecting Data: A Deep Dive Into the World of Synthetic Data
- Learn how to Efficiently Build and Operationalize Time Series Models in 2023
- Using Machine Learning to Discover Business Insights
- From Raw Data through Vectors to a Comprehensive Recommendation Model
- Building Robust and Scalable Recommendation Engines for Online Food Delivery
- PyTorch 2.1 – New Developments
