Did you know that the use of AI in many sectors of business has grown by 270% over the last four years? Not only has it grown exponentially recently, but the demand is only poised to keep growing in the future.
One reason for the heightened interest in the field was due to the pandemic bringing a greater focus on both machine learning and AI. In fact, an S&P Global Market Intelligence report found that 86% of organizations agreed that the pandemic has or will cause their organization to invest in new AI initiatives.
It makes sense then that the role of machine learning engineer was cited as the second most sought-after AI job, following only cloud architects.
So why is machine learning so important, and what does a machine learning engineer actually do? Let’s take a closer look at each.
Why Machine Learning is Important
First, let’s distinguish machine learning from AI: Machine learning is a part of AI, but not all AI consists of machine learning. Machine learning combines statistics and computer science to create mathematical models that automate different types of tasks, such as predicting whether an image contains a cat or a dog, or determining whether a line of credit can be approved for a particular person.
Leveraging data is the main fuel of machine learning techniques that make decisions based on predictive modeling. These models are adjusted using numerical optimization methods that find hidden patterns across a wide variety of datasets. These patterns can be useful for businesses to drive crucial decisions, improve operations, and/or launch new products.
In 2020, each person was generating 1.7 megabytes of data every second, which means that we’re drowning in a sea of data. This data can and should be used to enhance business insights, productivity, and growth. However, this will only happen if companies become competent at extracting value from this vast sea of information, and machine learning is key to navigating these waters successfully.
What Being a Machine Learning Engineer Entails
Before we get into the nitty gritty of what a machine learning (ML) engineer does, let’s first review what we touched on in a previous post: the difference between a data engineer, a data analyst, and a data scientist and how these roles fit in the greater data ecosystem:
- Data Engineers own the data realms; they build the systems that make the data usable across the organization.
- Data Analysts analyze the data using descriptive statistics to gather actionable insights and present those to stakeholders.
- Data Scientists build statistical models that allow companies to automate business processes and decisions, as well as deriving data-based insights.
Everyone wants to collect data to use it for better decision-making. Machine learning engineers simply slot between the input and output phases to make that happen. A single model built by a data scientist doesn’t contribute much in terms of optimizing processes but, with the help of a machine learning engineer, these models are prepared for use throughout an entire organization and its stakeholders, including customers.
In other words, if your business were a bakery and models were loaves of bread, then data scientists are the ones with the detailed bread recipe and machine learning engineers use the recipe to combine the ingredients, bake the bread, and deliver it to customers’ front doors to be enjoyed.
To accomplish this important mission, machine learning engineers need to be proficient in different areas so they can use models to generate meaningful business outcomes. Here are the main components of the machine learning engineer role:
- Data: They need to understand how data is accessed and recorded, in conjunction with data engineers’ work. They also need to perform special validations over the datasets they are using.
- Models: They need to help create the models that data scientists design, understand how these models are validated so that the organization can measure how much value they’re adding to the business, and know how to tune these models to make the most out of them when consumed by end users.
- Software Engineering: They need to be able to proficiently code backends that make models available to anyone in an easy-to-use API.
- DevOps: They need to be able to think ahead and anticipate how to scale infrastructure when thousands of users start to consume their models using APIs.
Caveats for Smaller Teams
Machine learning engineers can also double up as data engineers, data analysts, and data scientists at smaller companies that don’t have the resources for specialized roles. This is why ML engineers need to have a solid understanding and expertise in a variety of areas, as sometimes they need to jump in and support a different area of the data team or take on a role to stabilize processes.
Machine Learning in Action
As you can see, a machine learning engineer is an integral and crucial part of any company wanting to grow its data ecosystem and make tangible use of its data. To clarify the role further, here are some examples of machine learning in today’s world:
Netflix
Every time Netflix successfully suggests a movie that entirely suits your preference, it’s thanks to machine learning engineers deploying a recommendation engine that learns what you like and what you don’t, based on the movies you watch on your account.
Credit Cards
Thousands of people around the world who previously haven’t been able to access lines of credit now do, thanks to machine learning engineers building and deploying models that analyze tons of data to make credit evaluations and scoring more fair and accurate.
Uber
Whenever you order an Uber, machine learning engineers have been hard at work building algorithms to provide you with the best service. Thanks to machine learning, apps like Uber can determine where there are areas of high demand and what peak times are so they can provide vehicles and plan distribution accordingly.
How Factored Can Help
Factored can help both practicing ML engineers and ML engineering leaders stay on top of their game.
For Machine Learning Engineers
For ML engineers, Factored won’t just work with you, but will inspire you to bring your best self to the table—both technically and personally. We’re a team of high-performing engineers that doesn’t just get the job done, but strives to use the best practices and Service-oriented architecture (SoA) for machine learning. We’re always looking for the best way to do the work, collaborating on cutting-edge projects, leveraging the newest tools, and constantly improving our skills and knowledge.
For Machine Learning Engineering Leaders
If you’re looking to build out an entire data team, Factored can help set you up with expert machine learning engineers, data analysts, data scientists, and data engineers, so you can really make the most of your company’s data. We help CTOs, Heads of Engineering, and Heads of AI/Data Science not only garner a better understanding of how to structure their ML teams, but also understand exactly what they should expect of a ML engineer today. Additionally, we ensure our machine learning engineers aren’t only technically astute, but business savvy as well.
Are you ready to learn how Factored can help? Book a meeting to find out how our machine learning engineers can transform your team today.
