Machine learning engineers are often thought of as data scientists, but the two roles have different responsibilities.
We already touched on the difference between these roles in a previous article. This time, we want to give you a better idea of what machine learning engineers do and how they add value to your business.
The importance of machine learning engineers
The role of machine learning engineers is to use machine learning (ML) to add value to your business or product. But what does that mean in the real world?
In most cases, machine learning engineers create features for products or automate workflows. Their job may be to develop decision support systems or an entire decision automation system.
Responsibilities of machine learning engineers
What goes into building machine learning systems? In other words, what are the responsibilities of a machine learning engineer?
Continuing with our example, let’s examine what a machine learning engineer does to build and maintain a NEMT ride-along application.
Model building
The ML engineer begins by selecting and preparing the necessary data. The system needs to know the requested pickup time, the distance from the driver to the customer, the average speed of the vehicles on each road, the current weather conditions, and the traffic congestion along the route. A few more variables could be added.
Each variable becomes a feature. The attributes are the data points that the model uses to predict outcomes. The ML engineer analyzes past pickup records containing these variables to obtain this data.
After selecting and consolidating the correct data, the ML engineer removes any errors, fills in the missing records, and converts them to a consistent format. When the data is ready, the ML engineer selects the appropriate algorithm for the task at hand. Choosing the most efficient algorithm depends on the type of data, the expected prediction accuracy, and the resource intensity of the model.
For example, deep neural networks need to process images and videos with near-perfect accuracy. However, you will need to lease clusters of GPUs to train them, and you may need particular processors optimized for AI to run such models in production.
A standard decision tree may suffice if you don’t need that processing power. The ML engineer will experiment with several models and a subset of the data to determine which model best meets your business requirements.
Model training and deployment
Once a model has been selected for use, it must be trained. During training, the model will learn to make predictions by finding patterns in the training dataset. The ML engineer also needs a test set of historical data to determine if the model is making accurate predictions.
Once the model passes testing, the ML engineer prepares it for deployment.
Machine learning models are typically deployed as a microservice – an isolated container where code can function as a separate unit.
The applications give it some data, such as driver and customer geolocation, current vehicle speed, etc. We will also need data about traffic, delays, weather, and so on that comes from other sources.
From that point, the model consumes the necessary data, calculates the forecast, and sends it back to the users. But the ML engineer’s job continues.
Monitoring the model
Even though the model has been tested on historical data, the ML engineer needs to learn how it works with real-time data. Therefore, they need to monitor the model’s performance. Monitoring and evaluating the model is integral to a machine learning engineer’s job.
Monitoring systems tell ML engineers whether the model performs well or needs to be retrained. However, retraining does not mean the model is flawed or built incorrectly. It may be that it is using outdated information to make predictions.
Because real-world conditions are constantly changing, retraining often becomes a daily task for a machine learning engineer.
The skill set of a machine learning engineer
The machine learning engineer covers the entire machine learning part of your product, from analyzing raw data to training, deploying, and retraining the model. This means that they require exceptional experience and skill sets.
First and foremost, they must be well-versed in statistics, data analysis, and applied mathematics. They must also be familiar with existing machine learning algorithms and common architectures such as decision trees, deep learning networks, support vector machines, and Naive Bayes.
To train models, machine learning engineers should be familiar with standard tools, including:
- Python, the primary programming language
- Java and C++, two languages used to run models on big data servers
- Scikit-learn, a Python-based library with several machine-learning algorithms
- Tensor Flow, a free, open-source software library for machine learning and artificial intelligence with a focus on deep neural network learning and inference
- Hadoop, a distributed computing system
- Apache Spark, a data processing tool
- Nvidia Cuda, a GPU-based parallel computing platform for deep learning.
Machine learning engineers usually start with software development, although they may also start with data science and analytics. Either way, they have a background in computer science.
Machine learning engineers share some of the responsibilities of data scientists, but while data scientists focus more on analytics, machine learning engineers are more involved in production. For example, our NEMT company may hire a data analyst to explore new markets or expansion areas while their ML engineer focuses on improving product performance.
Similarly, ML engineers share responsibilities with data engineers. The difference is that the ML engineer defines the database specifications. In contrast, the data engineer uses those specifications to load data into the database and connect it to the model.
Do you need a machine learning engineer?
How do you know if you need to hire a machine learning engineer? Products that require machine learning are those that predict future conditions, offer product offers based on previous purchases (such as Amazon), or try to predict customer behavior and preferences in some other way (offer media based on prior videos watched or songs listened to, etc.).
If you still need to figure out your product development needs, we can help.
Contact Altezza Creative Solutions today!