Understanding ML Engineering
A machine learning engineer (ML engineer) is a programmer who designs and builds software that can automate artificial intelligence and machine learning (AI/ML) models.
ML engineers build large-scale systems that take in massive data sets and use them to train algorithms that can learn cognitive tasks and generate useful insights and predictions. These systems are then deployed to production where they can serve real users - this is known as the inference stage.
Machine learning engineers manage the entire data science pipeline, including sourcing and preparing data, building and training models, and deploying models to production.
ML engineers typically work within a data science team, collaborating with data scientists, data analysts, IT experts, DevOps experts, software developers, and data engineers.
This is part of an extensive series of guides about AI Technology.
In this article, you will learn:
- Machine Learning Engineer Roles and Responsibilities
- Skills Required to Become a Machine Learning Engineer
- Machine Learning Engineer Salary
- 5 Reasons To Become a Machine Learning Engineer
- What Makes a Successful Machine Learning Engineer?
- Machine Learning Engineer vs Data Scientist
- What is ML Engineering?
- 5 Phases of Machine Learning Engineering
- Machine Learning Automation
- Prioritization of Machine Learning Projects
Machine Learning Engineer Roles and Responsibilities
Machine learning engineers have two key roles: feeding data into machine learning models, and deploying these models in production.
Data ingestion and preparation is a complex task. The data might come from a variety of sources, often streaming in real time. It needs to be automatically processed, cleaned and prepared to suit the data format and other requirements of the model.
Deployment involves taking a prototype model in a development environment and scaling it out to serve real users. This may require running the model on more powerful hardware, enabling access to it via APIs, and allowing for updates and re-training of the model using new data.
In order to achieve these and related tasks, machine learning engineers perform the following activities in an organization:
- Analyze big datasets and then determine the best method to prepare the data for analysis.
- Ingest source data into machine learning systems to enable machine learning training.
- Collaborate with other data scientists and build effective data pipelines.
- Build the infrastructure required to deploy a machine learning model in production.
- Manage, maintain, scale, and improve machine learning models already running in production environments.
- Work with common ML algorithms and relevant software libraries.
- Optimize and tweak ML models according to how they behave in production.
- Communicate with relevant stakeholders and key users to understand business requirements, and also clearly explain the capabilities of the ML model.
- Deploy models to production, initially as a prototype, and then as an API that can serve predictions for end users.
- Provide technical support to data and product teams, helping relevant parties use and understand machine learning systems and datasets.
Related content: read our guide to machine learning infrastructure
What are the Skills Required to Become a Machine Learning or Deep Learning Engineer?
Here are some of the essential skills required from machine learning engineers:
- Linux/Unix—ML engineers working with clustered data and servers typically use Linux or other variants of Unix, and need good command of the operating system
- Java, C, C++—these programming languages are commonly used by ML engineers to parse and prepare data for machine learning algorithms
- GPUs and CUDA programming – large scale machine learning models use graphical processing units (GPUs) to accelerate workloads. CUDA is the most common programming interface used by GPUs, with strong support by GPU hardware and deep learning frameworks. CUDA is an essential skill for a machine learning engineer.
- Applied mathematics—machine learning experts must have strong math skills. Some important mathematical concepts are linear algebra, probability, statistics, multivariate computation, tensors and matrix multiplication, algorithms and optimization.
- Data modeling and evaluation—ML engineers must be proficient at evaluating large amounts of data, planning how to effectively model it, and testing how the final system behaves.
- Neural network architecture—a set of algorithms used to learn and perform complex cognitive tasks. It uses a network of virtual neurons, mimicking the human brain.
- Natural Language Processing (NLP)—allows machines to perform linguistic tasks with similar performance to humans. Common tools and technologies include Word2vec, recurrent neural networks (RNN), gensim, and Natural Language Toolkit (NLTK).
- Reinforcement Learning—a set of algorithms that enable machines to learn complex tasks from repeated experience.
- Distributed computing—ML engineers need to master distributed computing, both on-premises and in the cloud, to deal with large amounts of data and distributed computations.
- Spark and Hadoop—these technologies are commonly used for processing large-scale data sets in preparation for machine learning jobs.
Machine Learning Engineer Salary
The machine learning engineer role is new, and there is limited data about the salary range. However, because it is closely related to several well known roles, it is possible to estimate the salary based on these related roles. The following is the US national median salary for 2022 based on data from Robert Half:
5 Reasons To Become a Machine Learning Engineer
Here are a few reasons you should consider a career in machine learning engineering.
- Attractive salaries – according to Indeed, the average annual salary for a Machine Learning Engineer in the US is over $130,000, while in leading companies like Twitter, eBay, and Airbnb the average salary is well over $200,000.
- Demand is high – data is the fuel of the digital economy, meaning that demand for machine learning roles is high and growing. All indicators show that machine learning and AI will become more important in the future job market.
- Continuous learning – machine learning is new, with many technologies, tools, and algorithms being introduced. A machine learning role is an opportunity to learn, and also to innovate and develop the new generation of ML technology. It is very common in the field to continue education while working, in the form of in-person courses, digital workshops, webinars and podcasts.
- Cutting-edge technology – a machine learning engineering role is an opportunity to work with cutting edge technology that is driving the latest global innovations, such as self-driving cars, conversational AI, automated cybersecurity, and smart city technology.
- A role with variety – machine learning engineering roles are very diverse. You might find yourself working in any one of a range of industries, transitioning between multiple approaches and tools, and discovering new approaches and algorithms on a regular basis.
What Makes a Successful Machine Learning Engineer?
As you start on your machine learning engineering job path, here are a few things that will make you successful at this role.
- Solid programming skills – machine learning engineering is founded on software development skills. Become proficient at languages like Python (used in machine learning frameworks and data science), C++ (used in embedded applications), and Java (used in large enterprise applications). Also learn machine learning specific languages like R and Prolog.
- Strong mathematical foundation – machine learning is strongly focused on mathematics. To be a successful machine learning engineer you will need either academic training in mathematics and statistics, or at least advanced high school training. Keep in mind that many ML algorithms are extensions of traditional statistical techniques.
- Creativity and problem solving – machine learning is a new field and you’ll need to be creative to find solutions to problems encountered by your organization. Successful machine learning engineers identify systematic issues and find generalized solutions, rather than hunting down bugs one by one.
- Understanding of iterative processes – machine learning is driven by trial and error. Most models will initially not work, and will achieve good results through experimentation and fine tuning. You’ll need to develop determination, and be willing to try something multiple times until you find the right approach. At the same time, you’ll need to be flexible and learn when to walk away from a problem when it cannot be solved efficiently.
- Develop intuitions – machine learning is not a deterministic field, and the best machine learning engineers have intuitions about data and models. They can review a large data set, identify patterns, and have a feeling about which algorithm might be right to approach the data.
- Data management expertise – machine learning is a lot about managing large, messy datasets. Machine learning algorithms rely on data, and a lot of it, to train and achieve accurate predictions. As a machine learning engineer you will need to be proficient at data exploration tools like Excel, Tableau, and Microsoft Power BI, and learn to build a solid data pipeline that can feed your models.
Machine Learning Engineer vs Data Scientist
Machine learning engineers and data scientists, while they work in the same team towards a shared goal, have different roles and responsibilities.
How are the Two Roles Different?
Machine learning engineers build software systems and develop algorithms that can be used to generate business insights. Their main responsibility is to create AI tools and infrastructure enabling machine learning in production and at scale.
Data scientists are responsible for collecting data, analyzing it, and using machine learning algorithms to transform it into a usable form. They identify patterns in data that can help a business make better decisions, or can directly provide value to users.
So while machine learning engineers are mainly responsible for the “how” of machine learning, facilitating machine learning at scale, data scientists are responsible for the “what”, using the infrastructure to create an impact for the business.
How are they Similar?
While their responsibilities are different, machine learning engineers and data scientists have many of the same skills. Both positions require a good understanding of programming languages such as Python and R, a solid understanding of big data analytics, statistical data, and predictive models, and the ability to operate deep learning frameworks, clustered big data systems, and GPU hardware.
Both roles need to collaborate intensively with others. Dealing with large data sets is a problem that can span the entire organization, including IT, development teams, and business units. Both roles are also required to deliver their findings and make their work usable to others. Machine learning engineers create infrastructure and models that must be usable for day-to-day business problems, while data scientists create visualizations and dashboards for wide use.
Up until now we covered what is involved in becoming a machine learning engineer. Now let’s dive into the field itself: what is machine learning engineering and how it works.
What is ML Engineering?
Machine learning engineering (MLE) involves the use of various skills and technologies—including machine learning techniques, tools, and principles, and software engineering—for the purpose of designing and building complex computing systems.
MLE covers the entire data science pipeline, including data collection, training models, and releasing the model in production. A machine learning engineer is responsible for the entire process and may perform several tasks.
This article will explain five phases of the machine learning engineering process, and help you understand how MLE will fit into your organization – roles and responsibilities, prioritization of projects, and how machine learning operations and automation is transforming the field.
5 Phases of Machine Learning Engineering
Here are the main stages in a machine learning pipeline, and the machine learning engineering activities involved in each one.
Data Collection and Preparation
A machine learning model requires massive amounts of data, which helps the model learn how to perform its purpose. Before it can be used, big data needs to be collected and usually also prepared.
Data collection is the process of aggregating data from multiple sources. The data you collect needs to be sizable, accessible, understandable, reliable, and usable.
Data preparation, or data preprocessing, is the process of transforming raw data into usable information.
There are several challenges you might encounter when handling data. For example, high costs, bias, and low predictive power.
In general, good data has consistent labels and can reflect the real inputs the model is expected to work with in production. If you are using interaction data, you also need to make sure it comes with context, including the action and outcome of the interaction.
Feature engineering is the process of conceptually and programmatically transforming your raw example into a feature vector.
You first need to conceptualize the feature and then write a code that can transform your raw example into a feature. After creating several features, you need to scale and store them and document all features in feature stores or schema files. Additionally, you should make sure that all code, models, and training data are in sync.
Supervised Model Training
The next step in the process is training your ML model. There are several techniques you can use, including supervised and unsupervised learning.
Supervised learning involves the use of labeled datasets to train your model to classify data and predict outcomes, whereas unsupervised learning involves the use of unlabeled data.
The modeling process requires the use of algorithms. You can use your own algorithm or choose the relevant algorithms from an open source library like scikit-learn. Once you choose an algorithm, you can start testing different combinations of hyperparameters.
It is critical to evaluate a machine learning model before and after running in production. You can evaluate a model offline, after the training phase is complete. Offline evaluation is based on historical data. Alternatively, you can leverage online model evaluation to test and compare models running in production.
Ideally, model evaluation should be performed on a continuous basis. This process should help you gain several insights, including:
- Evaluating model performance before deploying in production
- Estimating the possible legal risks of deploying in production
- Monitoring the performance of the model after it is running in a production environment
Here are several model deployment options:
- Static deployment—enables you to maintain user privacy, achieve quick execution, and call the model while users are offline. However, upgrading the model usually requires upgrading the entire application.
- Dynamic deployment on user devices—enables the model to quickly answer device-based user calls. However, delivering updates to users can be complex. It is also difficult to monitor the model when it is deployed on user devices.
- Dynamic deployment on a server—enables you to deploy on a virtual machine (VM), in a container, or leverage serverless services.
- Model streaming—lets you register all models in a stream-processing engine. Alternatively, you can package the model as an application, which is based on a stream-processing library.
Related content: read our guide to machine learning workflow
Machine Learning Automation
Automation of machine learning processes is the next step forward for many data science organizations. Machine learning engineers play a key role in these automation efforts.
Machine learning automation makes machine learning engineering processes faster, more efficient, and easier to operate. Without machine learning automation, a new model can take months from data preparation and training to actual deployment.
Automated Machine Learning (AutoML) is an approach that automates many of the time-consuming and repetitive tasks associated with model development. It is designed to improve productivity for data scientists, analysts, and developers, and to make machine learning easier for those who are not data and machine learning experts.
AutoML has other important benefits:
- It improves model accuracy and insight, by ensuring machine learning processes are developed according to data science best practices. Ad-hoc machine learning processes may not always incorporate best practices.
- It improves security, by enforcing secure practices in treatment of data, training and inference processes.
Machine learning automation simplifies the input requirements for model development and makes it available to industries where machine learning was not previously available. This creates opportunities for innovation, strengthens market competitiveness and promotes development.
Related content: learn more in our detailed guide to machine learning automation
Prioritization of Machine Learning Projects
There are many aspects to consider when prioritizing machine learning projects. Perhaps the most critical aspects are the time and cost involved, and whether you can use these resources to build a model that meets the basic requirements.
Make sure you the ML model you release into production is designed to meet the following requirements:
- The ML model respects the specifications of input and output, as well as the performance requirements.
- The ML model is designed to benefit both the organization and the end user.
- The ML model is scientifically rigorous.
In addition to the above requirements, you also need to make sure that the machine learning project you prioritize has the greatest impact on your business at the lowest possible cost. Here are several considerations that can help you assess this aspect:
- If a ML project can replace a complex and time consuming component and increase efficiency, performance, or sales, it can be defined as having “great impact”.
- ML projects are expensive. Sometimes lower costs translate into imperfect predictions. Carefully assess and define the budget before starting work on the project. When estimating costs, account for the difficulty of the problem you need to solve, the amount of data needed and its cost, and the required model performance quality.
Note that machine learning projects are nonlinear. At first, errors decrease quickly and then the progress starts slowing down. If you need to quickly deploy the solution in production, machine learning may not be the right technology for your current needs.
You can track the progress of your model by logging all activities and monitoring the time each activity takes. You can use this data to continuously improve the model while also estimating the complexity of similar future projects.
Optimizing Your Machine Learning Infrastructure with Run:ai
Run:ai automates resource management and orchestration for machine learning infrastructure. With Run:ai, you can automatically run as many compute intensive experiments as needed.
Here are some of the capabilities you gain when using Run:ai:
- Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
- No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
- A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.
Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run:ai GPU virtualization platform.
See Our Additional Guides on Key AI Technology Topics
Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of AI Technology.
- TensorFlow CNN: Building Your First CNN with Tensorflow
- PyTorch ResNet: The Basics and a Quick Tutorial
- Understanding Deep Convolutional Neural Networks
- Best GPU for Deep Learning: Critical Considerations for Large-Scale AI
- PyTorch GPU: Working with CUDA in PyTorch
- Top 8 Deep Learning Workstations: On-Premises and in the Cloud
- CUDA Programming: An In-Depth Look
- CUDA vs OpenCL
- NVIDIA cuDNN: Fine-Tuning GPU Performance for Neural Networks
- Apache Airflow: Use Cases, Architecture, and Best Practices
- Edge AI: Benefits, Use Cases & Deployment Models
- JupyterHub: A Practical Guide
- Keras Multi GPU: A Practical Guide
- PyTorch Multi GPU: Four Techniques Explained
- Tensorflow with Multiple GPUs: 5
- NVIDIA Deep Learning GPU: AI & Machine Learning Guide
- NVIDIA DGX: Under the Hood of DGX-1, DGX-2 and A100
- NVIDIA NGC: Features, Popular Containers & Quick Tutorial