What is a Machine Learning Engineer?
Machine learning engineering involves using programming, analytics, and data science knowledge to work with a machine learning (ML) model and deliver it as part of a product or directly to end users. The ongoing process of deploying and operating machine learning models is known as machine learning operations.
Machine learning engineers are behind the scenes of endless applications of machine learning in today’s business environment. Some of the more common are:
- Image and audit recognition—machine learning can auto-tag images, identify objects in images, convert text-to-speech, and provides other capabilities needed to turn unstructured data into useful information.
- Predictive analytics—machine learning can create association rules, recommendation engines and predictions regarding customer behavior.
- Security and fraud prevention—machine learning algorithms can identify suspicious activity in computer networks, flag user accounts that may have been compromised, and detect fraud by analysing large volumes of financial transactions.
In this article, you will learn:
- Machine Learning Engineer Roles and Responsibilities
- What are the Skills Required to Become a Machine Learning Engineer?
- Machine Learning Engineer vs Data Scientist
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:
- Analyzing large datasets and choosing the best approach to prepare them for analysis
- Collaborating with data scientists to build effective data pipelines
- Building infrastructure necessary to bring machine learning code to production
- Maintaining, scaling, and improving machine learning solutions in production
- Working with common machine learning algorithms and software libraries
- Optimizing and tweaking machine learning algorithms based on production behavior
- Communicating with stakeholders and business users to understand their requirements and explain the capabilities of new models
- Providing technical support to data scientists and product teams with respect to machine learning datasets and systems
Related content: read our guide to machine learning infrastructure
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.
Related content: read our guide to machine learning automation
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, for 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.
Machine Learning Engineering 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.