Main image of article Machine Learning Engineer Skills: Essentials to Learn

The responsibilities of a machine learning (ML) engineer can vary significantly between organizations. However, in the most general of ways, machine learning engineers are typically responsible for deploying machine learning models into production.

The ways in which they contribute to productionizing a model may differ; it isn't simply about hosting a model on a server. Machine learning engineers may write data quality tests to ensure accurate model inputs, automate training pipelines for task-specific models, build model evaluation and monitoring processes, optimize model inference, and more.

Regardless of the specific tasks, the main goal of a machine learning engineer is to provide business value to an organization through some sort of model-based service.

Gauri Powale, software engineer at D2iQ, says machine learning engineers are typically responsible for researching machine learning models and algorithms. This often starts with a basic model and then improves and changes based on the specific problems. “They also design and develop algorithms and models that can learn from data and make predictions and use the data to train a machine learning model and optimize it for better performance,” she says.

Machine learning engineers must be able to deploy models to production, test for effectiveness, and then monitor model performance to make necessary updates as new data becomes available or model performance degrades.

Skill Sets Range from Programming to Cloud Computing

Powale breaks down the necessary machine learning skills into six different areas, from programming to cloud computing:

Programming: A deep understanding of data structures and algorithms; machine learning engineers are preferably proficient in Python or Java.

Machine learning algorithms and frameworks: Understanding of deep learning architectures, machine learning algorithms and theory, Keras, TensorFlow, PyTorch.

Software Engineering: Understanding software engineering principles such as debugging, testing, and continuous integration.

Machine learning system design: Machine learning engineers must design and build end-to-end machine learning systems and develop infrastructure and tooling to support machine learning pipelines.

Distributed computing: Experience handling large datasets and scaling machine learning models, as well as experience with distributed computing platforms such as Hadoop and Spark.

Cloud computing: Experience deploying machine learning models on cloud platforms such as AWS, GCP and Azure.

Dhruv Nair, senior data scientist with Comet ML, notes Python is the default language for most machine learning engineering roles. “Therefore, it is recommended to invest heavily in gaining a thorough understanding of its strengths and weaknesses and learning about best practices for software development with Python,” he says.

For specific machine learning frameworks, he recommends becoming familiar with either PyTorch or Keras/TensorFlow. “If working with tabular data, look into Scikit Learn and XGBoost,” he adds. “For Transformer or NLP problems, the Hugging Face ecosystem is definitely worth exploring.”

On a day-to-day basis, machine learning engineers might be asked to do the following:

  • Researching, designing and implementing ML models and systems
  • Implementing machine learning algorithms and tools
  • Scaling data science prototypes
  • Selecting appropriate data sets, verifying data quality, cleaning and organizing data (in collaboration with data engineers)
  • Performing statistical analysis
  • Executing tests and optimizing machine learning models and algorithms
  • Monitoring systems in production and retraining them to improve performance
  • Utilizing machine learning libraries

Websites such as Made with ML, Towards Data Science, and w3schools can provide you with tactical knowledge of machine learning if you’re just beginning to explore the discipline.

Knowledge of Cutting-Edge Technologies a Plus

Powale believes deep learning, reinforcement learning, AutoML, natural language processing (NLP) and quantum computing are among the cutting-edge technologies that machine learning engineers should also be aware of. “NLP is a field of artificial intelligence and linguistics that focuses on the interaction between computers and human languages and is used in many tasks, such as speech recognition, text-to-speech, and sentiment analysis,” she says.

She explains quantum computing—a type of computing that uses quantum phenomena such as superposition and entanglement to perform operations on data—has the potential to solve problems that classical computers cannot. “As an emerging field, the goal of quantum machine learning is to develop machine learning algorithms that can run on quantum computers to solve complex problems more efficiently,” Powale adds. “This field is evolving, and I expect that in the next decade or so, as the research in quantum computing grows, more companies will begin to look to it to solve classically challenging problems.”

Nair says it's difficult to pinpoint what's considered ‘cutting edge’ since it changes frequently. “However, if I had to make a recommendation, I would suggest looking into transformer models, attention mechanisms, self-supervised learning, multimodal models, and diffusion models,” he says. “Keep in mind that these methods and techniques could become outdated in a few years. Nevertheless, they are currently generating a lot of interest in the field.”

Soft Skills Count, Too

From Nair's perspective, tempering stakeholder expectations may be the most crucial skill for a ML professional. “Machine learning is sometimes seen as a magical solution to a company's problems, but this is not the case,” he says. “Communicating clearly with both technical and non-technical stakeholders about the challenging realities related to model development is critical to the long-term success of any machine learning project.”

Aside from that, the soft skills necessary for machine learning engineers match those in other tech professions. “Learn to be direct while also being kind to your peers,” Nair says. “Ask colleagues who are excelling in their job for actionable feedback so that you can learn and grow from them.”

Powale agrees effective communication skills are important in every role—but are even more essential for machine learning engineers: “Machine learning engineers must have the ability to clearly explain complex technical concepts to those who are non-technical.”

She notes machine learning engineers should also be able to work effectively with other teams and team members, as they often collaborate with a variety of other roles, such as data scientists, software engineers and product managers.

Business acumen and problem-solving skills are likewise critical, as machine learning engineers need a strong understanding of the business goals, its KPIs, and customers' needs. “It is important for ML engineers to be able to approach problems with a creative and innovative mindset and be able to adapt to changing requirements and constraints,” Powale says.