IN THIS ARTICLE
- Essential technical skills for ML engineers
- Essential soft skills for ML engineers
- Essential tools/programs to master for ML engineers
- Essential certifications for ML engineers
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Machine learning engineering is a cornerstone of AI—without it, recommendation algorithms like those used by Netflix, YouTube, and Amazon; technologies that involve image or voice recognition; and many of the automated systems that power the products and services we use wouldn’t work.
That’s because machine learning engineering sits at the intersection of data science and software engineering; where a data scientist will analyze collected data to tease out actionable insights, a machine learning engineer will design the self-running software that makes use of that data and automates predictive models.
In action, if a machine learning engineer were to work on a recommendation algorithm for YouTube, they might be tasked with developing a machine learning pipeline that integrates into the platform so that a user can click on the “next” button once a video finishes playing and watch another video that is relevant to their interests, as determined by the data-informed algorithm and predictive models developed by YouTube’s data specialists.
The interdisciplinary nature of the role means that machine learning engineers are well versed in foundational data science skills such as understanding data structures and data modeling, quantitative analysis methods, building out data pipelines, and statistics, while also having computer science fundamentals and software engineering skills such as understanding computer architecture, proficiency with programming languages, and the ability to program software solutions.
As artificial intelligence plays a growing role in our lives, the demand for machine learning engineers has skyrocketed. Read on to learn more about the skills needed to become an ML engineer.
Essential technical skills for ML engineers
Machine learning engineering combines software engineering principles with analytical and data science knowledge in order to make a machine learning model usable to a piece of software or a person. This means that machine learning engineers need to have a slate of skills that span both data science and software engineering.
- Software engineering skills. Some of the computer science fundamentals that machine learning engineerings rely on include: writing algorithms that can search, sort, and optimize; familiarity with approximate algorithms; understanding data structures such as stacks, queues, graphs, trees, and multi-dimensional arrays; understanding computability and complexity; and knowledge of computer architecture such as memory, clusters, bandwidth, deadlocks, and cache.
- Data science skills. Some of the data science fundamentals that machine learning engineers rely on include familiarity with programming languages such as Python, SQL, and Java; hypothesis testing; data modeling; proficiency in mathematics, probability, and statistics (such as the Naive Bayes classifiers, conditional probability, likelihood, Bayes rule, and Bayes nets, Hidden Markov Models, etc.), and being able to develop an evaluation strategy for predictive models and algorithms.
- Additional machine learning skills. Many machine learning engineers are also trained in deep learning, dynamic programming, neural network architectures, natural language processing, audio and video processing, reinforcement learning, advanced signal processing techniques, and the optimization of machine learning algorithms.
Essential soft skills for ML engineers
Soft skills are what set apart effective engineers from those who flounder. While machine learning engineering is, at its core, a technical job, soft skills such as the ability to clearly communicate, problem solve, manage time, and collaborate with others are what lead to a project’s successful completion and delivery.
- Communication skills. It is not unusual for machine learning engineers to work with data scientists and analysts, software engineers, research scientists, marketing teams, and product teams, so the ability to clearly communicate to stakeholders the project goals, timelines, and expectations is a crucial part of the job.
- Problem-solving skills. The ability to problem solve is important for both data scientists and software engineers and essential for machine learning engineers. Machine learning focuses on solving real-time challenges, so the ability to think critically and creatively about issues that arise and develop solutions is a foundational skill.
- Domain knowledge. In order to design self-running software and optimize solutions used by businesses and customers, machine learning engineers need to understand both the needs of the business and the kinds of problems that their designs are solving. Without domain knowledge, a machine learning engineer’s recommendations may lack precision, their work may overlook features that are useful, and it may be difficult to evaluate a model.
- Time management. Machine learning engineers often juggle demands from different stakeholders while having to find the time to perform research, organize and plan projects, design software, and test it rigorously. Being able to manage one’s time is key to making valuable contributions to the team.
- Teamwork. Machine learning engineers are often at the center of AI initiatives within an organization, so they inevitably work with data scientists, software engineers, marketers, product designers and managers, and testers. The ability to collaborate with others and contribute to a supportive work environment is a skill that many managers look for when hiring a machine learning engineer.
- Thirst for learning. The fields of artificial intelligence, deep learning, machine learning, and data science are rapidly evolving, and even those who have earned graduate degrees and work as machine learning engineers find ways to continue their education through bootcamps, workshops, and self-study. Whether it’s learning the latest programming languages, mastering new tools or programs, or studying the latest breakthrough techniques and technologies, the most effective machine learning engineers are constantly refreshing their toolkit and open to learning new skills.
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Essential tools/programs to master for ML engineers
In addition to a solid understanding of programming and scripting languages such as Python, SQL, Java, and C++, many machine learning engineers are also proficient in the following programs and tools:
- Spark and Hadoop
- R Programming
- Apache Kafka
- Google Cloud ML Engine
- Amazon Machine Learning
- PytorchJupyter Notebook
- IBM Watson
Essential certifications for ML engineers
Most machine learning engineering roles require a candidate to hold at least a bachelor’s degree in a related field such as computer science, mathematics, or statistics, and some require a master’s degree or Ph.D. in machine learning, computer vision, neural networks, deep learning, or a related field.
Beyond higher education, certifications in machine learning, artificial intelligence, or data science are useful because of the relevant skills they impart. There is no shortage of universities and online training programs offering machine learning certifications, each taking a different instructional approach and focusing on different facets of machine learning, so it’s important to examine closely the skills taught, the hands-on experience offered, and whether there are opportunities to work on projects that involve the full machine learning pipeline.
It’s also useful to cross-check program curricula against the requirements of recent machine learning engineering job listings. For example, a machine learning engineering role at Facebook requires a degree in a related field, plus experience with deep learning, reinforcement learning, privacy-preserving machine learning, large scale personalization, artificial intelligence, and experience with developing machine learning algorithms and infrastructure in C/C++ or Python.
Meanwhile, an Apple job listing requires a degree in a related field, plus experience with applying machine learning to solve real business problems, experience with cloud platforms such as AWS, experience with machine learning frameworks such as Scikit-Learn and TensorFlow, and experience with data processing.
The expectations placed on a machine learning engineer will differ depending on the organization and team they join. Whatever certification a candidate pursues, it’s important that it equips them with foundational skills that are transferable and can serve as the building blocks of their career.
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