According to “The best jobs in the U.S. 2019” research conducted by Indeed, machine learning engineer is one of the world’s fastest-growing positions. It does not seem surprising as, over the past few years, the popularity of artificial intelligence (AI) and machine learning (ML) solutions raised significantly, and so did the role of experts working with these technologies. Machine learning engineers are essential in that process since they are responsible for creating algorithms and applications that enable machines to perform specific tasks without the need to be directed by humans. Naturally, it is a very short description of the ML engineer role. Let us take a closer look at this profession and examine what it entails.
For starters, we ought to say that machine learning engineers are currently in demand. The problem is that the entire AI industry struggles with the massive workforce shortage, and that’s a worldwide occurrence. According to Element AI, in 2018, there were fewer than 10,000 specialists (worldwide!) who had the necessary skills and knowledge to create fully-functional ML algorithms.
Today, the situation is not much better. The reason is simple–machine learning currently flourishes, and this technology becomes more and more effective every month.
The rise of machine learning
A machine learning engineer’s work consists of aspects that data scientists and software engineers ought to have as well. However, while a software engineer’s primary focus is on writing code, and data scientists analyze and process the data, machine learning engineers combine both these specializations.
We have to note that both these roles require quite different sets of skills. Data scientists usually have analytical minds. In order to gather, process, and analyze huge volumes of data, they need to use a combination of mathematics, statistics, and knowledge of various machine learning tools. On the other hand, software engineers typically do not possess knowledge and understanding of AI-powered solutions. Their main role is based on writing programs and designing software systems for the companies.
That is why companies need a professional who can take out the best of both worlds i.e., you need to use artificial intelligence consulting services. Machine learning engineers are able to combine programming with the laws ruling data science so that companies can benefit from AI technologies and, at the same time, adhere to traditional programming protocols and practices.
On Addepto’s blog, we frequently explain what machine learning is all about, and why this technology is essential today. In short, we can say that machine learning takes data and AI-based algorithms in order to create intelligent, self-learning solutions that help companies execute simple and repetitive tasks. But that’s just a short explanation.
A bridge between production and building models
We could say that machine learning engineers act as a bridge between data scientists’ statistical work and model-building software engineering teams that need to deliver AI or ML-based systems, services, and platforms.
Data scientists can create extremely innovative models that fully solve issues encountered by companies and individuals. However, often one needs a certain level of technical knowledge and background in order to be able to use them. In such a situation, machine learning engineers need to take their AI knowledge and combine it with software engineering and programming skills so that potential users are granted easier access to AI-based systems. Tasks which goal is to provide such quick and easy access may include:
- Automation of model training and evaluation process
- Ensuring that data for model training is cleaned and data flow between ML models and the firm’s data system is facilitated
- Translations of data scientists’ work from various environments like analytical applications
Machine learning engineers are also responsible for applying and developing ML/AI algorithms so that routine tasks that humans normally perform can be fully automated. A successful engineer can observe a task performed by humans and figure out how it can be transformed in a way that would enable automation. To do so, ML engineers need to possess programming and analytical skills and understand techniques and tools of ML and apply them in real life.
The main purpose of ML engineer’s work is training, monitoring, optimization, testing, and deployment of ML algorithms for specific tasks. However, before it can be done, ML engineers need to prepare data that will be used by the algorithm. Data preparation usually includes normalization, dimensionality reduction, feature engineering, missing value imputation, and other similar operations. Once the data is prepared and organized, ML engineers set a training algorithm and execute it in order to achieve previously agreed goals.
Why companies need machine learning?
In short, we could say it because it’s a truly game-changing technology. Today, machine learning is being applied actually everywhere, both in B2B and B2C sectors. With machine learning, organizations all around the globe can:
- Cut costs (as we’ve just explained, machines can perform diverse tasks previously reserved for human workers)
- Make more informed decisions (ML is broadly used in business intelligence and business data analysis)
- Achieve much higher precision regarding the future (for instance, ML predictions in the medical field are typically more accurate than human)
- Increase effectiveness (usually, ML algorithms are far quicker than human employees)
Skills machine learning engineers ought to possess
A constant need for machine learning engineers in organizations around the world tends to convince IT, specialists, to shift to ML engineering. Since many of them do not face direct experience with AI solutions, employees seek individuals who work as data scientists and have a solid programming background. Employers, such as Addapto AI Experts, usually require the following skills from ML engineer candidates:
- Degree in computer science/statistics/mathematics, or related fields
- Math and statistic skills
- Advanced robust data modeling and data architecture skills
- Experience with programming environments such as Python, Java, C++, R, etc.
- Experience with working with ML frameworks and libraries
- Strong interpersonal, collaboration, and verbal communication skills.
The global machine learning market was valued at 6.9 billion USD in 2018 and is expected to register a CAGR of over 43% by 2025. These numbers make it clear that the ML market is expanding very quickly. Due to the fast growth pace, companies will be forced to hire machine learning engineers in order to stay ahead of the competition. In the future, we can expect many astonishing breakthroughs caused by AI and supported by ML engineers.