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Machine learning and deep learning are branches of artificial intelligence.

Linked to big data, these technologies enable computers to analyse data streams in a learning process. A machine learning or deep learning engineer is an expert in this field.

The distinction between machine learning and deep learning

In both these areas of artificial intelligence, the development and performance of algorithms result from learning based on data.

Machine learning (ML) is the application of statistical methods to computers to improve their ability to solve tasks without being explicitly programmed. The performance of a machine learning model depends on the quality of the training data and the suitability of the chosen model. Machine learning algorithms encompass both ‘classical’ algorithms and neural networks. Deep learning (DL) is limited solely to learning via neural networks. It is particularly well suited to processing unstructured data such as images or text and extracting relevant information from it (pattern recognition, automatic speech recognition, natural language processing, etc.)

Overview and responsibilities of a machine learning or deep learning engineer

Some engineers work with both technologies, whilst others specialise in ML or DL. It is also possible to focus on specific application scenarios, or to take on a role more centred on project management as a machine learning project manager or deep learning project manager. In both cases, the engineer applies the latest algorithmic innovations from the R&D team. They develop and test these using artificial intelligence algorithms.

Machine Learning Engineer

Their main role is to select, train and deploy machine learning models based on a dataset. They may also develop algorithms and write programmes to extract relevant information for use in the modelling phase. The aim is to enable the computer to respond to complex problems. This work lies at the crossroads of science, computer science and mathematics. This specialist may also be responsible for data engineering, which ensures the quality of all data. Often confused with the data analyst – who specialises in a specific category of data relating to a business or strategic issue – the data scientist has a more holistic view and a more cross-functional role.

Deep learning engineer

The role of a deep learning engineer is to specialise in the development and implementation of learning algorithms based on complex deep neural network architectures. This is a more technical task than that of a ‘traditional’ machine learning engineer, as the tools used are more advanced from a theoretical perspective. In agriculture, for example, deep learning enables machinery to distinguish between different plants and apply the appropriate treatment to each, thereby reducing the use of herbicides and improving yields. The system is based on visual recognition. Deep learning encompasses, amongst other things, convolutional neural networks (primarily suited to image recognition) and recurrent neural networks (effective for time-series problems).

Business implications

Machine learning and deep learning techniques can be applied across a wide range of sectors.

Artificial intelligence is already at the heart of medical imaging, robotics and agriculture, but its potential is vast. Industry, the service sector, public organisations
 all sectors recognise that AI is indispensable.

Careers in artificial intelligence are on the rise. The recruitment market is taking a keen interest in these roles.

Ethical implications
Artificial intelligence requires a legal and ethical framework to develop safely: compliance with the law, respect for freedom, equality and gender parity

aivancity’s programmes incorporate all aspects of artificial intelligence and the challenges it presents – whether technical, technological, commercial, ethical or legal – into their curriculum. These are comprehensive, blended learning programmes that enable future engineers to acquire the highest level of knowledge and a broad range of expertise.
Key skills
Whether working in machine learning or deep learning, the project manager is methodical and organised. Their expertise is built on a solid foundation in mathematics and computer science.

They are proficient in reporting and work equally well independently or as part of a team. Possessing unwavering determination, they thrive on challenges and questioning the status quo. On the technical side, the ML or DL engineer is familiar with frameworks such as TensorFlow and PyTorch. Python and C++ are part of their toolkit.

Knowledge of Git, Docker and CUDA is, of course, a plus. They are not daunted by English; they understand and use it very well.

Trends and drivers of change
Machine learning or deep learning engineers are employed either by end clients (software publishers, start-ups, IT departments) or by consultancy firms that provide solutions to these clients. Careers in artificial intelligence are set to evolve rapidly as there are still too few qualified professionals on the market. Experience is therefore key to career progression.

Cutting-edge roles, innovative technologies and specialised expertise: artificial intelligence is advancing, expanding and becoming increasingly sophisticated.