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Job File: Machine learning or deep learning engineer

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Machine learning and deep learning are branches of artificial intelligence. Linked to big data, these technologies enable computers to exploit data flows in a learning logic. The machine learning or deep learning engineer is an expert in this speciality.

 

DISTINCTION BETWEEN MACHINE LEARNING AND DEEP LEARNING

In these two cases of artificial intelligence, the evolution and performance of the algorithms are the result of learning from data.

Machine learning (ML) is the application of statistical methods to computers to improve their performance in solving tasks without being explicitly programmed. The performance of a learning machine model is related to the quality of the learning data and the compatibility of the chosen model. Machine learning algorithms cover on the one hand "classical" algorithms and on the other hand neural networks.

Deep learning (DL or deep learning) is limited only to learning through the neural network. It is particularly well suited for processing unstructured data such as images or text and for extracting relevant information (pattern recognition, automatic speech recognition, automatic natural language processing etc.).

 

THE MACHINE LEARNING OR DEEP LEARNING ENGINEER: PRESENTATION AND MISSIONS

Some engineers practice both technologies, others specialize in ML or DL. It is also possible to focus on concrete application cases, or to have a more project-oriented position as a machine learning project manager or deep learning project manager.

In both cases, the engineer applies the R&D team's latest innovations in algorithms. He develops and tests them using one of the artificial intelligence algorithms.

Machine Learning Engineer

His main role is to select, train and deploy learning models based on a set of data. He will also be able to develop algorithms and write programs to extract relevant information to be used in the modeling phase. The goal is to make the computer capable of reacting to complex problems. It is a work at the crossroads between science, computer science and mathematics. This specialist may also be in charge of data engineering, which guarantees the cleanliness of all data.

Souvent confondu avec le data analyst - spécialisé dans une catégorie de données autour d’une question business ou stratégique - le data scientist a une vision plus globale et un rôle plus transversal.

Often confused with the data analyst - specialized in a category of data around a business or strategic issue - the data scientist has a more global vision and a more transversal role.

Deep Learning Engineer

The role of a deep learning engineer is to be a specialist in the development and implementation of learning algorithms based on deep and complex neural network architectures. This is a more technical task than that of a “classical” machine learning engineer, because the tools used are more advanced from a theoretical point of view. In agriculture, for example, deep learning allows equipment to differentiate plants and distribute the right treatment to them, thus reducing the use of herbicides and improving production. The system is based on visual recognition. Deep learning includes, among others, convolutional neural networks (mainly adapted to image recognition) and recurrent neural networks (efficient for time series problems).

 

BUSINESS IMPLICATIONS

The techniques of machine learning and deep learning can be used in many fields of activity.

Artificial intelligence is already at the heart of medical imaging, robotics and agriculture, but its potential is extremely broad. Industry, services, public organizations... all sectors understand that AI is essential.

Businesses around artificial intelligence are developing. The recruitment market is taking a close interest in these profiles.

 

ETHICAL IMPLICATIONS

Artificial intelligence needs legal and ethical aspects to grow up safely: adherence to laws, freedom, equality, parity...

Aivancity programs integrate into their learning all the components of artificial intelligence and its challenges, whether technical, technological, commercial, ethical or legal. These are global and hybrid training courses that allow future engineers to benefit from a maximum level of knowledge and a wide know-how.

 

KEY COMPETENCIES

Whether in machine learning or deep learning, the project manager is methodical and organized. His expertise is based on a solid foundation in mathematics and computer science. He masters reporting and works both autonomously and as part of a team. Endowed with an unfailing pugnacity, he likes challenges and questioning. On the technical side, the ML or DL engineer is familiar with frameworks such as TensorFlow and PyTorch. Python and C++ are part of his world. Knowledge of Git, Docker and Cuda is obviously a plus. English does not freak him out; he understands it and uses it very well.

 

TRENDS AND EVOLUTION FACTORS

The machine learning or deep learning engineer is hired by the end customer (software publisher, startups, CIO) or in a consulting company that offers solutions to them. Artificial intelligence professions are bound to evolve rapidly because there are still too few profiles on the market. Experience is therefore a guarantee of career advancement.

Avant-garde professions, innovative technologies, cutting-edge specializations, artificial intelligence is progressing, growing and becoming denser.