Cursus Expert


ML Engineer

A program made for me?

ML engineer
What is the ML Engineer course
The course’s steps
Parcours ML Engineer

Price for our course : 7000€ *
*POSSIBILITy to pay in installments: learn more

Don't miss our next launch!

May 03, 2022

My goals?

Develop APIs to isolate machine processes Learning

Automate the deployment of processes, sometimes distributed, thanks in particular to containerization

Automate data pipelines using planning tools

Moving Machine Learning pipelines from an exploration and development environment to an operations and production environment

Follow the development and tests put in place to monitor the progress of a Machine Learning project

Deploy in production

API development


Pipeline management


Recognized by the State, our Data Science training courses are eligible for CPF.

Thanks to our strong ties with companies and our rate
of high employability, the Pôle Emploi – via the AIF
also funds some learners!

logo CPF

The curriculum
ML Engineer

Frise ML Engineer

Price of the training : 2500€*
*Possibility to pay in several times: learn more

Would you like to receive the complete syllabus ?

    S'inscrire à notre newsletter

    Do you have questions?

    The Machine Learning Engineer Job:

    The Machine Learning Engineer appeared with the development of data teams. The Data Scientist / Data Engineer separation can sometimes prove to be insufficient. For applications, there are fewer parts dealing only with machine learning than for companies, which are beginning to focus on creating and putting into production the technologies they have adopted. In order to take care of infrastructures, monitor data pipelines or check that computing resources are sufficient, companies are looking for profiles of engineers specializing in Machine Learning.

    The Machine Learning Engineer evolves within the Data teams, among Data Scientist, Data Engineers, partners or even product managers. It is not a new profession but rather a specialty that requires expertise to meet the needs of teams. The appearance of Machine Learning Engineers is the consequence of the growing maturity of the data business. 

    Our expert curriculum provides better knowledge in software engineering. It allows you to work more efficiently on the maintenance of production systems. In addition, you will acquire a better understanding of the difficulties of production startup, data pipeline obsolescence, security or data integrity. Halfway between Data Engineering and Data Science, you will know how to make data usable for both Data Scientist and Data Engineer. 

    The Machine Learning Engineer is a versatile expert who is a major part of the data team. So don’t hesitate and join our expert curriculum.

    The Machine Learning Engineer shares common missions with the data scientist. Like him, he develops Machine Learning algorithms in order to solve problems of classification, recommendation, anomaly detection …

    A Machine Learning Engineer can evolve in many sectors. He will work on anomaly detection, fraud detection, classification of searches, classification of texts/feelings, spam detection and many other aspects of Machine Learning.

    As an expert in software for building complex systems, he is responsible for guiding the use of technologies, data, machine learning. He helps to imagine, build, deploy and develop the next generation of data processing tools that will help fundamentally transform data activities. 

    The missions are diverse and varied. It applies software development practices and standards in order to develop robust and sustainable software. To do this, it must maintain an active role in every part of the software development life cycle. It is also necessary to guide non-technical teams in understanding best practices to guide software development. 

    Technically, it optimizes and improves the computational efficiency of algorithms and software design. In cooperation with Data Engineers and Data Scientists, he is in charge of putting the same software into production. His collaboration also extends to the IT teams to ensure the feasibility of projects or their evolution.

    Here are some of the ML engineer’s missions:

    • Identifying the main issues raised by the transition to production
    • Create a battery of tests to verify the proper functioning of a process
    • Create automated and scheduled Machine Learning pipelines 
    • Create and use containers in distributed environments

    Our course:

    Our ML engineer training is intended for an already specialized public. Our goal is to train Data Scientists to put their projects into production. Therefore, the mastery of Machine Learning algorithms and libraries is mandatory to follow the training.

    Moreover, programming is essential to the production of any Machine Learning project.  For this reason, an appetite for code and a taste for computers are also appreciated. 

    You are not sure you meet these prerequisites? Make an appointment here to discuss it with our educational consultants!

    All registrations require contacting our pedagogical advisers. Attentive and attentive, they will be able to answer your questions, guide you through your course and check from the outset that your expectations and our training courses are aligned.

    You can make an appointment with them here!

    After this quick discussion, they will send you a link to registration on our site. 

    Then, our admissions team will contact you by phone to discuss your motivation, your project and to discuss the topics of training financing. 

    Finally, a placement test will allow us to know and validate the basics you are starting with. For ML Engineer, it will be a timed technical test that will judge your technical and theoretical knowledge. 

    During your reflection and up to this stage, you are in no way committed to DataScientest and can at any time, if you wish, put an end to your steps. 

    Once your project has been confirmed, you will move on to the registration phase with our teams who will take care of initiating your training in data science and implementing it with you in all its aspects.

    DataScientest is the once organization to offer hybrid training, i.e. both face-to-face and distance learning. (approximately 10% and 90% respectively). This makes it possible to combine flexibility and rigor without compromising on either one or the other. It is a well thought-out choice that motivates our pedagogy so that learning can be carried out to its conclusion with motivation. We have detailed the advantages of this unique combination in an article on the subject.

    In addition, it is possible to follow the Data scientist training course in remote: face-to-face courses are then replaced by videoconference courses. However, the follow-up remains the same: the professors remain attentive and follow you throughout your course.

    To understand our learning method in 2 minutes, discover this video,

    Click here to watch our video

    At the end of your training you will have :

    • Move Machine Learning pipelines from an exploration and development environment to an operations and production environment
    • Create automated and scheduled Machine Learning pipelines 
    • Create and use containers in distributed environments
    • Follow the development and tests put in place to monitor the progress of a Machine Learning project

    Our curriculum:

    The curriculum is based on blocks, which are themselves divided into modules that allow you to master the skills deemed necessary for the job of machine learning engineer.

    Thanks to our studies with our communities: DataBoss, Alumni etc., our data science experts have been able to build a curriculum that precisely meets the skills sought by recruiters.

    Thus, throughout the training, you will master the following tools: Python, Git and Github, Flask, Docker, Kubernetes, Gitlab, Airflow …

    For a total hourly volume of 100 hours of training, 85% of your training takes place on a personalized coaching platform while the remaining 15% is in the form of a masterclass where an experienced teacher gives a lecture and answers all your questions.

    Beyond the platform and the masterclasses, you will work on a project that will confirm the skills you have acquired and thus allow you to be directly operational.

    The Machine Learning engineer training is available in the “continuing education” format which requires an involvement of 8 to 10 hours per week for 3 months. Make an appointment to learn more

    Absolutely! Throughout the training, our teachers are available and at your disposal to answer all your questions and accompany you in your professional training. Our pedagogy is based on a personalized accompaniment from beginning to end.  Moreover, the data science experts who designed the course are also your teachers. 

    They maintain human and daily relations with you to follow you closely and ensure your involvement in the training and its quality. We won’t let you down 

    All our exams are corrected by hand by our experts to ensure you a quality support. Indeed, you progress at your own pace and we accompany you to be directly operational at the end of your training.

    Throughout your professional training, you will put into practice the tools you have mastered through a common thread project.

    The subject of the red thread project depends on your interest. Indeed, the choice of the subject is yours and it is up to you to pitcher it to our teams for validation. Thus, you start from scratch since you do not have clean databases, the models are not pre-trained… With the support of our teachers, you move forward step by step to make your project a reality. 

    It constitutes an important dimension of your training action: punctuated by glues and support with your pedagogical director to ensure the progress and smooth running of the project. 

    It allows you to acquire operational experience that is highly appreciated by recruiting companies. It also ensures the quality of the training and the knowledge acquired at the end of the machine learning engineer training.

    In data, each business will have its own specificities. One thing is common to all of them, and that is the need to exchange and communicate on the use of data. Your work is part of an orderly process based on a common data culture and efficient information transfer.

    This is why we offer workshops to help you develop your soft-skills. Among these, you will find

    • Data class on project management.
    • A support through CV workshops and career coaching.

    As a B2B leader in data science training, DataScientest is well known among companies that entrust it with the data science training of their teams. This trust has forged the recognition of its diplomas.

    The Machine Learning Engineer career:

    The Machine Learning Engineer is halfway between the data scientist and the data engineer. His opportunities to find a job on the job market are therefore all the more tenfold as he must master both data science and software engineering. The qualifications acquired at the end of the training are such that the machine learning engineer will have no difficulty in reconverting to data scientist, NLP scientist, software engineer,…  

    Today, there are practically no more fields in which Machine Learning is not applicable. It is more and more used in the education, health and computer science sectors.

    As with the Data Scientist, Data Analyst or Data Engineer, the salary that a Machine Learning Engineer is entitled to varies according to his experience, the company that hires him and the city in which he works.

    On average, a junior Machine Learning Engineer can earn around €25,000 per year. The salary of an expert can go up to 60 000€ / year. The average salary in France is €40,000 per year, while it can exceed a hundred thousand euros in the United States!

    Of course! The Machine Learning Engineer is the bridge between the data scientist and the data engineer. As such, it juggles between mathematics, statistics and probability, which are the main tools of the data scientist, and the programming and production that are specific to the data engineer.

    So there are naturally obvious bridges between the Machine Learning engineer and the data engineer but also with the data scientist mainly.

    The demand for work and therefore the job offer in AI and especially in Machine learning engineering is booming. The labor market in Machine Learning is even currently in shortage. Companies are becoming more and more aware of the added value of Machine Learning to make full and more efficient use of their data and are struggling to find the right profiles. This opens the doors to candidates and puts upward pressure on salaries! 

    Today, there are hardly any sectors that do not compete for talent. Machine Learning applications are used in the fields of education, healthcare, industry, IT, etc. Moreover, they are as varied as the data itself: image and speech recognition, customer knowledge, risk management and fraud prevention.

    As a B2B leader in data science training, DataScientest is well known among companies that entrust it with the data science training of their teams. This trust has forged the recognition of its diplomas.

    Our post-training support services

    We set up betas tests for our entire alumni community. Indeed, these tests allow you to continue your training and gain data skills even after the end of the training. 

    We set up regular newsletters, elaborated by our data science experts, to inform you about the latest news. These newsletters are elaborated by our data science experts. 

    Finally, the DataAlumni community will allow you to develop your network, and to exchange with former learners on various themes around Data Science.

    The DataAlumni community is a LinkedIn that gathers DataScientest alumni. On this page, questions, advice and technological news are shared for the benefit of all. 

    In addition to this, DataScientest will launch in the coming weeks a thrombinoscope that will allow alumni to be connected, it will include the company and the position of each one.

    Initially, DataScientest supported the data transition of companies.  

    This allowed us to create strong links with the major groups that ensured the growth of our structure. 

    Subsequently, they were the ones who motivated the launch of our offer to individuals in order to compensate for the lack of competent profiles. 

    This need for good profiles is reflected in the survey we conducted among 30 of the CAC 40 groups 

    On the strength of our past with large companies, we then signed partnerships related to the hiring of our alumni. All our partner companies are committed to including all our students at the end of their training in their recruitment process: this, coupled with the help with CVs and interviews, means that you will be in pole position to get the job of your dreams!

    Not only can we help you, but we are also in an ideal position to do so and make a success of your professional integration.

    During the training, you will benefit from career and CV workshops organized by our HR team. They allow you to prepare your application and train you for recruitment tests in data science.

    Our career team is available to advise you, accompany you and ensure your professional integration after your graduation.

    To discover the list of our partner companies, click here 

    To find out more about DataScientest’s career support actions, click on this link

    Are you still hesitating on the route to choose closest to your expectations?

    A member of our DataScientest team will be happy to help you make the right choice