EXPERT COURSES

Machine Learning Engineer

Bootcamp (7 months)
or
Part-time (13 months)

Become a Machine Learning Engineer with our Bac +5 level training certified by the Ecole des Mines ParisTech.

NEXT ENTRY
September 06, 2022
PRICE OF THE TRAINING
7590 €
logo sorbonne
Certificate delivered by University La Sorbonne

Training content​

1. 1. Programming

Fundamentals of Python, Numpy, Pandas

2. Dataviz

Matplotlib, Seaborn, Bokeh

3. Machine Learning

Classification, Regression, Clustering with scikit-learn

4. Advanced Machine Learning

Time series, Text mining, Dimension reduction

5. Data Engineering

Database, SQL, PySpark

6. Deep Learning

Neural networks, CNN and RNN with Keras, TensorFlow and PyTorch

7. Complex systems and AI

Reinforcement Learning, Deep RL

8. Advanced programming

Webscraping, Linus Bash programming, Git/Github and Unit Testing

9. DataOps - Isolation

FastAPI, API security, Docker, Flask and Bootstraps

10. DataOps - Orchestration

Kubernetes, Airflow

11. ModelOps

MLFlow and Acculturation Data

2. Dataviz

Matplotlib, Seaborn, Bokeh

4. Advanced Machine Learning

Time series, Text mining, Dimension reduction

6. Deep Learning

Neural networks, CNN and RNN with Keras, TensorFlow and PyTorch

8. Advanced programming

Webscraping, Linus Bash programming, Git/Github and Unit Testing

10. DataOps - Orchestration

Kubernetes, Airflow

Throughout your Data Scientist training, you will carry out a 120-hour project.
The objective: apply your learning to a real problem and benefit from a first concrete achievement to add to your portfolio.

A Machine Learning Engineer missions

The Machine Learning Engineer develops artificial intelligence (AI) systems using large data sets to research, develop and generate algorithms capable of learning and predicting. He/she masters the entire Machine Learning process, from the design of the algorithm to its deployment and production.

Study and analyze

Investigate and analyze relevant data related to the company’s production process, sales or customer data set.

To predict

Develop predictive models to anticipate an evolution or to determine an interesting target value for the company

Deployment

Put Machine and Deep Learning algorithms into production and deploy models on Cloud solutions

To implement

Exploit the results of data analysis and modeling to make them readable, usable and actionable by the other departments of the company.

You have questions ? We have the answers.

Accordion Content

The Machine Learning Engineer appeared with the evolution of needs and missions given to Data Science teams. Professional experts in the data world have faced a demand for technical knowledge related to model training collaboration. Companies are therefore facing growing needs for automation and deployment of predictive models on the cloud.

Organizations, companies, public sectors and associations increasingly need to provide their customers, partners or public with predictive models based on Machine Learning.

The objective of the Machine Learning Engineer is to take a prediction model based on Machine Learning, to condition it (thanks to APIs and containers), to test it (with unit tests) and to set up its deployment on a Kubernetes cluster.

The Machine Learning Engineer is a versatile expert who occupies a major place in Data Science teams.

So don’t hesitate any longer and join our expert course!

The Machine Learning Engineer shares common missions with the Data Scientist. Like him, he develops Machine Learning algorithms in order to solve classification and recommendation problems, but the Machine Learning Engineer can deploy these models, for example, on the Cloud.

A Machine Learning Engineer can evolve in many sectors. He will notably work on anomaly detection, fraud detection, search ranking, text/sentiment classification, spam detection and many other aspects of Machine Learning.

A Machine Learning Engineer is also responsible for guiding the use of technologies, data and Machine Learning.

The missions are thus diverse and varied. In particular, he applies software development practices and standards in order to develop robust and long-lasting solutions. To do this, he must maintain an active role in each part of the development life cycle of Machine Learning-based solutions. It is also necessary for him to guide non-technical teams in understanding best practices to guide the development of these solutions.

Here are some missions of the Machine Learning Engineer:

  • Put Machine and Deep Learning algorithms into production;
  • Master the techniques of manipulation and pre-processing of data;
  • Develop APIs;
  • Automate the training of predictive models and deployment on the Cloud (containerization).
Accordion Content

In order to join the Machine Learning Engineer training, you must have obtained a Bac+3 level diploma in mathematics, statistics or science (European level 6). You also need to have good communication skills.

These prerequisites exist because although the training is centered on data science, and not mathematics, these are necessary for a good understanding of the logical principles of the concepts covered.

In addition, programming is essential to the development and production of any Machine Learning project. For this, an appetite for programming is appreciated. As terminology, documentation and online resources are primarily in English, we recommend that you are fluent in the English language.

After your registration on the site, we contact you for the first time for a presentation of what DataScientest is, what we can offer you but also your background and your wishes. The idea is to align your expectations from that moment with our training courses.

You can of course also make an appointment directly by clicking here ! 

Then we redirect you to a technical positioning test that we use to know what bases you are starting with . These are essentially mathematical questions dealing mainly with basic notions (L1/L2 level) in probability, statistics, analysis and algebra.

Once this test has been passed, a member of the admissions team will contact you to discuss your results and validate your professional project, your motivations, and finally the relevance of your educational project.

Once your project is confirmed, you go to the registration phase with our teams who will take care of initiating your Machine Learning Engineer training and setting it up with you in all its aspects.

At the end of your training you will know:

  • Prepare data, set up a data analysis strategy and master Python programming;
  • Train predictive Machine Learning models and automate the updating of these models;
  • Manipulate neural networks and implement Computer Vision or Natural Language Processing algorithms;
  • Exploit artificial intelligence models in a production context;
  • Implement a classic Machine Learning algorithm and use it on text data
  • Deploy models on cloud solutions;
  • Build Data Visualizations allowing the valuation of results.

The course is based on sequences themselves divided into modules that allow you to master the skills deemed necessary for the profession of Machine Learning Engineer. 

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

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

For a total hourly volume of 550 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 leads a course and answers all your questions.

Beyond the platform and the masterclasses, you will work on a common thread project which will confirm the skills 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 10 hours per week for 13 months. Make an appointment to find out more

Obviously ! And who better to provide support than our teachers , who also designed the program. They are available and attentive to any questions, whether theoretical or practical. 

They also follow the progress of learners closely so that everyone is neither neglected nor demotivated. Each disconnection of a certain duration will be communicated to your cohort manager who will then hear from you so as not to leave you in difficulty!

Finally, our papers, exams and defenses are also corrected by hand by our panel of qualified teachers: everything is done so that everyone can progress effectively at their own pace . At DataScientest we are convinced that only personalized monitoring ensures quality learning!

Throughout your training, and as your skills are developed, you will lead a project for an artificial intelligence solution.

This project may come from our catalog, composed of various subjects, with technical business issues and using rich and complex data. You can also propose a personal project, as long as the data is accessible and our teaching team validates it.

It is an extremely effective way to move from theory to practice and to ensure that you apply the themes covered in class.

It is also a project highly appreciated by companies because it ensures the quality of the training and the knowledge acquired at the end of the Machine Learning Engineer training since the use of soft-skills is also very present.

  • Ability to transmit information
  • Know how to present and popularize your work
  • Know how to highlight data with interactive tools (Dashboard, Streamlit, etc.)

In short, it is a project that will require a real investment: at least a third of your time spent on training will be on this project .

The project is supervised by a DataScientest mentor who will regularly discuss with you to ensure your progress and to guide you.

According to the data managers of the largest CAC 40 groups, knowing how to communicate both orally and in writing is more important than mastering the core business of the company for a Data Scientist and therefore for a Machine Learning Engineer. .

We have therefore taken this into account in our curriculum which also emphasizes Soft Skills with:

  • The written and oral defenses of the project, which allow these skills to be developed.
  • Masterclasses dedicated to project management and the interpretation of results.
  • Masterclasses on best practices in “data visualization” and on dedicated tools.

 

You will also have the opportunity to participate in CV workshops and career coaching via careers managers and the DataScientest HR team.

French leader in Data Science training, DataScientest enjoys a great reputation among companies who entrust it with the data science training of their teams. This confidence has forged the recognition of his diplomas.
Accordion Content

As for the Data Scientist, Data Analyst or Data Engineer, the salary to which a Machine Learning Engineer can claim varies according to his experience, the company that hires him and the city of exercise of his professional activity.

On average, a Junior Machine Learning Engineer can earn between €35,000 and €40,000/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 one hundred thousand euros in the United States!

The demand for work and therefore the job offer in AI and in particular in Machine Learning Engineering is booming. The Machine Learning labor market is even currently in short supply. Companies are becoming more and more aware of the added value of Machine Learning to take full and more effective advantage of their data and are struggling to find the right profiles. This opens the doors even more to candidates and puts upward pressure on salaries!

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

Accordion Content

Beta tests are available for our alumni in order to gain data knowledge even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. To keep in touch and allow former students to communicate with each other, DataScientest has set up a  group of alumni on LinkedIn  who share and discuss various themes around Data Science.

The  DatAlumni community  is a  LinkedIn community  that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope which will put alumni in contact, this one will include the company and the position of each one.

Initially, DataScientest supported the data transition of companies . This has made it possible to create strong links between the major groups which have ensured the growth of our structure . 

Subsequently, they are 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 CAC 40 groups . Even if they had tight budget constraints, only 4% believe they would downsize their data scientist workforce; by comparison, 28% would still seek to increase their number by more than 20%

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

With our experience with large companies, we regularly organize recruitment fairs with our partner companies, addressed to all our students and alumni.

On the first day of your entry into training, a platform dedicated to career services containing all the workshops essential to your job search will be presented to you.

You can access it continuously, even after the end of your training.

Mathilde and Morgane, our career managers are entirely dedicated to you throughout your training. It is possible to make an appointment individually with one of them in order to accompany you and answer any questions you may have about your career plan.

In addition to this, career workshops are organized every month:

  • A workshop to help you write a good CV and data-oriented Linkedin
  • A workshop to help you strategize your job search with different topics on presentation, career change, salary negotiation and technical test training.

 

In addition to these subjects, there are other workshops to be defined according to individual needs. On the other hand, concrete actions are implemented to support you in your job search: recruitment fair organized by DataScientest with its partner companies, organization of Webinars with data experts, communication actions to boost your visibility (CV competition, DataDays, project articles published on the blog and external reference media). 

To find out about all of DataScientest’s career support actions, click on this link .

Accordion Content

The Machine Learning Engineer appeared with the evolution of needs and missions given to Data Science teams. Professional experts in the data world have faced a demand for technical knowledge related to model training collaboration. Companies are therefore facing growing needs for automation and deployment of predictive models on the cloud.

Organizations, companies, public sectors and associations increasingly need to provide their customers, partners or public with predictive models based on Machine Learning.

The objective of the Machine Learning Engineer is to take a prediction model based on Machine Learning, to condition it (thanks to APIs and containers), to test it (with unit tests) and to set up its deployment on a Kubernetes cluster.

The Machine Learning Engineer is a versatile expert who occupies a major place in Data Science teams.

So don’t hesitate any longer and join our expert course!

The Machine Learning Engineer shares common missions with the Data Scientist. Like him, he develops Machine Learning algorithms in order to solve classification and recommendation problems, but the Machine Learning Engineer can deploy these models, for example, on the Cloud.

A Machine Learning Engineer can evolve in many sectors. He will notably work on anomaly detection, fraud detection, search ranking, text/sentiment classification, spam detection and many other aspects of Machine Learning.

A Machine Learning Engineer is also responsible for guiding the use of technologies, data and Machine Learning.

The missions are thus diverse and varied. In particular, he applies software development practices and standards in order to develop robust and long-lasting solutions. To do this, he must maintain an active role in each part of the development life cycle of Machine Learning-based solutions. It is also necessary for him to guide non-technical teams in understanding best practices to guide the development of these solutions.

Here are some missions of the Machine Learning Engineer:

  • Put Machine and Deep Learning algorithms into production;
  • Master the techniques of manipulation and pre-processing of data;
  • Develop APIs;
  • Automate the training of predictive models and deployment on the Cloud (containerization).
Accordion Content

In order to join the Machine Learning Engineer training, you must have obtained a Bac+3 level diploma in mathematics, statistics or science (European level 6). You also need to have good communication skills.

These prerequisites exist because although the training is centered on data science, and not mathematics, these are necessary for a good understanding of the logical principles of the concepts covered.

In addition, programming is essential to the development and production of any Machine Learning project. For this, an appetite for programming is appreciated. As terminology, documentation and online resources are primarily in English, we recommend that you are fluent in the English language.

After your registration on the site, we contact you for the first time for a presentation of what DataScientest is, what we can offer you but also your background and your wishes. The idea is to align your expectations from that moment with our training courses.

You can of course also make an appointment directly by clicking here ! 

Then we redirect you to a technical positioning test that we use to know what bases you are starting with . These are essentially mathematical questions dealing mainly with basic notions (L1/L2 level) in probability, statistics, analysis and algebra.

Once this test has been passed, a member of the admissions team will contact you to discuss your results and validate your professional project, your motivations, and finally the relevance of your educational project.

Once your project is confirmed, you go to the registration phase with our teams who will take care of initiating your Machine Learning Engineer training and setting it up with you in all its aspects.

At the end of your training you will know:

  • Prepare data, set up a data analysis strategy and master Python programming;
  • Train predictive Machine Learning models and automate the updating of these models;
  • Manipulate neural networks and implement Computer Vision or Natural Language Processing algorithms;
  • Exploit artificial intelligence models in a production context;
  • Implement a classic Machine Learning algorithm and use it on text data
  • Deploy models on cloud solutions;
  • Build Data Visualizations allowing the valuation of results.

The course is based on sequences themselves divided into modules that allow you to master the skills deemed necessary for the profession of Machine Learning Engineer. 

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

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

For a total hourly volume of 550 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 leads a course and answers all your questions.

Beyond the platform and the masterclasses, you will work on a common thread project which will confirm the skills 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 10 hours per week for 13 months. Make an appointment to find out more

Obviously ! And who better to provide support than our teachers , who also designed the program. They are available and attentive to any questions, whether theoretical or practical. 

They also follow the progress of learners closely so that everyone is neither neglected nor demotivated. Each disconnection of a certain duration will be communicated to your cohort manager who will then hear from you so as not to leave you in difficulty!

Finally, our papers, exams and defenses are also corrected by hand by our panel of qualified teachers: everything is done so that everyone can progress effectively at their own pace . At DataScientest we are convinced that only personalized monitoring ensures quality learning!

Throughout your training, and as your skills are developed, you will lead a project for an artificial intelligence solution.

This project may come from our catalog, composed of various subjects, with technical business issues and using rich and complex data. You can also propose a personal project, as long as the data is accessible and our teaching team validates it.

It is an extremely effective way to move from theory to practice and to ensure that you apply the themes covered in class.

It is also a project highly appreciated by companies because it ensures the quality of the training and the knowledge acquired at the end of the Machine Learning Engineer training since the use of soft-skills is also very present.

  • Ability to transmit information
  • Know how to present and popularize your work
  • Know how to highlight data with interactive tools (Dashboard, Streamlit, etc.)

In short, it is a project that will require a real investment: at least a third of your time spent on training will be on this project .

The project is supervised by a DataScientest mentor who will regularly discuss with you to ensure your progress and to guide you.

According to the data managers of the largest CAC 40 groups, knowing how to communicate both orally and in writing is more important than mastering the core business of the company for a Data Scientist and therefore for a Machine Learning Engineer. .

We have therefore taken this into account in our curriculum which also emphasizes Soft Skills with:

  • The written and oral defenses of the project, which allow these skills to be developed.
  • Masterclasses dedicated to project management and the interpretation of results.
  • Masterclasses on best practices in “data visualization” and on dedicated tools.

 

You will also have the opportunity to participate in CV workshops and career coaching via careers managers and the DataScientest HR team.

French leader in Data Science training, DataScientest enjoys a great reputation among companies who entrust it with the data science training of their teams. This confidence has forged the recognition of his diplomas.
Accordion Content

As for the Data Scientist, Data Analyst or Data Engineer, the salary to which a Machine Learning Engineer can claim varies according to his experience, the company that hires him and the city of exercise of his professional activity.

On average, a Junior Machine Learning Engineer can earn between €35,000 and €40,000/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 one hundred thousand euros in the United States!

The demand for work and therefore the job offer in AI and in particular in Machine Learning Engineering is booming. The Machine Learning labor market is even currently in short supply. Companies are becoming more and more aware of the added value of Machine Learning to take full and more effective advantage of their data and are struggling to find the right profiles. This opens the doors even more to candidates and puts upward pressure on salaries!

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

Accordion Content

Beta tests are available for our alumni in order to gain data knowledge even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. To keep in touch and allow former students to communicate with each other, DataScientest has set up a  group of alumni on LinkedIn  who share and discuss various themes around Data Science.

The  DatAlumni community  is a  LinkedIn community  that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope which will put alumni in contact, this one will include the company and the position of each one.

Initially, DataScientest supported the data transition of companies . This has made it possible to create strong links between the major groups which have ensured the growth of our structure . 

Subsequently, they are 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 CAC 40 groups . Even if they had tight budget constraints, only 4% believe they would downsize their data scientist workforce; by comparison, 28% would still seek to increase their number by more than 20%

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

With our experience with large companies, we regularly organize recruitment fairs with our partner companies, addressed to all our students and alumni.

On the first day of your entry into training, a platform dedicated to career services containing all the workshops essential to your job search will be presented to you.

You can access it continuously, even after the end of your training.

Mathilde and Morgane, our career managers are entirely dedicated to you throughout your training. It is possible to make an appointment individually with one of them in order to accompany you and answer any questions you may have about your career plan.

In addition to this, career workshops are organized every month:

  • A workshop to help you write a good CV and data-oriented Linkedin
  • A workshop to help you strategize your job search with different topics on presentation, career change, salary negotiation and technical test training.

 

In addition to these subjects, there are other workshops to be defined according to individual needs. On the other hand, concrete actions are implemented to support you in your job search: recruitment fair organized by DataScientest with its partner companies, organization of Webinars with data experts, communication actions to boost your visibility (CV competition, DataDays, project articles published on the blog and external reference media). 

To find out about all of DataScientest’s career support actions, click on this link .

Are you interested?