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The goal of an MLOps team is to automate the use of Machine Learning models in the company’s software system. In other words, it is about automating the complete steps of the “ML” workflow without any manual intervention.
Acquire the essential tools of advanced programming.
Be able to build, deploy and secure an API.
Ensure, monitor, operationalize and manage AI models used in production.
If you live in France, you can benefit from several financing options:
Don’t hesitate to make an appointment with one of our consultants to find the funding that best suits you!
If you are living in Germany you have multiple ways to finance your training courses depending on your professional situation.
Employees:
Unemployed, job seekers, self-employed or students:
The DataScientest team will help you find the best funding for your personal circumstances.
Different types of financing can be applied depending on your current situation:
For further information, please check this page and book an appointment with our team.
Patricia Jan, Data Scientist and alumni of DataScientest, tells you today in a video about her experience of further training and how data plays a role in her everyday life!
🎉 Would you also like to get started with one of our courses? New courses start every month and good news: We’ve just launched the DevOps course to extend our Data Science trainings!
Until a few years ago, to leverage data, companies hired only Data Scientists and Machine Learning Engineers. These professionals could build predictive models, allowing companies to leverage locally and make (some) important decisions.
However, Machine Learning projects failed when they were supposed to go into production. Companies missed opportunities and customers were dissatisfied.
Data Scientists focus exclusively on building Machine Learning Models. Once in the hands of the end user, there is no system in place to ensure that these models will work properly in the real world and in an environment different from the one in which they were trained.
However, the real world is unpredictable and constantly changing. Therefore, the performance of a Machine Learning model can change drastically from one day to the next.
For example, the slightest change in the training database can affect the accuracy of the model. This phenomenon is called “data drift” and must be detected quickly to update the model before it becomes biased.
Similarly, seasonality causes regular and predictable changes to the data at specific time intervals. Machine Learning Models need to be regularly updated with these seasonal changes.
In addition, many Machine Learning models are not suitable for production use because they cannot handle the large volumes of data entering the system in real time.
These phenomena, related to a lack of procedures for using machine learning models, can have an extremely negative impact on the performance of algorithms in production. To address this problem, the profession of MLOps (Machine Learning Operations) was created.
An MLOps has both Machine Learning and operations skills. His job is to support the workflow that follows the construction of the Machine Learning Model.
The MLOps job involves the deployment and ongoing maintenance of Machine Learning models. This job is at the crossroads of Machine Learning, Data Engineering and DevOps.
One of his tasks is to refactor the Data Scientists’ code to make it ready for production. He/she must ensure that the code changes made by the Data Scientists are delivered to production in a stable and timely manner. He/she has a deep knowledge of machine learning algorithms. He is also an expert in DevOps: the software development methodology based on collaboration between development and operations teams and on workflow automation.
The MLOps engineer role is very similar to the DevOps Engineer role but the main difference is the use of Machine Learning Models. They also master automation through CI/CD (continuous integration and continuous deployment/delivery) pipelines.
The data science teams build the Machine Learning Model, but the MLOps handles the code changes for deployment. It integrates the Machine Learning Model with the company’s existing data infrastructure and optimizes it to handle Big Data in a production environment.
After your registration on our website, we will contact you and give you a presentation of DataScientest. We will discuss your background and your wishes and give you an overview of what we offer. The idea is to align your expectations with our training courses.
Afterwards, we will redirect you to our placement test. It will allow us to know your background and your mathematical / data science level.
Once you have passed this test, 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 will go through the registration phase with our teams who will initiate your MLOps training and set it up with you in all its aspects.
Of course you can always book a meeting with one of our counselors now, click here!
DataScientest is the only company offering hybrid training.
This translates into 85% learning on the coached platform and 15% masterclass session by videoconference in order to combine flexibility and rigor without compromising on one or the other. It is a carefully considered choice that motivates our pedagogy to allow quality learning while maintaining your motivation.
Discover our pedagogical approach in this video.
Obviously ! And who better to provide support than our teachers, who also designed the program. They are available and attentive to all questions, whether theoretical or practical and will be able to demonstrate pedagogy in their response.
In addition, to ensure everyone’s completion and commitment, our teachers follow your progress closely. As soon as you stop logging in for an extended period, your cohort manager will hear from you: we won’t let you down!
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!
In order to integrate the MLOps training, the minimum requirement is intensive experience as a Data Scientist.
Moreover, programming is essential to the production of any Machine Learning project.
A placement test in the form of MCQs will allow us to assess your level before you enter the course.
Since the terminology, documentation and online resources are mainly in English, we recommend that you are comfortable with the English language.
The curriculum is structured in several modules, which are divided into different learning units. This way, you can acquire the skills that are relevant to the Machine Learning Engineer profession.
Thanks to our studies and surveys with our DataBoss communities, graduates, etc., our Data Science experts have been able to build a curriculum that exactly matches the skills recruiters are looking for.
Throughout the course, you will master the following tools : Python, Git and Github, Flask, FastAPI, Docker, Kubernetes, Airflow …
With a total of 150 hours, 85% of your training will take place on a personalized coaching platform, while the remaining 15% will be in the form of masterclasses, where an experienced teacher will guide the course and answer all your questions.
Beyond the platform and the masterclasses, you will work on a project that will confirm the acquired skills and allow you to be directly operational.
The MLOps course is only available in the “part-time” format, which requires a commitment of approximately 8 to 10 hours per week for 4 months.
At the end of your training you will:
Throughout your training, and as your skills are developed, you will carry out a data science project.
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 Data Scientist training since the use of soft-skills is also very present.
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.
Thanks to the validation of the competences developed during our Data MLOps training, you can obtain the nationally recognized certification “Artificial Intelligence Project Manager”, opening many doors in the job market.
As the B2B leader in Data Science training, DataScientest is highly regarded by companies that entrust it with the training of their teams in Data Science.
As for the Data Scientist, Data Analyst or Data Engineer, the salary that a Machine Learning Engineer can expect varies according to his experience, the company that hires him and the city where he works.
On average, a junior MLOps 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 be over 100,000 € in the United States!
The demand of work and therefore the supply of jobs in AI and especially in MLOps is exploding. The job market in Machine Learning is even currently in a shortage. Companies are becoming more and more aware of the added value of Machine Learning in order to take full and more efficient advantage of their data and are struggling to find the right profiles. This opens the doors to candidates and increases the pressure on salaries!
Today, there are hardly any sectors that do not compete for talent. The applications of Machine Learning touch the fields of education, health, industry, IT, etc. Moreover, they are as varied as the data itself: image and speech recognition, customer insight, risk management and fraud prevention.
On the first day of your training, you will be presented with a platform dedicated to career services, containing all the workshops essential to your job search.
You can access it continuously, even after the end of your training.
Estelle and Morgane, our career managers, are entirely dedicated to you throughout your training. It is possible to make an individual appointment with one of them to accompany you and answer your questions about your career plan.
Each month :
On the other hand, concrete actions are set up to help you in your job search: the recruitment fair organized by DataScientest with its partner companies, organization of webinars with data experts, communication actions to boost your visibility (CV contest, DataDays, project articles published on the blog and external reference media).
Finally, you should know that a specific Slack channel has been set up for people looking for a job, on which all the information about workshops and job offers are transmitted.
To learn more about DataScientest’s career support activities, click on this link.
If we refer to the Data managers of the big CAC 40 groups, it is more important for an MLOps to know how to communicate in writing and orally, than to master the company’s own business.
Therefore, we have integrated into our curriculum modules that allow you to practice these soft skills with :
You will also have the opportunity to participate in CV workshops and career coaching via DataScientest’s career managers.
Newsletters developed by our data scientists are sent regularly and are a reliable source of specialized data science information.
At the same time, 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 on this page for the benefit of all. You will be invited to join it at the beginning of your training.
This makes it possible to create strong links with the major groups, which have ensured the growth of our structure.
Initially, DataScientest supported the data transition of companies. This has made it possible to create strong links with 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%.
Thanks to our experience with large companies, we regularly organize recruitment fairs for all our students and alumni with our partner companies.
Until a few years ago, to leverage data, companies hired only Data Scientists and Machine Learning Engineers. These professionals could build predictive models, allowing companies to leverage locally and make (some) important decisions.
However, Machine Learning projects failed when they were supposed to go into production. Companies missed opportunities and customers were dissatisfied.
Data Scientists focus exclusively on building Machine Learning Models. Once in the hands of the end user, there is no system in place to ensure that these models will work properly in the real world and in an environment different from the one in which they were trained.
However, the real world is unpredictable and constantly changing. Therefore, the performance of a Machine Learning model can change drastically from one day to the next.
For example, the slightest change in the training database can affect the accuracy of the model. This phenomenon is called “data drift” and must be detected quickly to update the model before it becomes biased.
Similarly, seasonality causes regular and predictable changes to the data at specific time intervals. Machine Learning Models need to be regularly updated with these seasonal changes.
In addition, many Machine Learning models are not suitable for production use because they cannot handle the large volumes of data entering the system in real time.
These phenomena, related to a lack of procedures for using machine learning models, can have an extremely negative impact on the performance of algorithms in production. To address this problem, the profession of MLOps (Machine Learning Operations) was created.
An MLOps has both Machine Learning and operations skills. His job is to support the workflow that follows the construction of the Machine Learning Model.
The MLOps job involves the deployment and ongoing maintenance of Machine Learning models. This job is at the crossroads of Machine Learning, Data Engineering and DevOps.
One of his tasks is to refactor the Data Scientists’ code to make it ready for production. He/she must ensure that the code changes made by the Data Scientists are delivered to production in a stable and timely manner. He/she has a deep knowledge of machine learning algorithms. He is also an expert in DevOps: the software development methodology based on collaboration between development and operations teams and on workflow automation.
The MLOps engineer role is very similar to the DevOps Engineer role but the main difference is the use of Machine Learning Models. They also master automation through CI/CD (continuous integration and continuous deployment/delivery) pipelines.
The data science teams build the Machine Learning Model, but the MLOps handles the code changes for deployment. It integrates the Machine Learning Model with the company’s existing data infrastructure and optimizes it to handle Big Data in a production environment.
After your registration on our website, we will contact you and give you a presentation of DataScientest. We will discuss your background and your wishes and give you an overview of what we offer. The idea is to align your expectations with our training courses.
Afterwards, we will redirect you to our placement test. It will allow us to know your background and your mathematical / data science level.
Once you have passed this test, 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 will go through the registration phase with our teams who will initiate your MLOps training and set it up with you in all its aspects.
Of course you can always book a meeting with one of our counselors now, click here!
DataScientest is the only company offering hybrid training.
This translates into 85% learning on the coached platform and 15% masterclass session by videoconference in order to combine flexibility and rigor without compromising on one or the other. It is a carefully considered choice that motivates our pedagogy to allow quality learning while maintaining your motivation.
Discover our pedagogical approach in this video.
Obviously ! And who better to provide support than our teachers, who also designed the program. They are available and attentive to all questions, whether theoretical or practical and will be able to demonstrate pedagogy in their response.
In addition, to ensure everyone’s completion and commitment, our teachers follow your progress closely. As soon as you stop logging in for an extended period, your cohort manager will hear from you: we won’t let you down!
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!
In order to integrate the MLOps training, the minimum requirement is intensive experience as a Data Scientist.
Moreover, programming is essential to the production of any Machine Learning project.
A placement test in the form of MCQs will allow us to assess your level before you enter the course.
Since the terminology, documentation and online resources are mainly in English, we recommend that you are comfortable with the English language.
The curriculum is structured in several modules, which are divided into different learning units. This way, you can acquire the skills that are relevant to the Machine Learning Engineer profession.
Thanks to our studies and surveys with our DataBoss communities, graduates, etc., our Data Science experts have been able to build a curriculum that exactly matches the skills recruiters are looking for.
Throughout the course, you will master the following tools : Python, Git and Github, Flask, FastAPI, Docker, Kubernetes, Airflow …
With a total of 150 hours, 85% of your training will take place on a personalized coaching platform, while the remaining 15% will be in the form of masterclasses, where an experienced teacher will guide the course and answer all your questions.
Beyond the platform and the masterclasses, you will work on a project that will confirm the acquired skills and allow you to be directly operational.
The MLOps course is only available in the “part-time” format, which requires a commitment of approximately 8 to 10 hours per week for 4 months.
At the end of your training you will:
Throughout your training, and as your skills are developed, you will carry out a data science project.
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 Data Scientist training since the use of soft-skills is also very present.
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.
Thanks to the validation of the competences developed during our Data MLOps training, you can obtain the nationally recognized certification “Artificial Intelligence Project Manager”, opening many doors in the job market.
As the B2B leader in Data Science training, DataScientest is highly regarded by companies that entrust it with the training of their teams in Data Science.
As for the Data Scientist, Data Analyst or Data Engineer, the salary that a Machine Learning Engineer can expect varies according to his experience, the company that hires him and the city where he works.
On average, a junior MLOps 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 be over 100,000 € in the United States!
The demand of work and therefore the supply of jobs in AI and especially in MLOps is exploding. The job market in Machine Learning is even currently in a shortage. Companies are becoming more and more aware of the added value of Machine Learning in order to take full and more efficient advantage of their data and are struggling to find the right profiles. This opens the doors to candidates and increases the pressure on salaries!
Today, there are hardly any sectors that do not compete for talent. The applications of Machine Learning touch the fields of education, health, industry, IT, etc. Moreover, they are as varied as the data itself: image and speech recognition, customer insight, risk management and fraud prevention.
On the first day of your training, you will be presented with a platform dedicated to career services, containing all the workshops essential to your job search.
You can access it continuously, even after the end of your training.
Estelle and Morgane, our career managers, are entirely dedicated to you throughout your training. It is possible to make an individual appointment with one of them to accompany you and answer your questions about your career plan.
Each month :
On the other hand, concrete actions are set up to help you in your job search: the recruitment fair organized by DataScientest with its partner companies, organization of webinars with data experts, communication actions to boost your visibility (CV contest, DataDays, project articles published on the blog and external reference media).
Finally, you should know that a specific Slack channel has been set up for people looking for a job, on which all the information about workshops and job offers are transmitted.
To learn more about DataScientest’s career support activities, click on this link.
If we refer to the Data managers of the big CAC 40 groups, it is more important for an MLOps to know how to communicate in writing and orally, than to master the company’s own business.
Therefore, we have integrated into our curriculum modules that allow you to practice these soft skills with :
You will also have the opportunity to participate in CV workshops and career coaching via DataScientest’s career managers.
Newsletters developed by our data scientists are sent regularly and are a reliable source of specialized data science information.
At the same time, 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 on this page for the benefit of all. You will be invited to join it at the beginning of your training.
This makes it possible to create strong links with the major groups, which have ensured the growth of our structure.
Initially, DataScientest supported the data transition of companies. This has made it possible to create strong links with 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%.
Thanks to our experience with large companies, we regularly organize recruitment fairs for all our students and alumni with our partner companies.
