At the crossroads between the Data Scientist and Data Engineer, the job of Machine Learning Engineer is becoming more and more common in the Data offers. This position is accessible with our Data Science training courses, but what are the specificities of this job, the salaries to which one can aspire, the prospects for development and training, we answer all your questions in this article.
What is a Machine Learning Engineer?
To understand the job of Machine Learning Engineer, you need to understand the needs of Data today. When a company has a project to design a Machine Learning algorithm, there are several phases:
- Collecting data to make the algorithm learn
- Developing the algorithm
- Putting it into production: making the algorithm and its results available to all end customers (internal collaborators, external customers).
These are the three points on which the Machine Learning Engineer will intervene.
As you can imagine, this one is very machine learning oriented but he is not a statistician at heart. Rather, the MLE is an expert in the technologies surrounding the use of Artificial Intelligence at the enterprise level. Here are a few examples of missions on which you can find him:
- Develop a Machine Learning or Deep Learning algorithm using the most popular frameworks such as TensorFlow or Sklearn.
- Collect data to develop a Machine Learning algorithm.
- Designing a cloud architecture to provision Machine Learning algorithms.
- Putting a Machine Learning algorithm into production on an enterprise infrastructure (this can be on the cloud with AWS, GCP or Microsoft Azure).
- Testing the robustness of a Machine Learning algorithm in production.
As you can see, this job is very specialised around AI. However, it should not be confused with the job of Data Scientist or Data Engineer. The Data Scientist, for example, is more knowledgeable about the statistical aspect and will be more efficient in designing a custom algorithm for example. Whereas a Data Engineer is more specialised in the Data infrastructure aspect.
Finally, the Machine Learning Engineer is at the crossroads between these two professions.
At what stage is it found in a Data project?
If we were to summarise the stages of a data project, we could illustrate them in the form of a pipeline. The pipeline is as follows:
- Data collection: You will need to find the data you need for your analyses. It may be in databases, you will have to scrape it from the web or you can get it from your CRM!
- Exploration: Once you have the data, you will have to clean and prepare it. To do this, an exploratory phase is often carried out, known as EDA (Exploratory Data Analysis). The objective is to detect outliers or missing values, for example.
- Exploitation: Now that everything is ready, you can use the data for the objective that has been set. For example, in Data Science, you will develop a Machine Learning algorithm.
- Production: Once you have successfully developed your AI algorithm or solution, you will need to put it into production at enterprise level. This involves different technologies and is a separate step.
As a Machine Learning Engineer, you will mainly work on the last two steps of this pipeline. Therefore, this job requires skills in both the mathematical part and especially in the technological part such as programming.
If you want to become a Machine Learning Engineer, you will have a diverse set of skills to master. Without being an expert in everything, keep in mind that it is important to know both the statistics behind Machine Learning algorithms and the technologies you will need to develop them.
Here are the main concepts you will need to master to become a Machine Learning Engineer:
- Python programming: This is the leading language in Data today and one that you will need to know to advance. You will use it constantly in your work as it is the foundation of most AI technologies. For example, you will use Sklearn, TensorFlow or Pytorch, frameworks that you can use with Python.
- Statistics: There is a mathematical dimension to Data. Although you don't have to be a trained statistician, it is important that you have a solid grounding in statistics (averages, medians, confidence intervals), linear algebra (managing matrices) and functions (derivatives etc.)
- Machine Learning & Deep Learning: Linked to the world of statistics, this will be a subject on which you will work a lot. You will be designing Machine Learning and Deep Learning algorithms, very often in Python for your company's needs.
- SQL database management: When you collect your data, you will need to use SQL. This is also a highly demanded skill among Data Scientists and Data Analysts.
- Putting it into production: Once you have built your algorithm, you will need to put it into production so that it can be used on a large scale. To do this, you will need to be familiar with a range of technologies that will allow you to accomplish this task. You will need to know tools for standardising environments such as Docker and Kubernetes, Amazon Sagemaker for managing Machine Learning algorithms in production, Flask for building APIs and simple web applications to make the algorithm more accessible, and MLflow for standardising the way you design your algorithms.
Depending on the type of company and the sector, salaries vary, but here is an average salary scale:
Training to become a Machine Learning Engineer
The job of Machine Learning Engineer is recent and very specialised, so recruiters are open to profiles with relatively diverse backgrounds. You will still need to train and for this you can choose from three options:
- Online training: is great for getting started and training at your own pace. It is still difficult to have credibility in the market with only online training
- Masters in Data: you will be able to have a more complete training and a state-recognised diploma. However, you will need to invest between €10,000 and €20,000 and at least one year of your time.
- Intensive Bootcamp training: This is the right compromise between the flexibility of an online course and the theoretical depth of a master. Data bootcamps have become a real alternative to traditional training because they are very practical and will teach you skills that are directly applicable in companies. For example, take a look at our intensive Data training courses, ranked as the best Data bootcamp course in France.
Depending on your background, your initial level and your aspirations, you will opt for one type of training rather than another. We have written a whole article to help you choose the Data training that suits you. Don't hesitate to have a look at it.
Finally, you will see that there are very few training courses called "Machine Learning Engineer" because this profession is very specialised and still not very widespread.
We advise you to look for a Data Science course with a "production" component with mention of the technologies we have put in the competencies above.
The prospects for development
When you start your career in Data, you will find that there are many opportunities open to you. Once you have gained experience as a Machine Learning Engineer, you will be able to aspire to positions of greater responsibility:
- Lead Machine Learning Engineer
- Chief Data Officer
- Chief Technology Officer
You can also make a more horizontal progression with trades such as :
- Software Engineer
- Data Engineer
- Data Scientist
Remember: the field of data offers so many possibilities that you won't have enough of a career to discover all its facets.
Our advice for becoming a Machine Learning Engineer
If you are interested in getting started in data and are interested in becoming a Machine Learning Engineer, here are some tips that may help you:
- Put forward your technical skills but also your business skills: A good AI algorithm always learns on relevant data. You will have to learn how to select and process them as well as possible with the business teams so that your algorithms have acceptable performances
- Do projects: if you don't have work experience, that's okay! You can get some by doing open source projects on Kaggle for example. Don't hesitate to do as many as you can so you can put them in your portfolio.
- Build up your skills gradually: A useful and effective strategy is to start with jobs such as Data Analyst or Junior Software Engineer to gain initial experience, either in Data or in development, and then move on to the Machine Learning Engineer job. Beware, however, that the Data Analyst does not really have as much of a Tech component as a Software Engineer. You will probably have to work on side projects to work on these types of skills. Similarly, the Software Engineer job does not necessarily have a Data component. You will have to develop it yourself.
- Highlightyour background: If you have experience in other fields, it is important to highlight this. For example, you may have experience in marketing or finance and want to change careers. All your past experience is valuable and it will give you a "business" advantage over other candidates who only have technical skills.