The Machine Learning Engineer

The tittle “Machine Learning Engineer” is becoming one of the most looked for denominations on the tech industry. Lets explore why!

Data Scientist vs Machine Learning Engineer

Machine Learning Engineer vs Data Scientist

First, lets discuss the differences, from our point of view, between Machine Learning engineer and Data Scientist. Take into account that these roles don’t have closed definitions and can vary depending on the point of view of the reader, so our description and comparison is not a ground truth that should be taken as an inamovible statement.

For us, a Data Scientist has a wider set of functions than a Machine Learning engineer, being able to perform a larger list of tasks: from extracting the data from its cloud or local storage, to analysing/cleaning this data, building machine learning models, and sometimes even deploying them, although this last task is usually more in the realm of the Data Engineer.

It can vary depending on the person/company, but overall Data Scientists have a more scientific expertise from statistics and mathematical modelling than a Machine Learning engineer. They should have a great experience in analysing data and deriving insights from it through statistical methods, and how to perform exploratory data analysis and visualisations.

So what is a Machine Learning engineer then?

The strength of the Machine Learning engineer comes in one of the tasks that the Data Scientist can do, but that he might not be an expert on: building machine learning models, having expertise on the different frameworks and platforms, and knowing how these different frameworks are integrated into the productive environment. They must also be very fluent on the diagnosis of these models, knowing why they might not be getting the results they should, and being able to solve and debug these issues, with the final goal of building the best possible model with the available resources.

Machine Learning engineers know where to train the models, when it is worth it to train a model using available data or when to use an available pre-trained model, and also have a deep knowledge of tools like AutoML, BigML, and the Machine Learning offers of the big market players like Amazon, Google and Microsoft.

Why are ML engineers needed?

The reason why Machine learning engineers are needed is the following: if a company wants to streamline and optimise their procedure for building and deploying Artificial Intelligence applications, having a role in the team that exceeds in the model building and value phase is very useful.

In times where there are lots of work to do, data engineers can focus on building the architecture of the systems, data scientist can focus on analysing, cleaning, and optimising the data, and the machine learning engineers can focus on using this data to build and train models.

When there is less work and everything is flowing smoothly, data scientists can do a project almost from end to end, and machine learning engineers can give them advice on certain points if this is needed.

Also, having this division of roles helps individuals know exactly what their task is, and become experts in it, and removes the uncomfortable times when there is a certain job to execute and nobody knows for sure who should do it.

How do I become a Machine Learning engineer?

As mentioned before, an ML engineer is someone who may lack the in-depth scientific skills of a data scientist, but has other in-demand skills including programming, ML & DL frameworks, AutoML, MLOps, and data engineering.

He has to have knowledge of libraries like Sckit-Learn, Tensorflow and Keras, of the previous frameworks, and of the diverse platforms that allow the implementation and integration on production systems of Machine Learning models.

A model sitting on a Jupyter Notebook without being used for anything is useless, even if it has a 99% accuracy! Become a ML engineer and put it on production!

If you want to become a Machine Learning Engineer, we check out our section on Machine Learning books, and from those, we would specifically recommend Python Machine Learning, as it is very practical and implementation oriented.

Once you are ready to become one, and want to start scouting for an ML engineer job, you can check out awesome sites like Jooble to see if there is something that suits you. See Jooble Machine Learning engineer jobs.

jooble machine learning engineer

That is all, thanks a lot for reading How to Learn Machine Learning, follow us on Twitter and have a wonderful day!

Tags: Data Scientist jobs, Machine Learning jobs.

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