Pratique du Shadowing: Is ML Engineering Dead? (AI vs ML Engineer) - Apprendre l'anglais à l'oral avec la vidéo

One of the most confusing questions in tech right now is what is the difference between a AI engineer

and a machine learning engineer?

Both are six-figure jobs, but if you choose the wrong one, you can spend months, if not years,

learning the wrong skills for that particular job that you are after.

As a practicing machine learning engineer, I want to outline the key differences between the two roles,

so that way you can make the correct choice for your career path.

Let's get into it.

In a nutshell, an AI engineer is basically a software engineer,

but someone who specializes in the use and integration of foundational Gen AI models,

like Claude, BERT and the regular GPT model.

They don't build these models directly from scratch but rather use them to serve a certain purpose.

On the other hand a machine learning engineer is someone who actually builds models from scratch

or using basic libraries and deploys them in end to end systems.

These models that a machine learning engineer builds are mainly more traditional machine learning models like gradient booster trees

or neural networks

but sometimes they can also work on gen ai models as

well what i find funny about this naming convention is

that machine learning is actually a subfield of ai

so an ai engineer at least by today's definition is actually a gen ai engineer

so as you can see it's not very crystal clear which is why the question is so confusing.

Anyway, enough of me being pedantic, let's explain what these roles do in a bit more detail.

As I just mentioned,

you should think of an AI engineer as essentially a software engineer with a specialism in using Gen AI models

and using those models more to build a product rather than

building these llms directly from scratch ai engineers mainly work with something called foundational models

which are really big neural networks that are trained on oceans of data

that is like text image audio and video the most kind of popular gen ai

or foundational model is obviously the ChatGPT with its GPT series.

As I said before, AI engineers don't build or train these foundational models.

They rather integrate them to traditional software products.

For example, they may embed a foundational model as a chatbot on a shopping website

so that customers can quickly find what they're after on

that particular website or they may add it to a coding IDE as a coding assistant like a cursor is.

So what skills do you need to become an AI engineer?

Well this role is evolving very quickly and

so the requirements change pretty much every quarter

but let me give you a list of all the key things that you need to know.

What you mainly need to kind of be aware of or at least learning is all the latest developments in LLMs,

neural networks and pretty much just how the AI industry is moving.

To give you a concrete list, you should have solid software engineering skills,

skills in Python, SQL and other back-end languages like Java or Go,

CICD, Git and GitHub, LLMs and Transformers, RAG, prompt engineering, foundational models,

fine tuning and the model context protocol.

So as I said, it's basically all the fundamental software engineering skills, plus that extra knowledge about Gen AI,

LLMs, and basically all the cutting edge AI that's currently happening at the moment.

If you are looking to become an AI engineer, then I recommend you check out the AI engineer career track from 365 Data Science,

who are kindly sponsoring this video.

This career track will teach you everything you need to know to land a career as an AI engineer, even if you're a complete beginner.

It will teach you the basics of Python, how to work with data using pandas, what are LLMs and how do they work, how to use vector databases,

and further courses that you can elect to further solidify your understanding on a range of topics like model deployment, machine learning, and deep learning.

You will also develop portfolio projects along the way to solidify your understanding.

Learners from 365 Data Science now work at top tech companies like Amazon, Meta and Google.

So you'll be in great hands.

Not to mention, you'll get an AI engineer certificate that you can showcase to prospective employers.

These certifications are also accredited by leading industry bodies, making your application stand out even more.

I will leave all of this linked in the description below for you to check out.

A machine learning engineer focuses on building machine learning models

and then deploying them into production systems systems it initially came

from the software engineering role where there was demand for software

engineers with machine learning skills kind of similar how there is

demand now for software engineers with ai engineering skills the significant distinction between a machine learning engineer

and an ai engineer is that a machine learning engineer builds machine learning models directly from scratch.

However, these models are more specific and narrower in exactly what they solve.

For example, you may build a machine learning model

that detects credit card fraud or a recommendation system for a social media app.

But as you can see, these models are very targeted and have a very specific use case.

Whereas the models an AI engineer works with are generative

and more foundational and

because they're foundational they can be used on a wide variety

of tasks like chat gpt it can be used for loads of things

but the models built by machine learning engineers like i just said a lot more specific

so there is a difference in the types of models

and the use case of models between the two roles as well there also exists further specialism

and further distinction in the machine learning engineer role For example, you could also be a machine learning hardware engineer, a machine learning platform engineer,

or a machine learning infrastructure engineer.

These roles are kind of out of the scope for this video, and these roles are something that you kind of get into after like five years in the field.

And like I said, they're kind of out of scope, but I'm just mentioning them here so that you're aware of it.

As a practicing machine learning engineer myself, I can tell you the exact things that you need to know.

So obviously to start, you need to know Python and SQL.

However, some companies may require other languages.

For example, in my current role, I'm also learning Rust.

You also need to know Git and GitHub, Bash and Z shell, AWS,

Azure and GCP, software engineering fundamentals like CICD, MLOps and Docker,

excellent machine learning knowledge ideally with a specialism in an area like forecasting recommendation systems

or computer vision and solid mathematical understanding of statistics linear algebra

and calculus so as you can see you basically need software engineering skills

but also this kind of wide skill set in machine learning

and maths which makes sense from how i described the distinction between AI engineering and machine learning engineer.

So the question still stands, which one should you choose?

Well let me break down some of the more logistical aspects.

The background for both roles is pretty similar.

Ideally you have a master's in a stem based subject

and you have worked a couple of years as either a data scientist or software engineer.

If you want to become an AI engineer, the software engineer background is probably better than a data scientist.

But I wouldn't worry too much about this.

I will say though that AI engineering is slightly easier to get into,

mainly because learning how to use Gen AI is a slightly easier learning curve than understanding all the complex maths

and machine learning theory that is required to become a machine learning engineer

but that is just my opinion and i'm sure other people may disagree with

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that machine learning engineering is the more established role but that's mainly

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because gen ai foundational models haven't been around for that long

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and the current demand or the popularity of ai is increasing

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so the demand for ai engineers is currently skyrocketing you do need to be careful though

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because titles in this industry from first-hand experience are very vague

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for example i have people working in my company who are machine learning engineers

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but they specifically and only work on gen ai

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and foundational models yet their title is machine learning engineers

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or machine learning engineer but they do an ai engineer job

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so i wouldn't necessarily focus on titles too much

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but rather focus on the type of work that you'll be doing

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and make sure you read the job description that is the key thing

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when it comes to pay according to levels fyi the median salary of machine learning engineer

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in the uk is 105 000 pounds

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and for an ai engineer is 75 000 pounds

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but i would take this with a big pinch of salt

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because these job titles are mainly large tech companies

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and as i just mentioned titles can be misleading

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and it's kind of a blurry line of what classes as machine learning engineer and AI engineer.

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So I wouldn't base your career decision simply on this salary.

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Either way, both pay pretty well regardless.

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So what is your final choice?

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In my opinion, just go with what you're most interested in and what you're drawn to the most.

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That's how I became a data scientist and ultimately a machine learning engineer.

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If you love maths

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and understanding how things work under the hood then machine learning engineering is clearly the choice for you

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but let's say you're really interested in this ai kind of wave at the moment

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and you've been doing a lot of reading in that specific area

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and you really enjoy just shipping products that are ai focused

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and you don't care too much about the underlying maths then obviously an ai engineer

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or an ai engineering career track is best for you

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but as i said both jobs are clearly in high demand the demand is only growing they pay you well

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so either choice you make you really can't go wrong however suppose you do feel

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that greater pull to become a machine learning engineer

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if that's the case then i recommend you check out this

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video where i explain exactly how i would become a machine learning engineer

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if i was starting again i'll see you there

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278 phrases

Vocabulaire et conseils d’expression pour cette leçon

Cette leçon d’expression orale de niveau B2 s’appuie sur la vidéo « Is ML Engineering Dead? ». Les mots qui reviennent le plus souvent : engineer, learning, machine, model, software. Cette vidéo contient 136 phrases et 1774 mots à répéter en shadowing. La partie parlée dure 11:56. Le locuteur parle à un rythme régulier d’environ 149 mots par minute, confortable pour le shadowing. 82 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.

Vocabulaire clé de cette vidéo

Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :

Mot	Prononciation	Sens
mathematics nom	/mæθ(.ə)ˈmæt.ɪks/	mathématiques
scratch verbe	/skɹæt͡ʃ/	gratter
neural adjectif	/ˈnʊɹəl/	neural
distinction nom	/dɪˈstɪŋkʃən/	distinction, différence
solidify verbe	/səˈlɪdɪˌfaɪ/	solidifier
confuse verbe	/kənˈfjuːz/	rendre perplexe, confondre
ideally adverbe	/aɪˈdi.ə.li/	idéalement
recommendation nom	/ˌɹɛkəmɛnˈdeɪʃən/	recommandation
accredit verbe	/əˈkɹɛd.ɪt/	accréditer
blurry adjectif	/ˈblɜːɹi/	flou
chatbot nom	/ˈtʃætbɑt/	chatbot, dialogueur
cursor nom	/ˈkɜːsə/	curseur
generative adjectif	/ˈd͡ʒɛnəɹətɪv/	génératif
gradient nom	/ˈɡɹeɪdiənt/	pente
learner nom	/ˈlɜɹnəɹ/	apprenant, apprenante

Les verbes à particule que vous entendrez

Mot	Sens
check out verbe	mater, regarder
break down verbe	tomber en panne

La grammaire de cette vidéo

Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :

Structure	Dans la vidéo
Propositions relatives who / which + proposition — une précision sur une personne ou une chose	someone who specializes · someone who actually · clear which is
Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait	are trained · are also accredited · is required
Present perfect have/has + participe passé — une action passée qui compte encore maintenant	have worked · haven't been

Prononciation à surveiller

Le locuteur utilise 24 contractions et formes réduites, comme you're, don't, I'm. Prononcez-les sous leur forme courte, telles que vous les entendez.

Les sons « th »: mathematics /mæθ(.ə)ˈmæt.ɪks/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
Les sons « sh » et « zh »: foundational /faʊnˈdeɪ.ʃə.nəl/, distinction /dɪˈstɪŋkʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, nutshell /ˈnʌt.ʃɛl/, specialize /ˈspɛʃəˌlaɪz/
Mots longs — placez bien l’accent: foundational /faʊnˈdeɪ.ʃə.nəl/, mathematics /mæθ(.ə)ˈmæt.ɪks/, solidify /səˈlɪdɪˌfaɪ/, ideally /aɪˈdi.ə.li/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/

Les sons difficiles pour les francophones :

/tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: scratch /skɹæt͡ʃ/, chatbot /ˈtʃætbɑt/, generative /ˈd͡ʒɛnəɹətɪv/, logistical /ləˈdʒɪ.stɪ.k(ə)l/, pinch /pɪnt͡ʃ/
/r/ anglais — langue recourbée, sans frotter la gorge: scratch /skɹæt͡ʃ/, neural /ˈnʊɹəl/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, accredit /əˈkɹɛd.ɪt/, blurry /ˈblɜːɹi/

Comment s’entraîner avec cette vidéo

Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme mathematics, scratch, neural.

Qu'est-ce que la technique du Shadowing ?

Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.

Technique du shadowing : lire le guide complet étape par étape →

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