Shadowing Practice: Is ML Engineering Dead? (AI vs ML Engineer) - Learn English Speaking with Video
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One of the most confusing questions in tech right now is what is the difference between a AI engineer
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and a machine learning engineer?
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Both are six-figure jobs, but if you choose the wrong one, you can spend months, if not years,
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learning the wrong skills for that particular job that you are after.
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As a practicing machine learning engineer, I want to outline the key differences between the two roles,
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so that way you can make the correct choice for your career path.
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Let's get into it.
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In a nutshell, an AI engineer is basically a software engineer,
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but someone who specializes in the use and integration of foundational Gen AI models,
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like Claude, BERT and the regular GPT model.
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They don't build these models directly from scratch but rather use them to serve a certain purpose.
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On the other hand a machine learning engineer is someone who actually builds models from scratch
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or using basic libraries and deploys them in end to end systems.
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These models that a machine learning engineer builds are mainly more traditional machine learning models like gradient booster trees
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or neural networks
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but sometimes they can also work on gen ai models as
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well what i find funny about this naming convention is
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that machine learning is actually a subfield of ai
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so an ai engineer at least by today's definition is actually a gen ai engineer
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so as you can see it's not very crystal clear which is why the question is so confusing.
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Anyway, enough of me being pedantic, let's explain what these roles do in a bit more detail.
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As I just mentioned,
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you should think of an AI engineer as essentially a software engineer with a specialism in using Gen AI models
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and using those models more to build a product rather than
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building these llms directly from scratch ai engineers mainly work with something called foundational models
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which are really big neural networks that are trained on oceans of data
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that is like text image audio and video the most kind of popular gen ai
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or foundational model is obviously the ChatGPT with its GPT series.
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As I said before, AI engineers don't build or train these foundational models.
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They rather integrate them to traditional software products.
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For example, they may embed a foundational model as a chatbot on a shopping website
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so that customers can quickly find what they're after on
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that particular website or they may add it to a coding IDE as a coding assistant like a cursor is.
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So what skills do you need to become an AI engineer?
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Well this role is evolving very quickly and
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so the requirements change pretty much every quarter
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but let me give you a list of all the key things that you need to know.
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What you mainly need to kind of be aware of or at least learning is all the latest developments in LLMs,
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neural networks and pretty much just how the AI industry is moving.
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To give you a concrete list, you should have solid software engineering skills,
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skills in Python, SQL and other back-end languages like Java or Go,
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CICD, Git and GitHub, LLMs and Transformers, RAG, prompt engineering, foundational models,
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fine tuning and the model context protocol.
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So as I said, it's basically all the fundamental software engineering skills, plus that extra knowledge about Gen AI,
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LLMs, and basically all the cutting edge AI that's currently happening at the moment.
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If you are looking to become an AI engineer, then I recommend you check out the AI engineer career track from 365 Data Science,
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who are kindly sponsoring this video.
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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.
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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,
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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.
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You will also develop portfolio projects along the way to solidify your understanding.
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Learners from 365 Data Science now work at top tech companies like Amazon, Meta and Google.
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So you'll be in great hands.
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Not to mention, you'll get an AI engineer certificate that you can showcase to prospective employers.
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These certifications are also accredited by leading industry bodies, making your application stand out even more.
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I will leave all of this linked in the description below for you to check out.
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A machine learning engineer focuses on building machine learning models
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and then deploying them into production systems systems it initially came
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from the software engineering role where there was demand for software
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engineers with machine learning skills kind of similar how there is
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demand now for software engineers with ai engineering skills the significant distinction between a machine learning engineer
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and an ai engineer is that a machine learning engineer builds machine learning models directly from scratch.
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However, these models are more specific and narrower in exactly what they solve.
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For example, you may build a machine learning model
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that detects credit card fraud or a recommendation system for a social media app.
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But as you can see, these models are very targeted and have a very specific use case.
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Whereas the models an AI engineer works with are generative
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and more foundational and
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because they're foundational they can be used on a wide variety
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of tasks like chat gpt it can be used for loads of things
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but the models built by machine learning engineers like i just said a lot more specific
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so there is a difference in the types of models
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and the use case of models between the two roles as well there also exists further specialism
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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,
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or a machine learning infrastructure engineer.
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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.
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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.
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As a practicing machine learning engineer myself, I can tell you the exact things that you need to know.
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So obviously to start, you need to know Python and SQL.
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However, some companies may require other languages.
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For example, in my current role, I'm also learning Rust.
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You also need to know Git and GitHub, Bash and Z shell, AWS,
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Azure and GCP, software engineering fundamentals like CICD, MLOps and Docker,
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excellent machine learning knowledge ideally with a specialism in an area like forecasting recommendation systems
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or computer vision and solid mathematical understanding of statistics linear algebra
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and calculus so as you can see you basically need software engineering skills
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but also this kind of wide skill set in machine learning
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and maths which makes sense from how i described the distinction between AI engineering and machine learning engineer.
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So the question still stands, which one should you choose?
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Well let me break down some of the more logistical aspects.
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The background for both roles is pretty similar.
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Ideally you have a master's in a stem based subject
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and you have worked a couple of years as either a data scientist or software engineer.
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If you want to become an AI engineer, the software engineer background is probably better than a data scientist.
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But I wouldn't worry too much about this.
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I will say though that AI engineering is slightly easier to get into,
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mainly because learning how to use Gen AI is a slightly easier learning curve than understanding all the complex maths
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and machine learning theory that is required to become a machine learning engineer
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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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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Is ML Engineering Dead?”. המילים שחוזרות הכי הרבה: engineer, learning, machine, model, software. בסרטון הזה יש 136 משפטים ו-1774 מילים לתרגול Shadowing. הדיבור נמשך 11:56. הדובר שומר על קצב יציב של כ-149 מילים בדקה, קצב נוח לתרגול Shadowing. 82% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.
אוצר מילים מרכזי בסרטון הזה
15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| confuse פועל | /kənˈfjuːz/ | בלבל |
| recommendation שם עצם | /ˌɹɛkəmɛnˈdeɪʃən/ | הַמְלָצָה |
| blurry שם תואר | /ˈblɜːɹi/ | מטושטש |
| cursor שם עצם | /ˈkɜːsə/ | סמן |
| mislead פועל | /mɪsˈliːd/ | הִטְעָה |
| beginner שם עצם | /bəˈɡɪnɚ/ | מַתְחִיל, טִירוֹן |
| calculus שם עצם | /ˈkæl.kjə.ləs/ | חֶשְׁבּוֹן |
| pinch פועל | /pɪnt͡ʃ/ | צבט |
| algebra שם עצם | /ˈæl.d͡ʒɪ.bɹə/ | אַלגֶבּרָה |
| vague שם תואר | /veɪɡ/ | עמום, מעורפל |
| outline שם עצם | /ˈaʊtlaɪn/ | קו מתאר |
| vector שם עצם | /ˈvɛktɚ/ | וקטור |
| mathematical שם תואר | /ˌmæθ(.ə)ˈmæt.ɪ.kəl/ | מָתֵמָטִי |
| median שם עצם | /ˈmiː.di.ən/ | תִּיכוֹן |
| detect פועל | /dɪˈtɛkt/ | גילה, זיהה |
Phrasal verbs שתשמעו
| מילה | משמעות |
|---|---|
| check out פועל | בדק |
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | someone who specializes · someone who actually · clear which is |
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | are trained · are also accredited · is required |
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | have worked · haven't been |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-24 קיצורים וצורות מקוצרות, כמו you're, don't, I'm. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “sh” ו-“zh”: foundational /faʊnˈdeɪ.ʃə.nəl/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, nutshell /ˈnʌt.ʃɛl/, specialize /ˈspɛʃəˌlaɪz/, showcase /ˈʃoʊˌkeɪs/
- מילים ארוכות — שימו לב להטעמה: foundational /faʊnˈdeɪ.ʃə.nəl/, solidify /səˈlɪdɪˌfaɪ/, ideally /aɪˈdi.ə.li/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, generative /ˈd͡ʒɛnəɹətɪv/
צלילים שדוברי עברית מתקשים בהם:
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: chatbot /ˈtʃætbɑt/, pedantic /pəˈdæn.tɪk/, calculus /ˈkæl.kjə.ləs/, algebra /ˈæl.d͡ʒɪ.bɹə/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: neural /ˈnʊɹəl/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, accredit /əˈkɹɛd.ɪt/, blurry /ˈblɜːɹi/, generative /ˈd͡ʒɛnəɹətɪv/
- עיצור קולי בסוף מילה — /b/, /d/, /g/, /z/, /v/ בלי להפוך אותם לאטומים: confuse /kənˈfjuːz/, generative /ˈd͡ʒɛnəɹətɪv/, mislead /mɪsˈliːd/, specialize /ˈspɛʃəˌlaɪz/, evolve /ɪˈvɑlv/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- תרגלו Shadowing משפט אחר משפט במהירות רגילה, וחזרו על כל משפט עד שהקצב שלכם תואם את הדובר.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו confuse, recommendation, blurry.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.