🤖Data Scientist | Turning Raw Data into Money Generating Insights | Data-led Growth for Your Business 🚀

Freelance Data Scientist | ML Engineer | SQL | Python | Power BI | MLOps

Data analytics and Machine learning models built 5X faster - no quality compromise. Worked with 14 clients in 7 different industries in the last 3 years. Helped current company scale to $5M+ ARR. Featured TOP RATED PLUS talent on Upwork in 2025 with 100% job completion and satisfaction.

My Toolkit:
- Data analytics: Python, SQL, Excel
- ML Engineering: FastAPI, AWS, Streamlit, great_expectations
- Data visualization: PowerPoint, PowerBI, Plotly, Matplotlib
- Data modeling: Scikit-learn, TensorFlow, Keras
- MLOPs: CI/CD (GitHub Actions), Docker
- Additional tools: Notion, n8n, Canva, Git/GitHub

My Stats:
- 5-stars reviews on 99% of projects delivered.
- 100% Job Success Score for on-time, budget delivery.
- Helped scale currently business to $5M ARR with ATS analysis & Reporting.
- 1.5 years TOP RATER PLUS on Upwork.
- 1800 hours billed.
- $60k+ earned.

What You’ll Get Working With Me:
- A data analyst who is a real problem solver.
- Quick responses (within the hour on working hours)
- Clean, maintainable code and detailed documentation
- A clear “NO” if something isn’t feasible; no wasted time
- A well-crafted report or dashboard as per the requirements

My clients think:
- "Anas is great and highly knowledgeable. He is passionate about solving data science challenges and has helped me significantly streamline some of my processes, enabling better scalability.
- "Anas, was prompt and thorough and I would def use him again in the future."
- "Anas is great to work with. He made the job so fluid and his knowledge of data science makes him someone worth listening to. I’m glad I chose him for my small business. I’m looking forward to what’s coming next, and I would definitely hire him again."

Popular Clients & Products:
- Uhubs: Financial company based in London
- AllAboutGroup: Law recruitment firm based in London
- Growth Dynamics: Marketing and data strategist based in London.
- Adway: Tech and recruitment company based in Gotheborg, Sweden

About me:
- Football fan since I can remember (Real Madrid fan)
- Tennis lover and practitioner (one-handed backhand style)
- Mangas fan (One Piece, Naruto, Bleach)
- Travel addict (South Asia, Europe, Africa, America (soon))
- Reading is something I enjoy as much as coffee (oat latte fan recently)

-----

YouTube Channel
→ GO HERE: https://www.youtube.com/anas_riad

Request services via LinkedIn or Email below:
r.riadanas@gmail.com

Hire on Upwork:
https://www.upwork.com/freelancers/~0163377bf3029c7553

Anas Riad

YouTube is still the best place to learn ML.

Most people still watch the wrong videos.

Here are 𝟭𝟮 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝘃𝗶𝗱𝗲𝗼𝘀 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 ML:

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𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗖𝗦𝟮𝟮𝟵: 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
   
The legendary starting point for ML.
Covers core theory, intuition, and key algorithms.

Video: https://lnkd.in/e-aJRQun
   
---

𝗔𝗹𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗶𝗻 𝟮𝟮 𝗠𝗶𝗻𝘂𝘁𝗲𝘀

A fast, structured overview of all major ML concepts.
Great for building a mental map before going deeper.
   
Video: https://lnkd.in/exCP58F6
   
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𝗠𝗟𝗢𝗽𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀
   
Shows how ML actually works in real products.
Pipelines, deployment, monitoring, and production mindset.
   
Video: https://lnkd.in/erzM3b4F

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𝗛𝗼𝘄 𝘁𝗼 𝗗𝗲𝗽𝗹𝗼𝘆 𝗠𝗟 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 (𝗙𝗮𝘀𝘁𝗔𝗣𝗜, 𝗗𝗼𝗰𝗸𝗲𝗿, 𝗔𝗪𝗦)
   
From trained model to live service.
A practical deployment walkthrough with modern tools.
   
Video: https://lnkd.in/e3JtcAZk

---

𝗦𝘁𝗲𝗮𝗹 𝗠𝘆 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲

Learn how to structure ML projects properly.
Clean folders, reproducibility, and real-world practices.
   
Video: https://lnkd.in/eds2Buq8

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𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
   
A full ML project from start to finish.
Data prep, modeling, evaluation, and delivery.
   
Video: https://lnkd.in/ebx_dvtn

---

𝗠𝗟𝗢𝗽𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹 (𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗲𝘄𝗲𝗿 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁)
   
Build a real ML pipeline using real data.
Feature engineering, modeling, and orchestration.
   
Video: https://lnkd.in/e9_xVb-Z

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𝗬𝗼𝘂 𝗪𝗶𝗹𝗹 𝗡𝗢𝗧 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗶𝗻 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀
   
A reality check on learning machine learning.
Focuses on process, depth, and consistency.
   
Video: https://lnkd.in/eRqYhJWK

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𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
   
A practical regression project walkthrough.
Covers evaluation, assumptions, trade-offs.
   
Video: https://lnkd.in/e2GEnb4N

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𝗘𝘃𝗲𝗿𝘆 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱
   
A clear overview of common ML models.
   
Video: https://lnkd.in/eMW3YNKq

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𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗚𝗿𝗮𝗱𝗲 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹
   
Focus on building ML systems that last.
Code quality, deployment, and maintainability.
   
Video: https://lnkd.in/eXYhdxgt

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𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀)
   
A simple introduction to ML definitions and concepts.
   
Video: https://lnkd.in/eT3qrGBt
   
---

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Data Scientist & BI Consultant @Adway • ML Engineer • YouTube @ Anas Riad

Dear recruiters,

If you expect a Data Scientist to be an expert in:

→ Python for large-scale data analysis
→ Excel with complex financial-style reports
→ Pandas and NumPy on real, broken datasets
→ Heavy data cleaning and feature engineering
→ Git workflows, CI/CD, and automated testing
→ LLMs, prompt design, and applied AI use cases
→ Business KPIs, north-star metrics, and trade-offs
→ Clear storytelling with non-technical stakeholders
→ Dockerized environments and reproducible pipelines
→ Experiment tracking, model versioning, and rollbacks
→ Data pipelines touching cloud storage and warehouses
→ Regression, classification, and model selection at scale
→ Advanced SQL with messy joins and window functions
→ Exploratory analysis with Matplotlib, Plotly, and Seaborn
→ Probability theory, hypothesis testing, and A/B experiments
→ Bias–variance trade-offs and model performance monitoring

… you’re not hiring one 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁.
You’re describing an 𝗲𝗻𝘁𝗶𝗿𝗲 𝗱𝗮𝘁𝗮 𝗱𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁.

PS: Post inspired by José Siles

📌 Follow Anas Riad for more content around data science

90% of ML systems rely on fewer than 10 libraries

Choosing the right one matters more

Here are the Top Python libraries every ML engineer should know 👇

1️⃣ 𝗗𝗮𝘁𝗮 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲)

Before models, before AI, you need data control.

These are the base of everything:

𝗡𝘂𝗺𝗣𝘆
→ Numerical computing, arrays, math operations

𝗽𝗮𝗻𝗱𝗮𝘀
→ Data cleaning, analysis, CSV & SQL workflows

If you struggle here, every model later feels harder than it should.

-- -- -- 

2️⃣ 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (structured data still wins)

Most business problems are still tabular.

𝘀𝗰𝗶𝗸𝗶𝘁-𝗹𝗲𝗮𝗿𝗻
→ ML fundamentals, baselines, quick experiments

𝗫𝗚𝗕𝗼𝗼𝘀𝘁
→ High-accuracy models for structured / business data

This stack powers pricing models, churn prediction, fraud detection, and more.

-- -- -- 

3️⃣ 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (neural networks at scale)

When data grows and patterns get complex:

𝗣𝘆𝗧𝗼𝗿𝗰𝗵
→ Research and production deep learning

𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄
→ Scalable, production-ready ML systems

-- -- -- 

4️⃣ 𝗡𝗟𝗣 & 𝗟𝗮𝗿𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀

Text data is everywhere now.

𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀
→ LLMs, text generation, fine-tuning pretrained models

𝗚𝗲𝗻𝘀𝗶𝗺
→ Topic modeling, embeddings, document similarity

-- -- -- 

5️⃣ 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 & 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴

Often ignored. Always useful.

𝗣𝗿𝗼𝗽𝗵𝗲𝘁
→ Simple forecasting with clean APIs

𝗗𝗮𝗿𝘁𝘀
→ Forecasting and anomaly detection

-- -- -- 

6️⃣ 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴

When uncertainty matters:

𝗣𝘆𝗠𝗖
→ Bayesian modeling and probabilistic inference

-- -- -- 

You don’t need to master all 10 today.

Start with the foundations.
Add tools as your problems get more complex.

Knowing which tool fits the problem already puts you ahead.

PS: More essential libraries in first comment

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📌 Follow me for Data & ML content

I was never supposed to become a Data Scientist.

Nothing about my path pointed there.

I tried medicine.
Then architecture.
Spent 5 years on a Master’s in industrial engineering.
Worked as a professional DJ at the same time.

Then I found data science.
Late. Unexpectedly.
And honestly… it made no sense.

I changed countries.
I doubted myself constantly.
I had to spend one full year just learning how to speak better.
Five years ago, I couldn’t even express myself properly in this language.

Now I work in data science.
And create content in that same language.

It wasn’t linear.
It wasn’t clean.
And the odds were clearly against me.

What made the difference wasn’t just Python or maths.

Being a good data scientist is not only technical.

It’s also:

→ Being reliable when things break
→ Understanding the business, not just the model
→ Thinking clearly when data is messy
→ Explaining results so people actually act
→ Saying “this approach failed” and learning fast

Yes, you still need:

→ Coding
→ Statistics
→ Probability
→ Solid fundamentals

But that’s only part of the picture.

What really helped me:

→ Perseverance
→ Belief (especially when confidence was low)
→ Hard work
→ Learning from the right people

Data scientists don’t all look the same.
And they don’t come from the same background.

If your path feels messy, slow, or “wrong”,
you might actually be building something stronger.

PS: Did you find your path on the 1st try?

Get started in Time Series with Pandas
(in just 10 minutes)

Here are a few simple but powerful tips 👇

𝟭. 𝗨𝘀𝗲 𝗶𝗻𝗱𝗲𝘅_𝗰𝗼𝗹 𝗮𝗻𝗱 𝗽𝗮𝗿𝘀𝗲_𝗱𝗮𝘁𝗲𝘀
When loading your CSV, set the date column as your index and convert it to a datetime type in one line:

pd.read_csv("BTC-USD_full_history.csv", index_col="Date", parse_dates=True)

→ That small trick saves tons of manual work later.
→ This tells Pandas to use date column as index for time series.

-- -- -- 

𝟮. 𝗦𝗹𝗶𝗰𝗲 𝗱𝗮𝘁𝗮 𝗯𝘆 𝘆𝗲𝗮𝗿 𝗶𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆
With a datetime index, you can filter by date with one command:

btc_data.loc['2022']['Close'].plot()
btc_data.loc['2022-01']['Close'].plot()

→ Pandas understands almost any date format.
→ From just a year ('2022') to full timestamps with timezone or nanoseconds.

-- -- -- 

𝟯. 𝗨𝘀𝗲 𝗿𝗲𝘀𝗮𝗺𝗽𝗹𝗲() 𝗳𝗼𝗿 𝘁𝗿𝗲𝗻𝗱𝘀
Smooth noisy data using monthly or weekly averages:

btc_data['Close'].resample('M').mean().plot()
btc_data['Close'].resample('W').std().plot()

-- -- -- 

𝟰. 𝗖𝗼𝗺𝗽𝗮𝗿𝗲 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀 𝗲𝗮𝘀𝗶𝗹𝘆

→ Stack data from multiple assets (like BTC and ETH) and plot subplots to compare performance over time.

-- -- -- 

It’s amazing how much you can do with Pandas alone.
No need for complex tools when exploring time series.

Full video in the comments below.

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80% of analyst tasks will automate.

Will you still be relevant in 2026?

⭐ Venkata Naga Sai Kumar Bysani and I broke this down simply  ⤵️ 

1️⃣ Work with AI, not against it
Copilot, Claude, automation tools. Use them well. Guide them. Validate outputs.

2️⃣ SQL alone won’t cut it anymore
Add Python. Learn ML basics. Understand how data pipelines work.

3️⃣ Understand the business deeply
Dashboards are easy. Translating numbers into revenue, risk, and strategy is rare.

4️⃣ Solve problems, not tickets
No one cares about the tool. They care about impact.

5️⃣ Build systems that run without you
Automate reports. Schedule pipelines. Create alerts. Sleep while your work runs.

6️⃣ Explain insights clearly
If leadership can’t understand it, it won’t matter.

7️⃣ Share your work publicly
Document what you’re learning. Visibility creates leverage.

-- -- -- 

The analyst role isn’t disappearing.
It’s evolving.

And the ones who adapt will win big in 2026.

💬 What’s your take?
Is the Data Analyst role shrinking or just becoming more strategic?

📌 Follow Venkata Naga Sai Kumar Bysani & Anas Riad for top-tier data content
♻️ Repost this post with your network to benefit

I made $70k on Upwork as a data scientist.
Not by luck. By rules I didn’t break.

Here are my 6 non-negotiables 👇

-- -- -- 

𝟭. 𝗚𝗲𝘁 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝟮–𝟯 𝗳𝗶𝘃𝗲-𝘀𝘁𝗮𝗿 𝗿𝗲𝘃𝗶𝗲𝘄𝘀 𝗳𝗮𝘀𝘁

I started with people I already trusted.
Friends. Family. Warm intros.
Social proof changes everything.

-- -- -- 

𝟮. 𝗟𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝘁𝗼 𝘀𝗲𝗮𝗿𝗰𝗵 𝗼𝗻 𝗨𝗽𝘄𝗼𝗿𝗸 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆

Most people apply to bad jobs. I filtered hard:

- Same-day jobs
- Fewer proposals
- Verified clients
- $100k+ spent
- 4.3⭐+ rating

Bad jobs drain energy. Good jobs compound.

-- -- -- 

𝟯. 𝗙𝗶𝗹𝘁𝗲𝗿 𝗰𝗹𝗶𝗲𝗻𝘁𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀

Skills matter less than people. I avoided:

- Vague requirements
- Unreal timelines
- “Quick task” language
- Budget pressure early

Good clients respect scope. Toxic ones don’t.

-- -- -- 

𝟰. 𝗪𝗿𝗶𝘁𝗲 𝗽𝗿𝗼𝗽𝗼𝘀𝗮𝗹𝘀 𝘁𝗵𝗮𝘁 𝗳𝗲𝗲𝗹 𝗵𝘂𝗺𝗮𝗻

No copy-paste. No essays.
Short. Clear. Specific. And often a Loom video.
Seeing your face builds trust faster than text.

-- -- -- 

𝟱. 𝗣𝗿𝗶𝗰𝗲 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘃𝗮𝗹𝘂𝗲, 𝗻𝗼𝘁 𝗳𝗲𝗮𝗿

I raised rates as confidence grew.
Experience compounds. So should pricing.

-- -- -- 

𝟲. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗽𝗿𝗲𝘀𝗲𝗻𝗰𝗲 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 𝗨𝗽𝘄𝗼𝗿𝗸 (𝗵𝘂𝗴𝗲 𝗯𝗼𝗻𝘂𝘀)

YouTube + LinkedIn helped clients trust me faster.
They could see how I think. Not just what I claim.

-- -- -- 

Upwork wasn’t the magic.
Systems were.

PS: What’s been your biggest struggle with freelancing so far?

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80% of data science work fails after notebooks.

Engineering is the missing skill in 2026.

I worked on this with José Siles and the message is simple:
👉 Models don’t fail. Systems do

Here’s the assumption vs reality many data scientists face:

- Expectations: Notebooks are enough
- Reality: If it runs only locally, it’s not usable

- Expectations: Better models mean better results
- Reality: Bad data breaks even great models

- Expectations: Reproducibility is optional
- Reality: Versions and environments matter

- Expectations: Security comes later
- Reality: Access and data safety are part of the job

- Expectations: Accuracy is everything
- Reality: Cost, speed, and stability matter more

- Expectations: Complex beats simple
- Reality: Simple wins in real systems

- Expectations: Version only code
- Reality: Version data and models too

- Expectations: Docs are extra work
- Reality: Docs make work usable by others

- Expectations: Experiments create value
- Reality: Pipelines create value

- Expectations: Failures are rare
- Reality: Failures are normal, prepare for them

This isn’t about becoming a full engineer.

It’s about making your work real and trusted.

💬 Curious:

Which skill do you think matters most in real data roles?
Or which one did you learn the hard way?

PS: Follow José Siles for top-notch data engineer content 🌟

Two months. 250M views. 700K comments.

One question: who should Steven Bartlett invite next?

I’ve always loved The Diary of a CEO.

It’s one of those podcasts that makes you think deeper, 
about business, people, and purpose.

So I decided to turn my curiosity into a real AI project.

A few months ago, I built an AI system that analysed every episode of The Diary of a CEO in 2025, every topic, every guest, every comment.

Here’s what it did:

- Ran 𝘁𝘄𝗼 𝘁𝘆𝗽𝗲𝘀 𝗼𝗳 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:
 - 𝗧𝗼𝗽-𝗳𝘂𝗻𝗻𝗲𝗹: measuring episode performance through views, likes, and engagement metrics. Given them different weights.
 - 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀: using NLP, LLMs and SLMs to understand audience sentiment at scale
- Combined both layers to see what drives real engagement beyond the surface

This project wasn’t just about insights; it was about "learning".

I wanted to:

- Build a full 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 from scratch
- Handle real, messy text data at scale (emojis, sarcasm…)
- Use AWS GPU servers for training and Streamlit for deployment
- Actually ship something meaningful

It reminded me why I love data.

You don’t need to do random tutorials or fake datasets.

You can take something you’re genuinely passionate about, podcasts, football, fitness and turn it into a real AI project that teaches you 10x more.

𝗧𝗵𝗶𝘀 𝗼𝗻𝗲 𝘁𝗼𝗼𝗸:

💻 Python, NLP, LLMs, SLMs
☁️ AWS for compute
🕓 2 months (nights + weekends)
💰 ~$100 total cost

Based on the analytics, the top 5 topics were 𝗧𝗲𝗰𝗵, 𝗣𝗼𝗹𝗶𝘁𝗶𝗰𝘀, 𝗥𝗲𝗹𝗶𝗴𝗶𝗼𝗻, 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝗮𝗻𝗱 𝗙𝗶𝗻𝗮𝗻𝗰𝗲,

And the most requested guest by far was 𝗘𝗹𝗼𝗻 𝗠𝘂𝘀𝗸.

Who knows… 

With more time and resources, I could make this even better.

PS: Want to see a detailed breakdown? Let me know ⤵️

STOP chasing certifications.
They might be hurting your career.

Do you think visiting every football stadium in Europe
makes you a professional football player? ⚽

Same thing here 👇
• AWS certs
• Azure certs
• GCP certs

If you never build real projects,
you’re just collecting tickets, not skills.

Certifications support experience.
They don’t replace it.

Be honest:
What helped you more, certifications or real projects?

(Hot takes welcome 👀)

Everyone wants to be an AI Engineer.
But not many want to master the foundations first.

Miguel Otero Pedrido a Senior ML Engineer, recently told me:
“AI engineering becomes easy when your MLE foundations are strong.”

Still, most people skip the basics like:
→ CI/CD pipelines
→ Feature engineering
→ Model experimentation & tracking
→ Deployment, monitoring & observability
→ Reproducibility (Docker, version control, etc.)

Then they open n8n,
build a few workflows,
and call themselves AI engineers.

I’m not here to tell anyone what to do.
But over the years, I’ve learned to tune out the hype and stick to my plan.

The AI engineers you admire on LinkedIn?

Most have 5+ years of experience in data science and ML engineering.
They didn’t shift into AI overnight.

Stop following the hype.
Start building your career the right way.

♻️ Repost if useful
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