Matt Watson

Serial Entrepreneur, Startup CTO, Podcaster, Blogger @ VisionaryCTO.com

I’ve spent over 20 years as a founder and CTO, starting in 2003 when I co-founded one of the earliest SaaS companies. We bootstrapped VinSolutions to $35M ARR and sold it for $150M in 2011.

Since then, I’ve built and scaled multiple companies. I’ve led engineering teams through every phase of growth. I’ve also learned firsthand what breaks when leadership, product, and engineering aren’t aligned.

In 2018, I started Full Scale. It began as a way to help myself scale faster with top-tier engineering talent in the Philippines. Today, Full Scale has over 300 developers supporting fast-growing tech companies that need to move efficiently and with clarity.

I recently launched Full Scale Ventures, a venture studio focused on solving real problems for startup founders and engineering leaders. Our first product is EngagePath, a tool designed to help thought leaders identify warm leaders from their followers.

Most recently, I wrote Product Driven, a book for engineering leaders who want to stop playing whack-a-mole with tickets and start building real product momentum. The book is about aligning culture, leadership, and customer focus. Product thinking isn’t just for product managers anymore.

I also host a podcast and write a weekly newsletter focused on the gap between business and engineering and how to close it.

Read the book and subscribe here: https://productdriven.com/connect

Learn more about Full Scale: https://fullscale.io

Founder & CEO

We started Full Scale to do software development the right way from the Philippines. Our 300+ employees are helping dozens of companies build their software quickly and affordably.

* Philippines based, English fluency is superb!
* Staff augmentation model, our team works directly for you!
* Focused on senior level developers
* No middle man project managers in the way

Startups don’t need a CTO. They need a CPTO. That’s a hill I’m willing to die on.

For 20+ years, I’ve had the CTO title. The only reason I’ve been successful is because I never acted like just the head of engineering. I acted like the Chief Product AND Technology Officer.

Building software isn’t about code. It’s about outcomes.

It’s knowing what matters to the customer.
It’s tying what you build to real value.
It’s iterating based on impact, not ego.

In the age of AI, this is even more obvious.

AI can write the code. That’s not the bottleneck anymore.

Product thinking is.

What should we build?
Why does it matter?
How does it change the user’s life?

The worst setup in a startup is a Head of Product and a Head of Engineering negotiating priorities while momentum dies.

Engineering and product can’t be separate empires.

They have to be fused.

If you think you need to hire a CTO in 2026, they better be a CPTO.

5x Founder & CTO | Author of Product Driven | Bootstrapped to 9-Figure SaaS Exit | CEO of Full Scale | Teaching Product Thinking to Engineers

Realizations you have after being a CTO for 20+ years:

1. Speed doesn't come from coding faster.

It comes from clarity, focus, and fewer surprise pivots.

2. Most engineers want to care about the product.

They just don’t get the context they need to do it well.

3. Vision isn’t an HR slogan or a slide deck.

It’s a tactical tool that guides real-time decisions across the team. 

4. The best engineers aren’t just builders.

They’re problem solvers. But only if they understand the problem.

5. Bad culture isn't loud.

It’s passive. Quiet meetings. No pushback. Lots of guessing.

6. “Done” isn’t when it ships.

It’s when it solves something for the user.

7. Teams don’t magically become product driven.

They become product driven when leadership acts like it matters.

8. Product debt slows you down more than tech debt.

No amount of clean code can fix the wrong thing built really well.

9. Focus is a leadership decision.

Not a prioritization framework.

10. If your engineers are stuck waiting for better requirements...

You don’t need a new process. You need to lead differently.

Learn dozens more lessons from my new book, Product Driven. Also now available on Audible!

Scaling Tech Teams through Product Thinking & High-Performing Offshore Talent | CEO @ Full Scale | Author Product Driven | Podcast Host

AI generated code is amazing until the shit hits the fan.
Production is down. Your best engineer is troubleshooting it.

There is one massive problem.

They have never debugged any of this code before. They didn't write the code, AI did.

They lack familiarity and muscle memory about how the code works.

It isn't much different than asking your engineer who started yesterday to go figure it out.

This is the ultimate problem with AI generated systems.

They work great until they don't. Then nobody knows how it actually works. There is no deep expertise.

How do we fix this?

Pray that AI can fix it when production is down?

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Author

Chief Executive Officer

Podcast Host

Founder, CEO, CTO

Here are the 4 golden rules of software development: 
 
#1 If it can be null, it will be null 
 
#2 The answer to every question is "It depends" 
 
#3 Getting users to actually use software is 10x harder than building it 
 
#4 Great requirements are more important than great developers

If you can build an entire application with a few AI prompts, so can your competitors.

You have nothing of unique value.

Real software isn't that simple.

Every new software project should start with the database, not the UI. 
 
The database is the foundation of most software applications. 
 
Bad database architecture can make it difficult to add new features later. 
 
The UI is simply the lipstick on the pig if the database is done incorrectly! 
 
Spending time understanding the UI and UX is really important. It helps shape the database architecture. 
 
But when it comes time to write code and build something, it always starts with the database.

Most “senior” engineers aren’t senior.
They’re just juniors who stayed at the same company for 5+ years.

They know every quirk of the legacy code.
They can tell you which bug comes back every quarter.
But ask them to design a brand new clean, simple service? Well...

Because memorizing a codebase isn’t the same as mastering fundamentals.
Surviving time-in-seat isn’t the same as developing judgment.

At Full Scale, we don’t leave seniority up to tenure.
We provide ongoing training so our engineers develop the skills that actually make them seniors:

⚡ Architecture thinking. How to design systems that scale and stay simple.

⚡ Problem-solving. Approaching new challenges instead of defaulting to old hacks.

⚡ Communication. Explaining tradeoffs clearly to teammates and stakeholders.

⚡ Product context. Understanding why the code matters, not just how it works.


Real senior engineers aren’t defined by years in the seat.
They’re defined by how they grow and how they make the team better.

With 300+ engineers, career growth isn’t optional.
It is critical to our success and to the success of the 85 clients we serve at Full Scale.

I need to hire offshore software engineers for my startup. 
 
Where should I hire them? 
 
How do I trust them? 
 
How do I make this work?

Our client just fired two local software engineers.

Not because they were bad engineers.

Because they refused to use AI.

This isn’t the first time a profession has faced a shift like this.

Travel agents who ignored online booking tools disappeared.
Stockbrokers who ignored electronic trading platforms disappeared.
Accountants who ignored spreadsheet software disappeared.
Journalists who ignored the shift to digital media disappeared.

Technology doesn’t wait for people to get comfortable.

AI won’t replace engineers.

But engineers who use AI will replace engineers who don’t.

Claude Code now commits 4% of all GitHub public commits. 

That is insane!

Every week I keep telling all of our team members and clients how important it is for them to learn to use these tools.

If you don't adapt to them, you are going to be left behind.

If you use it for a day and don't see the immense value in it, you are already left behind.

But is still like herding cats to get 300 people to use them and nearly 100 of our clients on board as well.

There will always be early adopters and late adopters.

The AI hype isn't hype for us. Claude Code and agentic engineering has permanently changed how we work.

4% of all commits is wild.

The real number of commits that used AI in some way is substantially higher.

Software engineering has a K-shaped skill gap right now.

The engineers embracing agentic AI are on the top curve. 

The ones who aren't are on the bottom, and the distance between them is growing fast.

We talk to leaders at tech companies every day. The things I'm hearing:
"I haven't written code since November."
"I'm getting 5x more work done."

Six months ago, a lot of us thought this was hype. 

Nobody thinks that anymore.

You're either figuring it out, or you're in denial. 

There's not much middle ground left.

This weekend we had "Wellness Day" for all of our employees.
A day dedicated to them and their families to relax.

Swimming, yoga, food, music, and lots of other activities for kids of all ages.

No laptops or Slack notifications. Just family fun!

At Full Scale, we believe that team work is more than sprints. We also need to slow down once in a while, and when we do, something special happens.

I learned a long time ago that without your team, you don't have a business. We have to make time to recharge, reconnect, and remember why we do this in the first place.

Thanks to all the Full Scale team members who made it out this year. Next up is the summer party in May!

Full Scale

The death to software companies is rarely the engineering team. It's the product team. 
 
Any engineering problem can be solved with money. At worst, it's a funding or management problem, not engineering problem. 
 
Want to build a nuclear bomb or put humans on the moon? With enough money... no problem. 
 
However, money can't solve a terrible product vision or business model. 
 
As they say... fortune favors the bold. In this case, a bold product vision. All too often, the problem is the lack of product vision. 
 
Many companies struggle with what to build, how to differentiate, and their target market. 
 
They meander around in circles with no strong direction. 
 
The engineering team is left rudderless and often lost in refactoring, maintenance, technical debt, and architecture rewrite oblivion. 
 
They produce no real product value to the end users. Instead of building cool products, they are left to focus on building cool code. 
 
Customers don't buy cool code. They buy cool products. 
 
Cool products require a bold product vision and focus. A startup can raise all the money in the world but can't raise a product vision. 
 
That usually comes from the founders and product team. 
 
If you want your startup to succeed, a strong product vision is the most important piece.

They were right about software engineering being dead.
Just not in the way people think.

Software isn’t code.

It’s architecture, tradeoffs, and understanding the customer well enough to know what not to build, and connecting technical decisions to real business outcomes.

That isn't dead and it isn't going anywhere.

That has always been the job. Most people just forgot.

If you think software engineering is just about code, your career is dying.

Don’t tell me you’re using AI for software engineering if all you’re doing is opening ChatGPT.

That was impressive in 2024.

It felt like a superpowered Google search. You could research faster. Draft cleaner code. Spin up a small function without digging through Stack Overflow.

But that’s the old baseline.

If you’re an engineer in 2026 and you say you’re “using AI,” the question isn’t whether you’re chatting with a model. It’s whether you’re running an agent with real context.

We’re not asking AI how to do something anymore.

We’re telling it what to build.

There’s a difference.

A quick prompt has no idea how your system is architected. It doesn’t know your naming conventions, your constraints, your edge cases, your tech debt, or the trade-offs you’ve already made. It’s guessing from the outside.

An agent operating inside your environment is different. It understands your repo. It sees your patterns. It knows the rules you’ve defined. Tools like Claude Code or agent-based Codex setups can take detailed instructions, apply them across a codebase, and execute within the context of your actual system.

That’s not a better Google search.

It’s a hundred times more powerful.

If your team says they’re “using AI” and all they’re doing is pasting snippets into ChatGPT, that’s like saying you’ve modernized because you use Stack Overflow.

Helpful? Sure.

Transformational? Not even close.

The shift is from Q&A to delegation.

From isolated prompts to persistent context.

From “how do I write this function?” to “here’s the objective, here’s the codebase, here are the constraints, go execute.”

That requires more from you, not less.

You have to define standards. Provide context. Be explicit about architecture and intent. AI is only as good as the environment you plug it into.

The teams that win over the next few years won’t be the ones casually dabbling with prompts.

They’ll be the ones who build systems around agents. Be that Claude Code, Cursor, ChatGPT Code, etc.

Are we sure this isn't a software engineer running Claude Code?

BBC news showed this 15 screen setup.

“Monitoring the situation”
“Matrix”
“Where is my mouse?”

No matter what your thoughts on this. I genuinely impressed and feel ashamed of my four monitor setup now.


I’m off to buy more monitors 🤣

Shouldn't a "public benefit corporation" be against mass domestic surveillance and autonomous weapons?

I'm proud of Dario Amodei for holding his ground.

I wouldn't want my technology used for those things either.

CEO and Co-Founder at Anthropic

NoSQL will destroy your startup.
It's actually the #1 reason all startups fail.

Because you’re missing the most important thing every business is built on.

The foundation.
The core.
The one thing you cannot fake, skip, or outsource.

You’re scaling infrastructure.
Refining your stack.
Debating databases.

But none of that matters if you ignore this one truth.

And by the time you realize what’s missing, it’s already eating away at your runway.

Here’s the twist:

I’m not talking about databases.

I’m talking about your funnel.

No SQL = No Sales Qualified Leads.

You don’t need a better backend.
You need people to sell to.

Because you can’t scale a business that’s never had real demand.

And no tech stack in the world will save you from no leads.

You don't need rockstar developers. You need rockstar requirements.

That was true in 2025. It's even more true now.

AI can write the code. It can't write the requirements.

Most of us are building really simple software. Display some data,
save it to a database, call an API. AI handles that now. A junior dev
with good prompts ships features in hours.

AI can't figure out what the customer actually meant.
It can't tell you which features matter and which are distractions.
It can't make the call when scope gets messy and someone has to say no.

The requirements, the clarity, the "why this and not that": that's
still a human job. Now it's the only job that matters.

AI made average developers rockstars.
It made bad requirements catastrophic.

"I used to think the code was the thing."

The most code-obsessed architect I know told me that yesterday.

Now he's watching four Claude Code agents churn out features while he figures out what to actually build next.

Code was never the thing.
What the code does is the thing.

Tech founders figured this out the hard way.
You can't sell code. You sell what the code makes possible.

🤖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

Not every problem needs an LLM. 
Some need 12 characters of regex.

I see people spending $𝟬.𝟬𝟯 𝗽𝗲𝗿 𝗔𝗣𝗜 𝗰𝗮𝗹𝗹 to extract emails from text.

Meanwhile: ⤵️

[𝗮-𝘇𝗔-𝗭𝟬-𝟵+_.-]+@[\𝘄-]+\.\𝘄+

Done. 𝗭𝗲𝗿𝗼 𝗰𝗼𝘀𝘁. Runs in milliseconds.

We're in 2026 and somehow we forgot that basic code still works. 

Not everything needs a prompt, a model, and a 3-second response time.

Need to validate a phone number? 
→ Regex. 

Need to clean whitespace from 10,000 rows? 
→ A one-liner. 

Need to rename 500 files? 
→ A bash script your laptop runs in 0.2 seconds.

LLMs are incredible for reasoning, writing, and ambiguity. 
But when the task has a clear rule and a predictable pattern, just write the rule.

Cheaper. Faster. More reliable.
Stop using a rocket ship to cross the street.

PS: Do you use LLMs for everything because it's convenient?

♻️ Repost if your network needs this reminder 
📌 Save it for the next time you almost open ChatGPT/Claude for string matching

Follow me for more → Anas Riad

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

The ML project you wish you built sooner.
2.5hr of hands-on learning ⤵️

No excuses for skipping end-to-end ML now! 🤣

(PS: Link to the full YT video in the comments)

When I first started working on churn prediction, 
I noticed something that always bothered me.

There are tons of Regression projects out there, 
but 𝟗𝟗% 𝐨𝐟 𝐭𝐡𝐞𝐦 stop at the notebook.

It’s like drawing the blueprint for a house but never building it.

So I created a full end-to-end regression project that goes from:
raw dataset → trained model → deployment → tests → cloud.

Here’s what I cover in this project & video:
→ Split data by date (time-series aware)
→ Data Quality with Great Expectations
→ Well-structured pipelines (feature, training & inference)
→ Unit & smoke tests to keep things reliable
→ Experiment tracking with MLflow
→ Containerization with Docker
→ FastAPI serving layer
→ CI/CD with GitHub Actions
→ AWS (ECS, ECR, S3, ALB) for Deployment

It’s the same type of stack I use in freelance + personal work, 
but applied to regression.

Looking forward to your thoughts on it.

🌟 Link to the full YT video in the comments

PS: I have another end-to-end coming soon, stay tuned 🤔

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
 
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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

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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

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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
 
---

📌 Save this for your next project.
♻️ Repost this to help your network in data.

90% of analysts use only 10% of Pandas.

Master these 10 operations. Level up instantly.

When I started learning data analysis,
I thought Pandas was “too big” to master.

Turns out… you only need the core.

Here are the operations I use almost daily:

• 𝗟𝗼𝗮𝗱 𝗱𝗮𝘁𝗮 → pd.read_csv()
• 𝗜𝗻𝘀𝗽𝗲𝗰𝘁 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 → .head(), .info(), .describe()
• 𝗦𝗲𝗹𝗲𝗰𝘁 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 → df[["col1","col2"]]
• 𝗙𝗶𝗹𝘁𝗲𝗿 𝗿𝗼𝘄𝘀 → df[df["col"] > value]
• 𝗚𝗿𝗼𝘂𝗽 & 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 → .groupby().agg()
• 𝗖𝗿𝗲𝗮𝘁𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 → df["new"] = expression
• 𝗠𝗲𝗿𝗴𝗲 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 → pd.merge()
• 𝗛𝗮𝗻𝗱𝗹𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲𝘀 → .isna(), .fillna()
• 𝗦𝗼𝗿𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 → .sort_values()
• 𝗘𝘅𝗽𝗼𝗿𝘁 𝗰𝗹𝗲𝗮𝗻 𝗱𝗮𝘁𝗮 → .to_csv() or .to_excel()

That’s it.

If you can combine these properly, you can solve 80% of real-world problems.

The rest is practice and business thinking.

PS: Is Pandas your go-to library for data analytics?

📌 Follow Anas Riad for more content around data science

95% of data scientists who reach out to me

Have this 1 thing in common ⤵️ 

“They excel at building ML models in a notebook”
but "They struggle to build something beyond a Demo"

Look, I’m not against notebooks, but that mindset is restrictive.

If your project only:

→ runs on your machine
→ uses static data (all clean)
→ isn’t deployed and usable

Then it’s a "SIMPLE" demo, not a "REAL" data project.

In my recent journey to bridge the gap, I’ve decided to collaborate with Markus Kuehnle on a detailed carousel that includes major points of an end-to-end ML project, such as:

→ Setting project environment
→ Exploratory data analysis
→ Feature Engineering
→ Model Registry & Experiment Tracking
→ Feature, training, inference pipelines
→ Serving layer (FastAPI)
→ Docker container (reproducibility)
→ GitHub Actions (CI/CD)
→ Deployment & monitoring

Hope it gives you more clarity on what it takes to build and deploy an ML model end-to-end.

📌 Save this for your next project.
♻️ Repost this to help your network in data.

Machine learning End-to-End pipeline

I started in data analytics in 2021. 
The job barely looks the same today.

Back then, your value was tied to tools.

You learned SQL. You learned Python. Maybe some Tableau or Power BI. And that technical gap between you and everyone else in the company? That was your edge. You could dig deeper than the person pulling numbers from a quick Excel sheet. That was enough.

Fast forward to 2025 and 2026.

AI writes code faster than most junior analysts. It reasons through data problems. It spots patterns. And yeah, I know what some of you are thinking, "but it makes mistakes." Sure. So do we. We miss things. We overlook patterns. We carry bias into every analysis without realising it. We're not the gold standard we like to think we are.

I've been working with AI daily for the past 3 years now. And what I've watched happen slowly at first, then all at once, is that the technical barrier that made analysts valuable is shrinking fast.

That directly hits entry level roles the hardest.

If your entire value is "I can write a SQL query and build a dashboard," you're competing with something that does it in seconds. Not in a hypothetical future. Right now.

But here's what I've also noticed in 3 years of doing this work:

The analysts who actually move the needle? They were never just "tool operators."

They're the ones who sit with stakeholders and ask the hard questions. They're the ones who take a messy data output and turn it into a recommendation that helps the business earn more, save more, or move faster. They understand context. They push back on bad assumptions. They connect the numbers to actual decisions.

No AI tool does that well yet.

So if you're early in your career or thinking about getting into analytics the path forward isn't about memorising SQL syntax or chasing every new tool.

It's about:

→ Going deeper than surface-level reporting  
→ Learning to frame problems before jumping into data  
→ Giving recommendations, not just dashboards  
→ Expanding into predictive models, time series, and forecasting  
→ Using AI as a multiplier, not pretending it doesn't exist

The role of data analyst isn't dying.

But the version of it that was just querying and reporting? That chapter is closing.

The analysts who evolve will be fine. Better than fine, actually, because AI gives you leverage that didn't exist 3 years ago.

The ones who don't evolve will wonder what happened.

♻️ Repost if you think someone in your network needs to hear this  
📌 Save for when you need the reminder yourself

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