Niraj Patil | Data Scientist

Building real AI systems in industry 🏭
4+ yrs | Automotive AI | GCP | LLMs
I show you how production DS actually works.

[Niraj Patil | Data Scientist @Nee_0298]

Most DS teams choose their metric after building the model.

That's already too late.
The metric is a business decision - not a technical one.
Ask "what does it cost when we're wrong?" before writing a single line of code.
That one question changes everything. #DataScience #MLOps

[Niraj Patil | Data Scientist @Nee_0298]

Your model passed every test.

Accuracy ✅
Latency ✅
Edge cases ✅

But did you test what happens when someone deliberately feeds it poisoned data?

Most teams don't. That's exactly why it works.

#DataScience #AI

[Niraj Patil | Data Scientist @Nee_0298]

This week I'm breaking down the 3 biggest AI security risks nobody teaches DS professionals.

And what you can actually do about them.

Follow along 👇

#MLOps #MachineLearning #LLMs #TechIndia

[Niraj Patil | Data Scientist @Nee_0298]

Your AI app can be hijacked without touching a single line of your code.

No server breach. No stolen credentials.

Just a few crafted words typed into your own interface.

Most DS professionals have never heard of this. 🧵

#DataScience #AI #SecureAI

[Niraj Patil | Data Scientist @Nee_0298]

You didn't build a security flaw.

You built an AI system without knowing AI has its own attack surface.

One that your security team has never seen before.

Are you checking for this before you ship?

[Niraj Patil | Data Scientist @Nee_0298]

One thing I keep seeing in DS Twitter: people debating tools.
Nobody debates this enough: are you solving a real problem?
That's the only question that matters in industry.
See you Monday.
#DataScience #MLOps #AI

[Niraj Patil | Data Scientist @Nee_0298]

Kaggle rank = 0 in my first DS interview.
They didn't ask about my projects.
They asked: "How would you handle data that keeps changing every week?"
Industry cares about real problems. Kaggle doesn't have those.

#DataScience #AI #MachineLearning #RealTalk #Data #DataEngineering

[Niraj Patil | Data Scientist @Nee_0298]

Everyone told me to learn Python, SQL, and ML algorithms.
Nobody told me that explaining a model to a non-technical manager is 10x harder than building it.
Communication is a DS skill. Treat it that way.
#Communication #DataScience #AI

[Niraj Patil | Data Scientist @Nee_0298]

The DS role that pays best in industry is not the one that builds the most models.
It's the one that understands the business problem better than the business team.
What's the most surprising thing you learned about DS in your first year?
#MachineLearning #TechIndia #AI

[Niraj Patil | Data Scientist @Nee_0298]

4 years in Data Science.
Here's what nobody told me when I started.
#DataScience 🧵

[Niraj Patil | Data Scientist @Nee_0298]

Your first 3-6 months in DS will be 80% data cleaning.
Not modelling. Not AI. Not LLMs.
Cleaning, joining, fixing broken pipelines.
Nobody says this. Now you know.

[Niraj Patil | Data Scientist @Nee_0298]

Nobody asks "what does it cost when we're wrong?"

They just pick accuracy, train the model, and ship it.

That one skipped question is why most production models quietly fail.

Ask it first. Every time. #DataScience #MLOps #AI #Statistics #DeepLearning #MachineLearning

[Niraj Patil | Data Scientist @Nee_0298]

🚨 Claude Managed Agent just made 1000+ AI agent startups obsolete overnight.

Anthropic built natively what they were charging for.

This is not an update. This is a market reset.

Are you building on top of AI or are you the product? #AI #Claude #LLMs #Agent #Anthropic

[Niraj Patil | Data Scientist @Nee_0298]

Accuracy = how often you're right.
F1 = how often you're right when it matters.

For fraud, failures, defects — they are NOT the same.

One gets you claps in a meeting.
The other saves you from a production disaster.

Which one does your team default to? #DataScience #AI

[Niraj Patil | Data Scientist @Nee_0298]

Everyone validates the model.

Nobody validates the metric.

Choosing accuracy for imbalanced data isn't a beginner mistake.
It's the most common senior mistake I've seen in production. #DataScience #AI

What metric do you default to? 👇

[Niraj Patil | Data Scientist @Nee_0298]

Before your next model — ask one question:

"What does it cost when I'm wrong?"

That answer should drive your metric. Not what looks best in the notebook.

Have you shipped a model that looked good on paper but failed in the field?

#DataScience #AI #MLOps

[Niraj Patil | Data Scientist @Nee_0298]

Your model is 96% accurate.

And it's completely useless.

Here's something most DS courses never tell you 🧵

[Niraj Patil | Data Scientist @Nee_0298]

I've seen this in production.

A model built to catch rare failures scored 94%.
Stakeholders were happy. It went live.

It missed almost every failure it was built to catch.