the Rise of Data-Centric Intelligence.pdf

Smarter models start with smarter data. Data-Centric AI flips the script—focusing on data quality, not just model size. It’s the secret top AI teams use to boost performance fast.

1. The Shift from Model-First to Data-First
Thinking DATA-CENTRIC AI
The Shift from Model-First
to Data-First Thinking

2. WHAT IS DATA CENTRIC AI ?Data-Centric AI is an approach to building artificial intelligence systems
which emphasizes on improving the data rather than solely improving the
algorithms.
In simple terms, think about teaching a student. The most talented student in
the world will only learn well if the material (data) is well-structured,
accurate, and complete. Data-Centric AI is similar in philosophy. Instead of
spending our days tuning a model's weight and architecture, we are putting
effort into improving the quality, accuracy and diversity of the data.
This allows us to clean datasets that are messy, remove incorrect labels and
change biased examples so that the AI learns more completely and fairly.
Summarizing, Data-Centric AI = Better data → Smarter and more reliable AI
systems.
Data-Centric AI

3. WHY DOES IT
MATTER ?Good data :
Reduces errors
Makes AI more accurate
Helps models learn faster
Reduces bias
And honestly, improving data is often easier
than building complex models.
Data-Centric AI

4. MODEL-CENTRIC VS DATA-CENTRICModel-Centric AI = Keep the data the
same, and improve the model.
Data-Centric AI = Keep the model the
same, and improve the data.
Why does this matter?
Because most AI models today are already
very powerful — they just need better data
to learn from
Data centirc Ai

5. BENEFITS OF DATA CENTRIC AI Improves model accuracy with clean, well-labeled
data.
Reduces bias by balancing data across gender,
region, language, etc.
Lowers the need for complex models by enhancing
data quality instead.
Simplifies debugging by making it easier to fix data-
related issues.
Enhances generalization so AI performs better in
real-world scenarios.
Cost-effective by reducing the need for expensive
model training.
Scalable and sustainable with reusable data
pipelines for future use. data centric AI

6. IMPACT OF DATA CENTRIC AI Improves AI accuracy, reliability, and
effectiveness.
Promotes fairer and more ethical AI by
reducing bias.
Lowers development costs by reducing
reliance on complex models.
Enables faster and easier troubleshooting
of AI systems.
Enhances real-world performance of AI
applications.
Allows reuse of clean, structured data
pipelines across projects. data centric AI

7. With the growing demand for AI, more
students are enrolling in data science course in
Kerala.
These courses are starting to include Data-
Centric AI as a key topic, because companies
now value data quality as much as model
quality.
So if you're planning to join a data science
course in Kerala, learning about data
preparation, cleaning, and labeling is just as
important as learning machine learning
algorithms.
Data centiric AI

8. Data centric AI
THANK YOU