Artificial Intelligence breakthroughs often grab headlines with powerful new models. But behind the scenes, there’s a less glamorous – yet critical – factor that determines success: data.
When our founding team at Akridata started researching opportunities in the AI market, we ran countless brainstorming and “assumption shredding” sessions. What survived was one clear insight: in deep learning (DL) and computer vision (CV), data is the real bottleneck.
Why is Data the real Bottleneck?
While tech giants like Google, Amazon, Microsoft, Meta, and Apple dominate AI innovation, their biggest advantage isn’t just algorithms – it’s exabytes of high-quality, well-labeled data.
Most organizations outside this elite circle will struggle in the face of the main data-related challenges:
- Limited access to large datasets & data acquisition cost
- Low-quality or inconsistent labels
- Missing edge-case scenarios
Without solving these issues, even the best model will most likely fail in real-world conditions.
Industries Feeling the Pressure
Data is a bottleneck across different industries, affecting all companies and sectors.
- Automotive – Autonomous vehicle leaders like Waymo and Tesla have massive datasets. Smaller players struggle to match that scale.
- Retail & Industrial – Many projects are still in early AI adoption stages and lack curated data pipelines.
- Healthcare & Surveillance – Data collection is complicated with few examples of the real edgecases.
This is where Akridata’s Vision Copilot saves hours on data curation, preparing it for model training and testing.
Steps to Improve Your AI Data Pipeline
Vision Copilot was designed to help teams from across the globe to build a clean dataset for training and testing a DL model for computer vision tasks. It supports every step along the data journey:
- Audit your current data – visualization of raw data provides insights about outliers, imbalance in the data, and allows you to understand what you have. .
- Data Selection – choosing the most relevant subset of the data will save costs and time. Use smart sampling, visual-based or text-based approaches to select the most relevant set of data.
- Standardize your labeling process – Consistent annotation provides quality GT data to train your model on and test your model against..
- Use synthetic data – Fill dataset gaps and simulate hard-to-capture events.
- Evaluate model’s accuracy – Complete the loop from the evaluation metrics back to the data.
Vision Copilot supports you along the data journey, and even provides model training capabilities.
The Takeaway
AI success isn’t just about better models – it’s about better data.
By combining curated datasets, high quality synthetic data, and disciplined data management, you can unlock real competitive advantages.
Ready to improve your AI data strategy?
Explore how Akridata can simplify dataset discovery, curation, and quality checks.
Contact us for a tailored consultation.
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