Artificial Intelligence

Artificial Intelligence

The Challenge

The AI company faced a critical challenge: acquiring sufficient data while keeping costs under control. They needed a smarter way to collect, filter, and annotate vast amounts of information for training machine learning (ML) models in a cost-effective manner.

At the same time, they were navigating a complex landscape of potential models, searching for the perfect fit and fine-tuning it for peak performance. And as new data flowed in, the company had to ensure their models stayed sustainable and scalable, all while optimizing resources and maintaining top-tier reliability.

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Artificial Intelligence

The Solution

Infinittex teamed up with the AI company to design and implement a comprehensive solution, covering everything from data acquisition to ongoing model sustainability. This approach was driven by the following key actions:
Rapid data collection

A streamlined process was implemented to address limited data availability by analyzing the existing pipelines and strategically integrating optimized data acquisition mechanisms at key operational points. This was done seamlessly, requiring almost no additional effort from the team.

Optimization of data annotation

It’s well-known that the driving force behind supervised ML is the combination of data and its necessary annotations, often at a significant cost. This challenge was addressed through a mix of techniques, including active learning to select the most informative data points for labeling, and unsupervised ML to automatically extract patterns that served as seeds for efficient data annotation.

Rapid data collection

Once the data is annotated, the real challenge lies in navigating a vast array of possible models, where training and validation can be highly resource-intensive in both time and computational power. This is where we excelled, leveraging our multi-year experience and proven methodologies for effective model selection. We secured high-performance outcomes by focusing on optimizing key parameters, ensuring the model remained generalizable while avoiding overfitting.

Sustainability

Having a champion model is just the beginning—it’s the tree, not the forest. The real need is a broader framework that ensures sustainability as new data and evolving requirements emerge. We tackled this challenge by establishing a structured operational process that spans across various datasets and models. This was further supported by adopting key metrics to quantify dataset and model properties, such as inter-annotator agreement and the ratio of intra- and inter-class distances, ensuring a robust and adaptable system moving forward.

DEALIO X

The Results

Achieved five times more data while reducing annotation costs by 70%
Enhanced model performance by 20-25%
Increased readiness for deploying new models by 40%

DEALIO X

The Conclusion

Infinittex’s involvement in the early stages of Dealio’s platform development was key to the successful launch and ongoing performance of DealioX. By providing exceptional DevOps support and cloud architecture redesign, Infinittex enabled Dealio to overcome initial challenges and establish a strong, cost-efficient foundation for their platform.

This partnership showcases Infinittex’s commitment to helping clients achieve their goals through expert guidance and tailored solutions, ultimately driving business success and competitive advantage.