Success Story

AI model training for Scale.com- a global leader in GenAI applications

About the client


Headquartered in San Francisco, Scale AI is the go-to solution for businesses seeking to transform raw, unstructured data into high-quality training datasets, thereby fueling the advancement of AI technology across various industries. Scale AI powers the most advanced LLMs and generative models in the world through world-class RLHF, data generation, model evaluation, safety, and alignment.

Scale AI is trusted by leading technology companies such as Microsoft and Meta, enterprises such as Fox and Accenture, Generative AI companies such as Open AI and Cohere, U.S. Government Agencies such as the U.S. Army and the U.S. Air Force, and Startups such as Brex and OpenSea.

  • 98% Accuracy achieved
  • 7.7B Annotations to date
  • 1B+ Scenes labeled

Business Situation

Scale AI, a leader in data annotation and AI/ML products, needed to optimize the training of their AI models for increased efficiency. The success of their comprehensive solutions for ML lifecycle management relied on achieving precision and speed in model training.

Scale AI required a strategic partner adept at confidently managing large-scale, complex model training. The goal was to bring clarity to ML training operations, increase efficiency, and ensure scalability, specifically in ML model development.

Scale AI team sought a partner who could seamlessly integrate AI into their operations, providing not just technical expertise but a holistic approach to model training that aligned with their broader business objectives.

To meet this demand, Scale AI reached out to Daffodil Software to engage multiple Python developers on a Time and Material (TnM) model to develop and improve machine learning models and natural language processing (NLP) algorithms for their portfolio of products.

Key expectations from Daffodil Software were:


  • Streamline and enhance the efficiency of AI model training processes.
  • Ensure scalability with evolving ML model development requirements.
  • Implement best practices to improve ML & training processes.
  • Minimize bottlenecks, reduce errors, and enhance overall operational efficiency.
  • Ensure timely delivery to facilitate prompt integration of improved AI and ML models into operations.

The Solution

In order to tackle Scale AI’s challenges, Daffodil Software embarked on a collaborative journey by understanding Scale AI’s unique needs, aligning with their vision, and strategically deploying technology experts to enable ML model training.

Here’s how team Daffodil enabled the success of Scale AI’s ML model:


Collaborative team building:
Daffodil Software recognized that addressing Scale AI’s challenges required a specialized team. The focus was on curating a specialized team equipped with the right technological expertise, including proficiency in Python, Django, C#, React, CoreJs, and Java. This strategic composition of the team aimed to create a dynamic and synergistic environment where each team member brought a unique skill set to the table.

Model training process:
Developers from Daffodil Software were strategically tasked with training complex models. The training process involved responding to a set of technical questions posed by a system developed by the Scale AI team. This interactive approach ensured that the developers were not just coding but actively engaging with the intricacies of the models they were training.


Rigorous evaluation process:
The developers were subjected to a meticulous evaluation process by Scale AI’s audit team to ensure a comprehensive assessment of responses and the performance of the model. Specific technical questions had to be answered within a stipulated time frame. Further, the logic behind their responses underwent cross-examination. This rigorous evaluation was required to deliver high-quality, error-free ML models.

Focus on efficiency and best practices:
There was an overarching focus on efficiency. Daffodil Software streamlined ML learning processes and maximized the effectiveness of model training. The team rigorously ensured best practices at each step in the training process & contributed to the overall efficiency and quality of the models.


The Impact

The training of ML models resulted in high efficiency & accuracy of Scale AI's products. By incorporating best practices in knowledge processing, the collaboration significantly reduced handoff and deployment errors. Bulk data training achieved an impressive accuracy rate of over 98%.The success of the collaboration was evident as the Scale AI team promoted the developers from Daffodil Software, to their audit team, recognizing the enhanced performance and impact on their operations.Daffodil Software's expertise in developing and improving ML models, coupled with Scale AI's visionary strategy, resulted in operational clarity, increased efficiency, and scalability for powerful ML models, thereby contributing significantly to the advancement of AI applications.

  • 98% Accuracy achieved
  • 7.7B Annotations to date
  • 1B+ Scenes labeled

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