Fine-Tuning Service with AWS

Fine-tuning adapts a pre-trained model to specific business needs, improving accuracy, efficiency, and alignment with business goals.

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Enabling Enterprise and SMB to customize and enhance the accuracy of their GenAI models for domain-specific tasks

By leveraging best-in-class human data training capabilities, including Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), we ensure that models align perfectly with business needs.

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Advanced AI training services combined with AWS infrastructure

Through delivering AI training services via AWS infrastructure, such as Amazon Bedrock and AWS SageMaker, we offer customers a seamless, scalable solution for model enhancement. Supported by a network of over 3,000 highly skilled AI trainers and domain experts, our tech-enabled service delivers high-quality human-generated data to elevate model performance and achieve superior user satisfaction.

Supervised Fine-Tuning (SFT) with Demonstration Data

Drive relevancy and accuracy in your generative AI applications with our comprehensive Supervised Fine-Tuning service, tailored for both model producers and application builders.

Generate task-specific datasets

Our SFT service involves generating task-specific demonstration datasets to fine-tune foundation models for specific tasks like answering questions or summarizing text. We provide labeled examples of completed tasks, guiding models to recognize human behavior patterns and improve their performance.

We enhance the relevancy and accuracy of generative AI applications with industry-specific or company-specific data, ensuring that models are finely tuned to meet your unique needs.

Through our SFT service, application builders can significantly improve their AI models' performance, making them more effective and reliable in real-world scenarios.

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Instruction Tuning with Preference Data (RLHF)

Ensure your AI models meet user expectations with our advanced techniques for aligning models with human preferences through comprehensive human feedback.


Collect preference data

We gather preference data by having humans rate, rank, or compare model outputs based on attributes like helpfulness, accuracy, and harmlessness.


Instruction tuning methods

This preference data is used in instruction tuning methods, including Reinforcement Learning from Human Feedback (RLHF), to fine-tune models.


Enhance model performance

By aligning models with human preferences, we ensure they perform better on tasks that matter to users, improving overall effectiveness.


Deliver optimal experiences

Our approach guarantees that models meet user expectations and deliver optimal user experiences, enhancing satisfaction and usability.

Benefits of Fine-tuning as a Service

Overall, fine-tuning is a powerful tool for businesses looking to leverage AI technologies to enhance their operations and decision-making processes, tailored precisely to their unique environmental and market demands.


1

Enhanced accuracy

Fine-tuning tailors AI models to specific business contexts, drastically improving accuracy in specialized tasks.


2

Cost efficiency

By building on existing pre-trained models, businesses can save significant resources and time compared to training a model from scratch.


3

Competitive advantage

Fine-tuned models can provide more relevant insights and predictions, giving businesses a competitive edge in their industry.


4

Scalability

Fine-tuning allows businesses to scale their AI solutions efficiently, adapting to new challenges and data as they grow.


5

Customization

Through techniques like SFT and RLHF, businesses can customize AI behavior to closely match human preferences or operational requirements, ensuring that the model performs optimally in real-world applications.

Invisible’s engagement model

Invisible engages with you on a scoping call to ensure we are aligned on client needs and goals before issuing an SOW. Your account manager will set up weekly calls for a tight feedback loop to ensure data quality and meeting SLAs.

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