AI Development
AI development proceeds with data as its essence. The quality and quantity of this data significantly influence the accuracy of AI. However, the accuracy of AI tends to degrade over time. To address this, continuous monitoring of the model and data, as well as periodic retraining, are indispensable. We deeply understand this and fully incorporate the concept of MLOps. While recognizing that data is perishable, we automate the monitoring and retraining of models to consistently offer peak performance.
Difficulties in AI development
AI development works around raw data as its core. Unlike regular system development, the system behavior changes depending on the data, which increases complexity. In AI systems, the proportion of machine learning models (ML Code) is very small, and peripheral elements are enormous and complex.
What Traffine can do
We support AI development considering operational phases, not only data science fields like machine learning modeling. With the awareness that data is raw, we build systems to automate model accuracy monitoring, retraining, and provide the best performance continuously.
Model accuracy monitoring
Data quality monitoring
Example Project ❶
Project
Development of Pedestrian Tracking Algorithm
Description
We utilized technologies from the Multiple Object Tracking (MOT) domain to detect and track pedestrians. In this process, we developed an algorithm to identify and rectify pedestrian IDs that get interrupted due to overlaps with other objects.
Stacks
Example Project ❷
Project
Development of Chat System Using LLM (Large Language Model) and Vector Database
Description
By integrating the OpenAI API with the Pinecone vector database, we developed a chatbot that can instantly refer to the latest documents. Queries or inputs from users are vectorized in real-time using the OpenAI API, and the transformed vectors consult Pinecone's database to fetch responses or documents with high similarity. The bot can instantly acquire updates or new knowledge from documents and promptly answer user queries.
Stacks
Example Project ❸
Project
Development of an application to detect the difference between objects in two images.
Description
Utilizing technologies such as object detection, background removal, and feature vector extraction, we developed an application that detects the difference between objects in two images.