Run any open-source LLMs, such as Llama, Gemma, as OpenAI compatible API endpoint in the cloud.

CLIP as a service - Embed image and sentences, object recognition, visual reasoning, image classification and reverse image search

Resources of our survey paper "A Comprehensive Survey on AI Integration at the Edge: Techniques, Applications, and Challenges"

Streamlining the process for seamless execution of PyCoral in running TensorFlow Lite models on an Edge TPU USB.

EmbeddedLLM: API server for Embedded Device Deployment. Currently support CUDA/OpenVINO/IpexLLM/DirectML/CPU

Генерация описаний к изображениям с помощью различных архитектур нейронных сетей

Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.

The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

Example distributed system for ML model inference by using Kafka, including spring boot REST+JPA server with Java consumer program

This project is a web-based application that uses a pre-trained Mask R-CNN model to detect and classify car damage types (scratch, dent, shatter, dislocation) from images. Users can upload an image of a car, and the application will highlight damaged areas with bounding boxes and masks, providing a clear visual representation of the detected damage

Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.

The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

Successfully fine-tuned a pretrained DistilBERT transformer model that can classify social media text data into one of 4 cyberbullying labels i.e. ethnicity/race, gender/sexual, religion and not cyberbullying with a remarkable accuracy of 99%.

This repository contains Python code to classify fashion items using a Convolutional Neural Network (CNN) implemented with TensorFlow and Keras. It includes data preprocessing, model building, training, evaluation, and visualization of results.

Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.

POC of image classification using scikit-learn.

A cloud run function to invoke a prediction against a machine learning model that has been trained outside of a cloud provider.