Fine-tune a new generative model for toxic comment detection across multiple languages. Work with real-world, large-scale text data to improve model performance and reduce bias.

Develop a computer vision model to identify anomalies and outliers in process execution flows from visual data logs. Curate a unique dataset and train a baseline model for defect detection.

Build a production-ready RAG (Retrieval-Augmented Generation) system. Connect a Large Language Model to a vector database of internal help-desk articles to power a new customer service chatbot.

Conduct a comprehensive sentiment analysis of financial news articles to gauge market sentiment. Scrape, clean, and analyze data from various news sources to create a sentiment dashboard.

Develop a machine learning model to predict the likelihood of patient readmission based on electronic health records. This involves data preprocessing, feature engineering, and model validation.

Create an algorithm to optimize delivery routes in a dense urban environment, considering traffic patterns, delivery time windows, and vehicle capacity to reduce costs and emissions.

Build and deploy a collaborative filtering-based recommendation system to provide personalized product suggestions for an e-commerce platform. You will handle a large user-item interaction dataset.

Explore the creative possibilities of text-to-image models. Develop a workflow for generating high-quality artistic images from complex text prompts, focusing on prompt engineering and style control.

Train a custom YOLOv8 model to detect and classify objects in a warehouse environment. The goal is to create a system that can track inventory from a live video feed.

Build a time-series model to forecast energy output from a solar farm based on historical weather and production data. The model will help in optimizing energy grid management.

Develop a system using abstractive text summarization models (like PEGASUS) to create concise summaries of long legal contracts and court documents.

Implement a speaker diarization pipeline to identify 'who spoke when' from raw audio files of business meetings. You'll work with libraries like pyannote.audio to segment and cluster speakers.

Build a classification model using XGBoost to predict customer churn. You will analyze customer behavior data to identify key factors leading to churn and help the business retain customers.

Create a proof-of-concept system that can take a video of a person and replace the background with a scene generated from a text prompt. Involves video segmentation and generative models.