Computers in the Cloud
A gateway to high-performance computing, designed specifically for business faculty research
Common Scenerios
GPU and High RAM (e.g. Python)
Imagine you are a marketing analyst working for an e-commerce company. Your task is to analyze customer reviews to understand sentiment, identify common issues, and gauge customer satisfaction. The dataset consists of millions of reviews, each containing detailed feedback. Using a GPU with Python is crucial in this scenario.
- Processing Large Volumes of Text Data: Customer reviews can be extensive, and processing such large datasets requires substantial memory (RAM) to efficiently load and manipulate the text data.
- Training Advanced NLP Models: Natural Language Processing (NLP) models, especially deep learning models like BERT (Bidirectional Encoder Representations from Transformers), require significant computational power for training and fine-tuning, which is best handled by a GPU.
High RAM (e.g. Using R)
Imagine you are a business analyst working on accounting data analysis for a large multinational corporation. You need to analyze years of financial transactions, involving millions of records, to identify trends, anomalies, and potential areas for cost savings. High RAM is essential in this scenario to:
- Load Large Datasets into Memory: High RAM allows you to load extensive accounting datasets into memory for efficient processing.
- Run Complex Financial Models: Advanced models and algorithms that analyze financial data require significant memory.
- Perform Real-Time Analytics: High RAM supports real-time data processing and analytics, enabling you to make timely decisions based on current data
- Avoid Memory Swapping: With high RAM, the system can handle large data operations without resorting to disk swapping, which significantly slows down processing.
Long Compute Times - Powerful CPU and RAM
Imagine you are a data scientist working for a large bank. Your task is to develop and run financial risk models to predict potential risks and make informed decisions on credit scoring, fraud detection, and investment strategies. The dataset includes millions of transaction records, historical financial data, and customer profiles. Using a dedicated machine with a powerful CPU and high RAM is crucial in this scenario.
- Processing Large Financial Datasets: Financial datasets are often vast and complex, requiring substantial memory (RAM) to efficiently load and manipulate the data.
- Running Advanced Statistical Models: Financial risk models, including Monte Carlo simulations, regression analysis, and machine learning algorithms, require significant computational power for training and execution, which can be efficiently handled by a powerful CPU.
Options
Why Use the Computer in the Cloud (CiC) Service?
Performance
- Access state-of-the-art machines with high RAM and powerful GPUs tailored for deep learning and complex computations
- Experience seamless performance 24/7, ensuring your research is never hindered by hardware limitations
Flexibility and Convenience
- Enjoy the flexibility of "renting" our high-end machine whenever you need them, with the hassle of maintenance and upgrades
- Work from anywhere, anytime, with our always-available cloud-based solution
Cost-Effective Solutions
- Save on the costs assocaiated with purchasing high-performance hardware
- Pay only for the resources you use optimizing your research budget or PDF