Today, anyone with a basic computer can perform powerful data analysis and extract valuable insights without needing expensive, sophisticated equipment.

With an average computer, you can, for example, use one of Python’s open-source libraries and perform linear or logistic regressions, time series analysis, or apply classical inferential statistics methods. These tasks are generally light on computational requirements and don’t demand advanced hardware.

However, when it comes to big data, more advanced tasks are required. Here’s how GPU cloud computing can be useful for big data analytics.

What is Big Data Analytics?

Big data analytics refers to a more specialized branch of data science and data analysis. It handles massive, complex and diverse datasets that typically require specialized tools and platforms.

Big data analytics is characterized by the 3 Vs:

• Volume – The amount of data.
• Velocity – How fast the data moves through the system.
• Variety – The diverse forms of data (from structured and semi-structured data to unstructured data like text, video and images from various sources).

What is a GPU?

GPUs (Graphics Processing Units) are specialized computer processors.

Unlike CPUs, which have a few cores (smaller processing units) optimized for sequential tasks, GPUs have thousands of cores that can execute many operations simultaneously.

Originally designed for rendering graphics, GPUs have become necessary for various computational tasks, particularly in artificial intelligence (AI) and machine learning, where large-scale data processing is common.

Platforms like CUDA, developed by NVIDIA, enables developers to take advantage of GPU parallel processing power for complex calculations. Popular libraries like TensorFlow, PyTorch, and cuDF are optimized for CUDA, which makes it faster to train models and process data.

What is On-Demand GPU?

An on demand GPU (also known as cloud GPU) is a GPU that you can access over the Internet. It offers fast computational resources as you need them.

The main benefits of On-Demand GPUs over “On-Premises” GPU include:

• Cost efficiency

You don’t need to make a big investment on expensive hardware.

• No resource shortages

You have access to boundless storage and computational power anytime so that you can scale up or down depending on task requirements.

Read our recent blog: What is GPU cloud computing?

How Can Cloud GPUs Help With Big Data Analysis?

Due to its parallel (fast) processing and scalable infrastructure, cloud GPUs have proven to be great for individuals and companies interested in artificial intelligence and machine learning, which can greatly improve data scientists’ productivity and creativity.

For example, if a data scientist for a small finance company wanted to go beyond classical regression models and start experimenting with neural networks to predict something like future stock prices, he could use a model like a LSTM (Long Short-Term Memory) model. LSTM is a type of neural network that’s popular for time series data.

Here is how cloud GPUs could help:

1. Faster training

Training an LSTM network with millions of historical data points and multiple predictors on a regular computer could take several hours or even days. A cloud GPU to create the LSTM model, the data scientist could reduce the training time to minutes or just a couple of hours.

2. Real-time predictions

For the LSTM model to be really useful for financial decisions, real-time performance is important. A cloud GPU could execute the LSTM model quickly to ensure that predictions are available in milliseconds.

3. Scalability

Imagine that the data scientist also wanted to complement the stock price data with additional information, such as market sentiment obtained from social media platforms like X, formerly known as Twitter, or financial news outlets. Through natural language processing (NLP) techniques, the data scientist could quantify these emotions and use them as complementary indicators in market prediction models beyond just analyzing prices. A cloud GPU could provide scalability to handle increasing data and model complexity.

A cloud GPU would accelerate training of a custom data analysis machine learning model, helping that data scientist and his company gather faster insights, act on those insights faster than before, and gain an edge over the competition.

Take your data science work to the next level with advanced machine learning models, large datasets, and complex tasks like natural language processing by renting a GPU virtual machine in the cloud.

Massed Compute offers NVIDIA cloud GPUs. Use the coupon code MassedComputeResearch for 15% off any GPU rental.