Prediction and forecasting are powerful tools that enable organizations to model future trends. With NVIDIA's Accelerated Data Science, organizations can extract highly accurate insights from large datasets to make data-driven decisions.

Building tomorrow's business today

The growing practice of data science has made prediction and forecasting more common in organizations, allowing companies to leverage large historical data sets to gain insights into the future.

Although predictive analytics are highly valuable, building effective, accurate models is a tedious process. Due to the data-intensive nature of predictions, data science teams often face computational bottlenecks and significant cycle times with CPU-based implementations. NVIDIA provides accelerated solutions that build on common data science solutions to solve common problems and help organizations get the most out of their data - from software to hardware.

The challenges of forecasting and prediction

It is time consuming

Making accurate predictions requires a large amount of data. As Big Data use cases continue to grow, CPU power becomes a major bottleneck. These constraints increase cycle time and costs.

It is expensive

Making accurate predictions requires a large amount of data. As Big Data use cases continue to grow, CPU power becomes a major bottleneck. These constraints increase cycle time and costs.

It is frustrating

Producing a large-scale prediction process is tedious. It usually requires extensive software reengineering and inter-team exchanges, which can significantly delay the extraction of insights.

The challenges of forecasting and prediction

Less waiting

Spend less time waiting for processes to complete and more time solving difficult problems with solutions that handle large data sets.

Better results

Use all your data to make better business decisions, improve business performance, and better meet customer needs.

No refactoring

Use your favorite tools to experiment with models, build them, and put them into production - with no learning curve and minimal changes to your code.

Higher ROI

Get more out of your data and your budget with accelerated solutions that reduce infrastructure costs and data center footprint.

Better decisions

Generate highly accurate insights from your data to better understand trends and maneuver your business to succeed in future markets.

Seamless scaling

With tools that can easily scale from a laptop to a large multi-node, multi-GPU cluster, you can easily move from experimentation to production.

NVIDIA Full-Stack Solutions for Data Science

NVIDIA provides solutions to accelerate prediction in your business, whether you are building new models from scratch or optimizing business-critical processes. Through holistic software and hardware development, NVIDIA provides enterprise-grade solutions that make it easy for companies to gain insights and deploy models to improve operations or better serve customers. With RAPIDS™ and CUDA®, data scientists can accelerate prediction and forecasting pipelines on NVIDIA GPUs and reduce operations such as loading, processing, and training data from days to minutes. NVIDIA accelerated computation can be used with familiar Python or Java-based languages, making it easy to get started with accelerated data science. Global NVIDIA Enterprise Support is available with NVIDIA AI Enterprise, an end-to-end AI software suite, and includes guaranteed response times, priority security notifications, regular updates, and access to NVIDIA AI experts.

Accelerate your data science toolchain with ease

Leverage accelerated computing power for machine learning
to Deep Learning - all without having to learn new tools.
Software Stack

Accelerate your data science toolchain with ease

This new foundation dramatically lowers the barrier to entry through interoperability. Integrations with leading data science frameworks such as Apache Spark, cuPY, Dask, XGBoost, and Numba, as well as numerous deep learning frameworks such as PyTorch, TensorFlow, and Apache MxNet, expand adoption and promote further integration into the data science ecosystem. With GPU acceleration, innovations in the machine learning ecosystem such as RAPIDS Hyperparameter Optimization (HPO) and RAPIDS Forest Inferencing Library (FIL) reduce once time-consuming operations to a matter of seconds.

Amazing results from our customers

More and more companies are turning to Data Science to improve and refine their business. Some of the most innovative companies are achieving incredible results with accelerated data science solutions from NVIDIA. By using accelerated computing, companies can process more and more data, reduce infrastructure costs, and better serve their customers.
Walmart Labs Logo
Walmart is one of the largest retailers in the world. To continually improve and meet the needs of its customers, Walmart must drive innovation throughout the company. Walmart Labs is the team that drives that innovation.

Walmart Labs relies heavily on Data Science to accurately predict inventory needs across its thousands upon thousands of stores worldwide. While the operations have always been highly efficient, RAPIDS and NVIDIA GPUs have improved predictive performance by 1.3 percent, saving Walmart millions and helping more customers get what they need.
100x faster model training
97x Lower computing costs
Accurate results for less money to better serve customers
Capital One Logo
Capital One has always been a data-driven company. To better serve its customers, Capital One has made a significant transformation, moving from a bank that uses technology to a technology company that runs banks.

As part of this transition, Capital One has embraced Data Science. To foster a data-driven culture, Capital One has adopted RAPIDS, Dask, and NVIDIA GPUs, significantly improving performance and return on investment for its predictive operations. Powered by NVIDIA's accelerated data science, Capital One has the tools they need to truly change banking forever
100x faster model training
20x reduced computing costs
Savings in the millions through improved forecasting processes

The challenges of forecasting and prediction

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Data Center

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