Accelerated
Machine Learning

Increase model accuracy and directly impact the bottom line with highly optimized machine learning pipelines.

Machine learning helps businesses understand their customers, build better products and services, and improve operations. With accelerated data science, businesses can iterate on and productionize solutions faster than ever before all while leveraging massive datasets to refine models to pinpoint accuracy.

Faster Predictions for Better Decisions

Businesses use machine learning to improve their products, services, and operations. By leveraging large amounts of historical data, businesses can build models to predict customer behaviors and refine internal processes. While machine learning provides incredible value to an enterprise, current CPU-based methods can add complexity and overhead reducing the return on investment for businesses.

With a data science acceleration platform that combines optimized hardware and software, the traditional complexities and inefficiencies of machine learning disappear. Data scientists can now conduct rapid feature iteration, use massive datasets for highly accurate predictions, and deliver value-generating solutions to production with ease. Data scientists can easily access GPU-acceleration through some of the most popular Python or Java-based APIs, making it easy to get started fast whether in the cloud or on-premise.

By leveraging the power of accelerated machine learning, businesses can empower data scientists with the tools they need to get the most out of their data.

The Challenges of Machine Learning

Model iteration adds overhead

Iteration means waiting for results to return and spending more on compute power. While iteration leads to better results, data science teams often limit iteration to deliver solutions faster.

Downsampling means less accurate models

Data science teams often find themselves downsampling datasets due to limitations in computation power leading to less accurate results and suboptimal business decisions.

Productionizing models is arduous

Delivering models to production is incredibly time consuming and cumbersome, often involving substantial code refactoring, increasing cycle time and delaying value generation.

The Benefits of Accelerated Machine Learning

Data Scientists
IT Infrastructure Professionals

Testing solutions with a solution that’s 19X faster than the CPU-based industry standard

Less Wait

Spend less time waiting for processes to finish, and more time iterating and testing solutions with a solution that’s 19X faster than the CPU-based industry standard.

Better Results

Analyze multi-terabyte datasets with high performance processing to drive higher accuracy results and quicker reporting.

Accelerate Existing Data Science Toolchain

No Refactoring

Accelerate and scale your existing data science toolchain with no need to learn new tools and minimal code changes.

Less Spending

Get the most out of your budget with GPU-acceleration with a solution that’s 7X more cost effective than the CPU-based industry standard.

Better Decisions

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

Seamless Scaling

Effortlessly scale from a desktop to multi-node, multi-GPU clusters with a consistent, intuitive architecture.

Advancing the Machine Learning Ecosystem

NVIDIA offers solutions to accelerate your business’ machine learning operations, whether you’re building a new model from scratch or fine-tuning the performance of critical business-enabling processes. NVIDIA provides solutions that combine hardware and software optimized for high-performance machine learning to make it easy for businesses to generate illuminating insights out of their data. With RAPIDS and NVIDIA CUDA, data scientists can accelerate machine learning pipelines on NVIDIA GPUs, reducing machine learning operations like data loading, processing, and training from days to minutes. CUDA’s power can be harnessed through familiar Python or Java-based languages, making it simple to get started with accelerated machine learning.

Single-GPU cuML vs Scikit-learn

1x V100 vs. 2x 20 Core CPU

RAPIDS provides a foundation for a new high-performance data science ecosystem and lowers the barrier of entry through interoperability. Integration with leading data science frameworks like Apache Spark, cuPY, Dask, XGBoost, and Numba, as well as numerous deep learning frameworks, such as PyTorch, TensorFlow, and Apache MxNet, broaden adoption and encourage integration with others. Through GPU-acceleration, machine learning ecosystem innovations like RAPIDS hyperparameter optimization (HPO) and RAPIDS Forest Inferencing Library (FIL) are reducing once time consuming operations to a matter of seconds.

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Accelerate Your Machine Learning in the Cloud Today

Get started with RAPIDS on Amazon Web Services whether you’re utilizing SageMaker, EC2s or EKS

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Get started with RAPIDS on Google Cloud whether you’re using CloudAI or DataProc.

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Get started with RAPIDS on Microsoft Azure whether you’re utilizing AzureML or other instances

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Accelerated Computing Solutions for Machine Learning

PC

Get started in machine learning.

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Workstations

A new breed of workstations for data science.

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Data Center

AI systems for enterprise production.

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Cloud

Versatile, accelerated machine learning.

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Unlock the Value of Big Data with the Power of AI

Download our new e-book, Accelerating Apache Spark 3.x — Leveraging NVIDIA GPUs to Power the Next Era of Analytics and AI, to learn more about the next evolution in Apache Spark.

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