Machine learning helps companies gain a more detailed view of their customers in order to develop tailored products and services. Companies also benefit from smooth operations in production and supply chains by means of artificial intelligence. With the acceleration in data science, companies can iterate and productively place solutions faster than ever before. At the same time, you can leverage massive data sets to refine models to the point.
FASTER PREDICTIONS FOR BETTER DECISIONS
While machine learning provides incredible value to an organization,
current CPU-based methods can increase complexity and overhead, reducing return on investment.
current CPU-based methods can increase complexity and overhead, reducing return on investment.
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 perform rapid feature iterations, leverage massive datasets for highly accurate predictions, and effortlessly move value-added solutions into production. Data scientists can easily access GPU acceleration via some of the most popular Python or Java-based APIs, enabling a quick start - whether in the cloud or on-premise.
By harnessing the power of accelerated machine learning, organizations can give data scientists the tools they need to get the most out of their data.
By harnessing the power of accelerated machine learning, organizations can give data scientists the tools they need to get the most out of their data.
THE CHALLENGES OF MACHINE LEARNING
MODEL ITERATION ADDS LABOR
Iteration means waiting for results and consuming more computing power. Although iteration leads to better results, data science teams often limit iteration to deliver solutions faster.
DOWNSAMPLING MEANS LESS ACCURATE MODELS
Data science teams often face downsampling of datasets due to limited computing power, leading to less accurate results and suboptimal business decisions.
MODELS TO PRODUCE
IS LABORIOUS
IS LABORIOUS
Handing over models to production is incredibly time-consuming and tedious, often requiring extensive code refactoring, which increases cycle time and delays value creation.
THE ADVANTAGES OF ACCELERATED MACHINE LEARNING
LESS WAITING
Spend less time waiting for processes to complete and more time iterating and testing solutions with a solution that is 19 times faster than the CPU-based industry standard.
BETTER RESULTS
Analyze multi-terabyte datasets with powerful processing to get more accurate results and faster reports.
NO REFAcTORiNG
Accelerate and scale your existing data science toolchain without having to learn new tools and with minimal code changes.
LESS EXPENSES
Get the most out of your budget with GPU acceleration - a solution that's 7x less expensive than the CPU-based industry standard.
BETTER DECISIONS
Use all your data to make better business decisions, improve business performance, and better meet customer needs.
SEAMLESS SCALING
Effortlessly scale from a desktop to multi-node, multi-GPU clusters with a consistent, intuitive architecture.
FURTHER DEVELOPMENT OF THE MACHINE LEARNING ECOSYSTEM
NVIDIA offers solutions to accelerate machine learning in your organization, whether you are building a new model from scratch or fine-tuning the performance of business-critical processes. NVIDIA offers solutions that combine hardware and software optimized for high-performance machine learning to make it easy for organizations to generate insightful insights from their data. With RAPIDS and NVIDIA CUDA, data scientists can accelerate machine learning pipelines on NVIDIA GPUs, reducing machine learning operations such as loading, processing, and training data from days to minutes. The power of CUDA can be leveraged by familiar Python or Java-based languages, making it easy to get started with accelerated machine learning.
CUML WITH SINGLE GRAPHICS PROCESSOR VS. SCIKIT-LEARN
1 x v100 vs. 2 x CPU with 20 processing units
RAPIDS provides the foundation for a new high-performance data science ecosystem and lowers the barrier to entry through interoperability. Integration 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, broadens adoption and drives integration with others. GPU acceleration reduces ecosystem innovations for machine learning such as. RAPIDS hyperparameter optimization (HPO) and RAPIDS Forest Inferencing Library (FIL) reduce once time-consuming operations to a matter of seconds.
Accelerated computing solutions for machine learning
PC
Familiarize with machine learning.
Workstations
Novel workstations for data science.
Cloud & Data Center
AI systems for enterprise production.