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 through 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 use huge data sets to refine models to the point.

FASTER PREDICTIONS FOR BETTER DECISIONS

While machine learning brings incredible value to an organisation,
current CPU-based methods can increase complexity and overhead, reducing return on investment.

With a data science acceleration platform that combines optimised 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 them to get started quickly - whether in the cloud or on-premise.

By harnessing the power of accelerated machine learning, organisations can give data scientists the tools they need to get the most out of their data.

THE CHALLENGES OF MACHINE LEARNING

MODEL ITERATION ADDS LABOUR

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 sehen sich häufig mit Downsampling von Datensätzen konfrontiert, weil die Rechenleistung begrenzt ist, was zu weniger genauen Ergebnissen und suboptimalen Geschäftsentscheidungen führt.

PRODUCING MODELS
IT IS HARD

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 finish and more time iterating and testing solutions with a solution that is 19 times faster than the CPU-based industry standard.

BETTER RESULTS

Analyse multi-terabyte datasets with powerful processing to get more accurate results and faster reports.

KEINE REFAkTORIERUNG

Accelerate and scale your existing data science toolchain without having to learn new tools and with minimal code changes.

LESS SPENDING

Get the most out of your budget with GPU acceleration - a solution that is 7 times more cost-effective 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 organisation, whether you're building a new model from scratch or fine-tuning the performance of business-critical processes. NVIDIA offers solutions that combine hardware and software optimised for high-performance machine learning to make it easy for businesses 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 harnessed 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 encourages integration with others. Through GPU acceleration, machine learning ecosystem innovations such as RAPIDS Hyperparameter Optimisation (HPO) and RAPIDS Forest Inferencing Library (FIL) reduce once time-consuming operations to a few seconds.