Systeme und Informatikanwendungen Nikisch GmbHsysGen GmbH - Am Hallacker 48a - 28327 Bremen - info@sysgen.de

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INCREASE MODEL ACCURACY AND DIRECTLY IMPACT THE BOTTOM LINE
WITH HIGHLY OPTIMISED PIPELINES FOR MACHINE LEARNING

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

Companies are using machine learning to improve their products, services and operations. By using large amounts of historical data, companies can build models to predict customer behaviour and refine internal processes. While machine learning brings incredible value to a business, current CPU-based methods can increase complexity and overhead, reducing the return on investment for companies.

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
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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.

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.

NO REFACTORING

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 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.