{"id":3735,"date":"2024-09-25T14:55:27","date_gmt":"2024-09-25T14:55:27","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2024\/09\/25\/decoding-how-ai-can-accelerate-data-science-workflows\/"},"modified":"2024-09-25T14:55:27","modified_gmt":"2024-09-25T14:55:27","slug":"decoding-how-ai-can-accelerate-data-science-workflows","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2024\/09\/25\/decoding-how-ai-can-accelerate-data-science-workflows\/","title":{"rendered":"Decoding How AI Can Accelerate Data Science Workflows"},"content":{"rendered":"
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Editor\u2019s note: This post is part of the <\/i>AI Decoded series<\/i><\/a>, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for RTX workstation and PC users.<\/i><\/p>\n

Across industries, AI is driving innovation and enabling efficiencies \u2014 but to unlock its full potential, the technology must be trained on vast amounts of high-quality data.<\/p>\n

Data scientists play a key role in preparing this data, especially in domain-specific fields where specialized, often proprietary data is essential to enhancing AI capabilities.<\/p>\n

To help data scientists with increasing workload demands, NVIDIA announced that RAPIDS cuDF<\/a>, a library that allows users to more easily work with data, accelerates the pandas software library with zero code changes. Pandas<\/a> is a flexible, powerful and popular data analysis and manipulation library for the Python programming language. With cuDF, data scientists can now use their preferred code base without compromising on data processing speed.<\/p>\n

NVIDIA RTX<\/a> AI hardware and technologies can also deliver data processing speedups. They include powerful GPUs that deliver the computational performance necessary to quickly and efficiently accelerate AI at every level \u2014 from data science workflows to model training and customization on PCs and workstations.<\/p>\n

The Data Science Bottleneck<\/b><\/h2>\n

The most common data format is tabular data, which is organized in rows and columns. Smaller datasets can be managed with spreadsheet tools like Excel, however, datasets and modeling pipelines with tens of millions of rows typically rely on dataframe libraries in programming languages like Python.<\/p>\n

Python<\/a> is a popular choice for data analysis, primarily because of the pandas library<\/a>, which features an easy-to-use application programming interface (API). However, as dataset sizes grow, pandas struggles with processing speed and efficiency in CPU-only systems. The library also notoriously struggles with text-heavy datasets, which is an important data type for large language models<\/a>.<\/p>\n

When data requirements outgrow pandas\u2019 capabilities, data scientists are faced with a dilemma: endure slow processing timelines or take the complex and costly step of switching to more efficient but less user-friendly tools.<\/p>\n

Accelerating Preprocessing Pipelines With RAPIDS cuDF\u00a0<\/b><\/h2>\n
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RAPIDS cuDF speeds the popular pandas library up to 100x on RTX-powered AI PCs and workstations.<\/figcaption><\/figure>\n

With RAPIDS cuDF<\/a>, data scientists can use their preferred code base without sacrificing processing speed.<\/p>\n

RAPIDS<\/a> is an open-source suite of GPU-accelerated Python libraries designed to improve data science and analytics pipelines. cuDF<\/a> is a GPU DataFrame library that provides a pandas-like API for loading, filtering and manipulating data.<\/p>\n

Using cuDF\u2019s \u201cpandas accelerator mode,\u201d data scientists can run their existing pandas code on GPUs to take advantage of powerful parallel processing, with the assurance that the code will switch to CPUs when necessary. This interoperability delivers advanced, reliable performance.<\/p>\n

The latest release of cuDF<\/a> supports larger datasets and billions of rows of tabular text data. This allows data scientists to use pandas code to preprocess data for generative AI use cases.<\/p>\n

Accelerating Data Science on NVIDIA RTX-Powered AI Workstations and PCs<\/b><\/h2>\n

According to a recent study, 57% of data scientists use local resources such as PCs, desktops or workstations<\/a> for data science.<\/p>\n

Data scientists can achieve significant speedups starting with the NVIDIA GeForce RTX 4090 GPU<\/a>. As datasets grow and processing becomes more memory-intensive, they can use cuDF to deliver up to 100x better performance with NVIDIA RTX 6000 Ada Generation GPUs<\/a> in workstations, compared with traditional CPU-based solutions.<\/p>\n

\"A<\/a>
Two common data science operations \u2014 \u201cjoin\u201d and \u201cgroupby\u201d \u2014 are on the y-axis, while the x-axis shows the time it took to run each operation.<\/figcaption><\/figure>\n

Data scientists can easily get started with RAPIDS cuDF on NVIDIA AI Workbench<\/a>. This free developer environment manager powered by containers enables data scientists and developers to create, collaborate and migrate AI and data science workloads across GPU systems. Users can get started with several example projects available on the NVIDIA GitHub<\/a> repository, such as the cuDF AI Workbench project<\/a>.<\/p>\n

cuDF is also available by default on HP AI Studio<\/a>, a centralized data science platform designed to help AI developers seamlessly replicate their development environment from workstations to the cloud. This allows them to set up, develop and collaborate on projects without managing multiple environments.<\/p>\n

The benefits of cuDF on RTX-powered AI PCs and workstations extend beyond raw performance speedups. It also:<\/p>\n