Big data is fundamentally changing the way companies do business. From consumer behavior to predictive analytics, companies of all sizes across industries like finance, healthcare, retail, and others are now capturing, storing, and analyzing more data than ever before. Companies hope to use this data to better understand their daily operations, learn more about customer behavior, and ultimately design better products and services. While tools and technologies have emerged to accelerate the data collection process, turning data into knowledge is still a cumbersome process. More companies now rely on full-fledged teams to address this issue, driving higher demand for data scientists, analysts, and engineers.
Data scientists use machine learning algorithms for model training, a highly iterative step in the overall data science workflow. Model training is further slowed by the fact that these algorithms are traditionally run on CPUs, making it more time consuming and more prone to stifling innovation.
Data scientists today use a wide array of computing resources from shared resources like CPU clusters, to do-it-yourself personal computers, and even laptops. Companies try to add data center computing resources but shrinking CapEx budgets limit the ability to scale computing resources for data science projects. As the number of data science projects increase within an organization, new tools and technologies are needed to provide the necessary compute power and to maximize the efficiency of scarce data science resources.
Companies today have access to massive amounts of data. While this data can provide companies with valuable insights, processing and extracting the right information is a challenge. The time it takes to wrangle, prepare, and clean data from multiple data stores can be significant. Once the data is prepared, models must be created, trained and refined, and then the results visualized to glean insights. Each step of this process takes a considerable amount of time and often leads to a slow user experience, particularly when run on traditional CPUs.
NVIDIA optimized TensorFlow and PyTorch
Data scientists, engineers, and analysts across industries like retail, financial services, consumer Internet, healthcare, retail, manufacturing, oil & gas, telecom, and automotive market segments.
Data scientists who need a high-performance, enterprise-class solution that has the power and memory to handle massive amounts of data, while bringing GPU-accelerated software to machine learning workflows.
Maximize data science productivity gains with a integrated hardware and pre-installed software stack that’s ready out of the box. Experience faster model development and training with high-performance Quadro RTX GPUs so data scientists can iterate and move to production faster, reducing time to insights. Get enterprise-class reliability and support with workstations that are built and tested for the highest level of compatibility and reliability. NVIDIA also offers optional software support services for NVIDIA developed software and containers including deep learning and machine learning frameworks.
Traditional CPU-based solutions that do not provide the fast computation power necessary for data science projects.