I Have NVIDIA, Why Do I Need Dataiku?

Dataiku Product, Featured Chris Harrison

Computation is crucial when building data products. If you're stuck with slow CPUs, you may lack the computing power needed for your innovative machine learning (ML) models and Generative AI (GenAI) projects.

According to an upcoming Dataiku survey, 60% of IT leaders are now leveraging GPUs in their organizations' daily AI and analytics projects. Among these, nearly half are hybrid users, with 49% utilizing both on-prem and cloud solutions, while 43% rely exclusively on cloud-based GPUs. For those not yet using GPUs, 43% plan to adopt them within the next 12 months. This highlights a critical understanding among successful IT leaders: Accelerated computing is essential for staying competitive and driving impactful results.

To do this, data experts need the right tools for both the front end and back end. They can't afford to be slowed down by manual coding or unstandardized environments. Instead, they need the most efficient computation methods, such as GPUs. This is where Dataiku and NVIDIA step in.

nvidia-and-dataiku

Dataiku streamlines data and analytics through a unified, standardized environment accessible to all skill levels, while NVIDIA provides top-tier accelerated computing for AI tasks. Together, they democratize AI and accelerate processing, enabling teams to conduct more experiments, use advanced algorithms, and focus on producing high-quality projects with more reliable outcomes.

AI for All 

With Dataiku, AI becomes accessible to everyone. By enabling data experts and domain experts to collaborate in a unified environment, they can create and deploy solutions more effectively. Through Dataiku's no-code and code-based data science capabilities, along with powerful AutoML, practitioners of all levels can find the best models and seamlessly support code notebooks, IDEs, and CI/CD tools. Paired with NVIDIA’s accelerated computing and GPUs, Dataiku enables users to work together efficiently, focusing on quality while moving faster.

In addition, NVIDIA's AI Enterprise provides comprehensive frameworks with pre-trained models and development tools, ensuring data scientists have the resources they need when working on GenAI projects. This allows the focus to stay on generating insights and democratizing models for all, rather than maintaining the infrastructure.

Deliver More Solutions With Confidence 

Beyond collaboration, Dataiku empowers organizations by building trust and confidence in their AI projects. Dataiku’s visual data pipelines allow stakeholders to trace and understand data usage, building transparency and accountability. All actions are tracked and logged through Dataiku’s built-in governance, ensuring that significant projects undergo proper review and final sign-off before deployment to production.

 —> Read Now: How to Make Your AI Projects Successful: Insights From NVIDIA

Using NVIDIA’s GPUs with Dataiku allows for a greater number of projects to be developed due to their speed and reliability — but it doesn’t stop there. By committing to patch-critical CVEs (common vulnerabilities and exposures) while maintaining API compatibility up and down the tech stack, NVIDIA ensures that AI solutions developed within Dataiku are both secure and scalable.

Dataiku’s user-friendly, governed platform combined with NVIDIA’s optimized, secure AI infrastructure ensures reliable performance and reduces risks when moving from pilot to production.

Save Cost & Optimize Performance

IT leaders that are using GPUs know optimizing performance while managing cost can be a tricky balance. Data teams using Dataiku and NVIDIA accelerated computing benefit from sped-up AI workloads, enhancing efficiency and performance while also reducing energy consumption, footprint, and investment in the data center or cloud.

 —> Read Now: How to Do Image Classification and Object Detection in Dataiku  With NVIDIA

At a more granular level, the NVIDIA RAPIDS™ Accelerator for Apache Spark can accelerate data processing up to 5x and reduce operational costs by as much as 5x compared to CPU-only platforms. This allows Dataiku users to process data faster and more cost effectively.

AI tasks such as traditional ML, deep learning, and natural language processing (NLP) are done visually with Dataiku’s AutoML, and efficiently with NVIDIA GPUs.

At a macro level, this means:

  • Business teams can now build visual projects and pipelines that are faster and more efficient, leading to quicker insights and decision making.
  • IT operators do not have to manage complex AI workloads, leading to more consistent and reliable processes.
  • Executives can have confidence in their AI projects and applications without worrying about high costs.

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