Amsterdam, November 22nd, 2023 — UbiOps today announced certification on NVIDIA AI Enterprise software, which powers the NVIDIA AI platform. This certification will enable organizations to use the software’s included AI frameworks, libraries, and SDKs for accelerating data science and streamlining development and deployment of production-ready generative AI, computer vision, speech AI, and more on the UbiOps AI Infrastructure platform.
Users can leverage NVIDIA AI Enterprise for AI and ML workloads running on UbiOps. UbiOps capabilities for production-grade model training, serving, and data processing in combination with powerful NVIDIA AI Enterprise software will help businesses develop and run the next generation of AI systems.
‘’UbiOps’ team is thrilled to earn certification of NVIDIA AI Enterprise ” said Bart Schneider, CCO at UbiOps. ”By enabling customers to deploy NVIDIA AI software and tools, the UbiOps platform opens up an exciting era of innovation, where we combine our strengths to deliver a game-changing MLOps solution to the market.”
UbiOps now supports using NVIDIA AI Enterprise certified software container environments as a foundation for workloads running in UbiOps.
Model training:
UbiOps’ certification of NVIDIA AI Enterprise introduces organizations to a software stack for model training. Users can leverage AI frameworks like TensorFlow to train and improve machine learning models efficiently and cost-effectively. This certification ensures that organizations have the tools they need to get the most from their training processes, accelerating time-to-insight and time-to-deployment.
Powerful AI inference, also for GenAI & LLMs:
The UbiOps platform with the certification of NVIDIA AI Enterprise software will offer customers a powerful inference solution for all types of data science and AI models. This includes easy access to NVIDIA’s inferencing software, like Triton Inference Server. Organizations can also tap into NVIDIA’s cutting-edge tools and SDKs and use the UbiOps platform to deploy their inference workloads in local, cloud, or hybrid environments. This means organizations can effortlessly deploy and execute AI and ML workloads with the power of NVIDIA hardware and tooling.
The UbiOps ‘Pipeline’ feature grants users the ability to create modular AI applications by chaining together deployments, each with their own customizable computing configurations. This makes it possible for organizations to achieve faster, more efficient, more cost-effective operations and better outcomes for their AI and ML initiatives.
Optimized End-to-End Workflows:
Maximizing performance and meeting production latency requirements requires end-to-end optimization. With NVIDIA AI Enterprise certification, organizations can use NVIDIA RAPIDS to accelerate every stage of their data science workflows, including feature engineering and model training.
“Becoming certified on NVIDIA AI Enterprise is a big step on our mission to provide data science and AI teams with the best infrastructure and capabilities for running and managing their AI workloads to build production ready AI services. Letting our users leverage the certified NVIDIA software stack and containers provides them with the right tools and technologies to build and run cutting-edge AI applications.” – Victor Pereboom, CTO, UbiOps
UbiOps will keep collaborating with NVIDIA to offer users the latest NVIDIA software and technologies for building AI applications.
About UbiOps
UbiOps is a powerful AI infrastructure platform designed to help teams deploy and serve the next generation of AI systems and products.
With UbiOps you can deploy all their Machine Learning models, as well as off–the–shelf AI models like LLMs, as scalable inference APIs. Built with scalability, security and robustness in mind, UbiOps serves as a foundation for many AI applications across different industries.
From revolutionary computer vision systems in health care, to IoT applications in the energy sector.
The unique UbiOps hybrid and multi-cloud orchestration engine helps you deploy, serve and manage your AI workloads across multiple clouds and on-premise environments.
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