Company offers NVIDIA GPU performance-optimized NGC containers on premises or on any cloud with MLOps, automation and monitoring capabilities, accelerating enterprise AI from research to production

cnvrg.io, the data science platform simplifying model management with MLOps and continual machine learning automation, announces its advanced MLOps solution will be integrated with the NVIDIA NGC container registry. Through a full, native integration, cnvrg.io will deliver accelerated enterprise artificial intelligence (AI), machine learning (ML) and data science automated pipelines to enterprise teams in multi-cloud and hybrid-cloud environments.

cnvrg.io’s integration with NGC containers significantly accelerates time from research to production. Development teams can seamlessly launch a GPU-optimized NGC container in one click across any on-premises or hybrid-cloud compute. cnvrg.io simplifies model management and solves reproducibility with end-to-end traceability, allowing enterprises to get the latest, most optimized NGC containers, as well as the dataset the model was trained on.

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The NGC integration into cnvrg.io is a breakthrough in MLOps solutions, automating DevOps, minimizing complexity, saving time, and giving data science teams a unified hub to standardize their ML workflows. cnvrg.io provides advanced resource management and meta-scheduling capabilities, enabling automation of MLOps tasks, while providing IT teams with pipeline performance monitoring and metrics.

Taking advantage of NGC, cnvrg.io offers a unified solution to accelerate enterprise AI through MLOps, automation and NVIDIA GPU performance-optimized containers. The combined enterprise solution accelerates ML pipelines, empowering data science teams to:

• Launch NGC containers with one click – easily launch any ML or DL framework on premises or in the cloud with ready-to-run NGC containers
• Enhance model management – track end-to-end ML pipelines including code, model, version, metadata and associated training data with containers
• Enable model reproducibility – reproduce results and improve governance with traceable ML pipelines
• Extend resources – gain advanced resource management and meta-scheduling system built on containers with support for Kubernetes, multi-cloud and hybrid cloud
• Employ cutting-edge MLOps – simplify engineering tasks with automation, instantly deploy jobs and provide IT with live monitoring and metrics

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cnvrg.io users will benefit from a fully managed registry of ready-to-run containers optimized by NVIDIA engineering teams, granting them instant access to NVIDIA NGC containers of deep learning and machine learning frameworks that are GPU-optimized for better performance and speed.

“At cnvrg.io we are constantly working to give data scientists and data engineers the best possible resources to do what they do best: data science.” said Yochay Ettun, CEO and co-founder of cnvrg.io. “We see great customer traction and now with the NGC container integration we have another enhancement for our MLOps solution. Any AI/ML use case has the flexibility to run on any compute resource whether it’s cloud or on premises.”

“NVIDIA GPUs with NGC containers accelerate and simplify AI deployments in the enterprise. cnvrg.io MLOps offers the tools needed to build, run, deploy and monitor workflows. With one-click deployment for NGC containers, we’re accelerating the work of data scientists,” said Adel El Hallak director of product management for NGC at NVIDIA.

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