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White Paper—Optimize Machine Learning Through MLOps with Dell Technologies and cnvrg.io
Introduction Revision history The challenges of implementing machine learning cnvrg.io for MLOps
A platform for managing the MLOps life cycle
ML workflows with cnvrg.io
Key features
cnvrg.io architecture
Benefits
The Dell Validated Designs for AI Conclusion References

A platform for managing the MLOps life cycle

Home > AI Solutions > Artificial Intelligence > White Papers > White Paper—Optimize Machine Learning Through MLOps with Dell Technologies and cnvrg.io > A platform for managing the MLOps life cycle

A platform for managing the MLOps life cycle

  • Available either as self-hosted software or as the Metacloud SaaS offering, cnvrg.io delivers a full stack MLOps platform that helps simplify continuous training and deployment of AI and ML models. With cnvrg.io, organizations can automate end-to-end ML pipelines at scale in all environments, whether on premises or across clouds. cnvrg.io makes it easy to place training or inferencing workloads on CPUs, GPUs, TPUs, and other specialized accelerators depending on the wanted cost and performance trade-offs. Developers get a cloud-like self-service experience for choosing compute and storage resources from market leaders like Dell Technologies.

    Regardless of the components in the pipeline, the result is a single end-to-end flow designed to maximize workload performance, optimizing with the right hardware and processing elements beneath each stage in the flow. ML jobs can be launched on demand, regardless of the underlying compute and storage elements.

    Whether from the command line, SDK, or an intuitive visual interface, cnvrg.io provides access to all models, code, and datasets that can be run across an organization’s compute and storage resources. Utilization and efficiency are boosted as data scientists can aggregate and use the best components, optimized for the job and then orchestrate those flows, all from a unified graphical interface. Through native Kubernetes pod and cluster orchestration and cnvrg.io’s internal job scheduler, both cloud and on-premises resources can be easily scaled to meet the computational and storage needs of an organization’s ML workloads.

    All these capabilities help remove friction and latency from the data science process, getting models out of the lab and into production quicker and reducing time to value for of the data. By removing much of the underlying complexity from the model, data scientists can spend more time delivering insight and spend less time dealing with configuration and testing. With cnvrg.io, enterprises can apply MLOps for continuous training and deployment of ML in the way that DevOps principles enable CI/CD for traditional IT workloads.