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On Demand Webinar: Machine Learning Model Deployment for the Enterprise

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Learn best practices of machine learning model deployment for the enterprise

Deployment is a major challenge facing enterprise success in AI. In our last webinar, we discussed why many machine learning models don’t make it into production. On premise solutions face specific difficulties that we will discuss in this webinar. We will discuss best practices of enterprise machine learning, and how to get more of your models to production. While there are many solutions that help streamline the ML deployment process for cloud enterprises, few solutions exist for on premise enterprises. Join Aaron Schneider as he discusses ways to quickly deploy on your own servers or hardware at scale or on any cloud service. 


Key takeaways:

  • How to leverage secured Kubernetes for machine learning deployment
  • Quickly provision resources for your ML train and deployment
  • How to set up container-based architecture for your machine learning
  • Prioritize on premise compute and data center and enable cloud bursting

Learn best practices of machine learning model deployment for the enterprise

Deployment is a major challenge facing enterprise success in AI. In our last webinar, we discussed why many machine learning models don’t make it into production. On premise solutions face specific difficulties that we will discuss in this webinar. We will discuss best practices of enterprise machine learning, and how to get more of your models to production. While there are many solutions that help streamline the ML deployment process for cloud enterprises, few solutions exist for on premise enterprises. Join Aaron Schneider as he discusses ways to quickly deploy on your own servers or hardware at scale or on any cloud service. 


Key takeaways:

  • How to leverage secured Kubernetes for machine learning deployment
  • Quickly provision resources for your ML train and deployment
  • How to set up container-based architecture for your machine learning
  • Prioritize on premise compute and data center and enable cloud bursting
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Avi GabayDirector of Architecture at Playtika
As with many data science professionals, our team is hard to please. They want the flexibility to use any language, and the ability to write their own custom packages to improve performance, set configurations, etc. With cnvrg.io, this is the first time I’ve heard our data scientists and analysts say ‘when can we have it’.
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Alexander RyabovHead of Data Services & Business Intelligence at Wargaming.net
Working in a hybrid cloud environment has major advantages but can be increasingly complex to manage, especially for AI workloads. cnvrg.io has the potential to enable us to operate in a hybrid cloud environment seamlessly. The new ML infrastructure dashboard could fill a major need in connecting our infrastructure to ML projects. It provides visibility into our on-prem GPU clusters and cloud resources, paving the way for increasing the utilization and ROI of our GPUs.
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Bruce KingData Science Technologist at Seagate Technology Advanced Analytics Group
Lightricks is a research-driven company, which is why we’ve built a team with some of the top computer vision researchers in the country. cnvrg.io ensures our highly qualified researchers are focused on building the industry-leading AI technology that we are now world renown for, instead of spending time on engineering, configuration and DevOps.
Ofir Bibi
Ofir BibiHead of Research at Lightricks
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