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Learn how to increase your ML server utilization with visibility dashboards and data-driven infrastructure management. In this webinar, we will discuss key solutions for fragmented MLOps processes, and how to reduce technical and computational debt. Companies invest millions of dollars on compute that has the potential to dramatically accelerate AI workloads and improve performance, but end up only utilizing a small fraction of it, sometimes as low as 20% of these powerful resources.

In this talk, we will introduce a way to streamline your MLOps process, monitor all GPU, CPU, and memory resources, and maintain high utilization of your resources. You will learn key strategies to increase utilization for your ML/DL infrastructure with MLOps and resource management best practices. We will discuss the benefits of hybrid cloud infrastructure, and how to maximize utilization with MLOps visibility dashboards. 

What you’ll learn: 

  • How to increase utilization by up to 80% with infrastructure visibility
  • How to monitor utilization, capacity, and allocation of ML servers across all runs
  • MLOps strategies to reduce computational debt in your infrastructure
  • Benefits and strategies for managing a Hybrid Cloud environment
  • How to use data-driven ML infrastructure and capacity planning

Learn how to increase your ML server utilization with visibility dashboards and data-driven infrastructure management. In this webinar, we will discuss key solutions for fragmented MLOps processes, and how to reduce technical and computational debt. Companies invest millions of dollars on compute that has the potential to dramatically accelerate AI workloads and improve performance, but end up only utilizing a small fraction of it, sometimes as low as 20% of these powerful resources.

In this talk, we will introduce a way to streamline your MLOps process, monitor all GPU, CPU, and memory resources, and maintain high utilization of your resources. You will learn key strategies to increase utilization for your ML/DL infrastructure with MLOps and resource management best practices. We will discuss the benefits of hybrid cloud infrastructure, and how to maximize utilization with MLOps visibility dashboards. 

 

What you’ll learn: 

  • How to increase utilization by up to 80% with infrastructure visibility
  • How to monitor utilization, capacity, and allocation of ML servers across all runs
  • MLOps strategies to reduce computational debt in your infrastructure
  • Benefits and strategies for managing a Hybrid Cloud environment
  • How to use data-driven ML infrastructure and capacity planning
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With cnvrg.io we were able to increase our model throughput by up to 50% and on average by 30% when comparing to RESTful APIs. cnvrg.io also allows us to monitor our models in production, set alerts and retrain with high-level automation ML pipelines
Avi Gabay
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’.
Alexander Ryabov
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.
Bruce King
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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