How to Increase ML Server Utilization With MLOps Visualization Dashboards

Abstract: 

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 a 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

Bio: 

Yochay is an experienced tech leader and has been named in the 2020 Forbes 30 under 30 list for his achievements in AI advancement and for building cnvrg.io. Since the age of 7 Yochay has been writing code. He served in the Israeli Defence Force Intelligence unit for 4 years, and studied Computer Science at the Hebrew University of Jerusalem (HUJI) where he founded the HUJI Innovation Lab. Yochay has been consulting companies in AI and machine learning. After 3 years of consulting, Yochay, along with Co-founder Leah Kolben decided to create a tool to help data scientists and companies scale their AI and Machine Learning with cnvrg.io. The company continues to help data science teams from Fortune 500 companies manage, build and automate machine learning from research to production.