Главная
Study mode:
on
1
Intro
2
From notebooks to training jobs
3
Experiment dashboard
4
Cluster Dashboard
5
Multi-Cloud ML Infrastructure
6
Build hybrid cluster with Kubernetes & Terraform
7
Terraforming AWS Infrastructure
8
Test AWS Infrastructure
9
Terraforming GCP Infrastructure
10
Cloud Troubleshooting
11
Dataset Mounting
12
Cluster Management & Monitoring
13
Common Interface
14
Fractional GPUs
15
multicluster-scheduler
16
reCap: Step-by-Step Guide
Description:
Discover how to significantly reduce machine learning computing costs in this 38-minute conference talk from MLOps World. Learn from Jaeman An, Founder & CEO of VESSL AI, as he shares insights on building time- and cost-effective ML infrastructure. Explore hybrid cloud architectures using Terraform and Kubernetes, cost optimization techniques with spot instances and fractional GPUs, and solutions to common multi-environment challenges. Gain practical knowledge on dataset mounting, network performance optimization, and server monitoring. Follow a step-by-step guide to implement these strategies and potentially achieve over 80% cost savings on ML projects.

How to Reduce ML Computing Costs: Building Efficient Multi-Cloud Infrastructure

MLOps World: Machine Learning in Production
Add to list