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Iguazio Product Update: Optimize Your ML Workload Costs with AWS EC2 Spot Instances

Alexandra Quinn | August 30, 2022

Iguazio users can now run their ML workloads on AWS EC2 Spot instances. When running ML functions, you might want to control whether to run on Spot nodes or On-Demand compute instances.
When deploying Iguazio MLOps platform on AWS, running a job (e.g. model training) or deploying a serving function users are now able to choose to deploy it on AWS EC2 Spot compute instances.
Choosing a spot instance is a great cost saving choice if you can be flexible about when your applications run and if your applications can be interrupted from your ML perspective.

Configuring Spot nodes inside the Iguazio MLOps Platform

About Amazon EC2 Spot Instances:

“Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. You can use Spot Instances for various stateless, fault-tolerant, or flexible applications such as big data, containerized workloads, CI/CD, web servers, high-performance computing (HPC), and test & development workloads. Because Spot Instances are tightly integrated with AWS services such as Auto Scaling, EMR, ECS, CloudFormation, Data Pipeline and AWS Batch, you can choose how to launch and maintain your applications running on Spot Instances.

Moreover, you can easily combine Spot Instances with On-Demand, RIs and Savings Plans Instances to further optimize workload cost with performance. Due to the operating scale of AWS, Spot Instances can offer the scale and cost savings to run hyper-scale workloads. You also have the option to hibernate, stop or terminate your Spot Instances when EC2 reclaims the capacity back with two-minutes of notice. Only on AWS, you have easy access to unused compute capacity at such massive scale - all at up to a 90% discount.”