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OptScale — FinOps
FinOps overview
Cost optimization:
AWS
MS Azure
Google Cloud
Alibaba Cloud
Kubernetes
OptScale — MLOps
ML/AI Profiling
ML/AI Optimization
Big Data Profiling
OPTSCALE PRICING
Acura — Cloud migration
Overview
Database replatforming
Migration to:
AWS
MS Azure
Google Cloud
Alibaba Cloud
VMWare
OpenStack
KVM
Public Cloud
Migration from:
On-premise
Acura — DR & cloud backup
Overview
Migration to:
AWS
MS Azure
Google Cloud
Alibaba Cloud
VMWare
OpenStack
KVM

In-depth analysis of performance metrics for ML model training profiling

Improve the algorithm to maximize ML/AI training resource utilization and outcome of experiments
ML-AI performance profiling
ML-model-training-tracking-and-profiling-OptScale

ML/AI model training tracking & profiling, internal/external performance metrics

ML-AI-optimization-recommendations-OptScale

Granular ML/AI optimization recommendations

Hystax-OptScale-runsets-ML-model-training-simulation

Runsets to identify the most efficient ML/AI model training results 

Spark integration

Spark integration

ML/AI model training tracking and profiling, internal and external performance metrics collection

OptScale profiles machine learning models and analyzes internal and external metrics deeply to identify training issues and bottlenecks.

ML/AI model training is a complex process that depends on a defined hyperparameter set, hardware, or cloud resource usage. OptScale improves ML/AI profiling process by getting optimal performance and helps reach the best outcome of ML/AI experiments.

OptScale-performance-profiling-inside-outside-metrics-analysis
granular ML/AI optimization recommendations

Granular ML/AI optimization recommendations

OptScale provides full transparency across the whole ML/AI model training and teams process and captures ML/AI metrics and KPI tracking, which help identify complex issues in ML/AI training jobs.

To improve the performance OptScale users get tangible recommendations such as utilizing Reserved/Spot instances and Saving Plans, rightsizing and instance family migration, detecting CPU/IO, IOPS inconsistencies that can be caused by data transformations, practical usage of cross-regional traffic, avoiding Spark executors’ idle state, running comparison based on the segment duration.

Runsets to identify the most efficient ML/AI model training results with a defined hyperparameter set and budget

OptScale enables ML/AI engineers to run many training jobs based on a pre-defined budget, different hyperparameters, and hardware (leveraging Reserved/Spot instances) to reveal the best and most efficient outcome for your ML/AI model training.

runsets to identify efficient ML-AI model training results
Spark-integration-with-OptScale

Spark integration

OptScale supports Spark to make Spark ML/AI task profiling process more efficient and transparent. A set of OptScale recommendations, delivered to users after profiling ML/AI models, includes avoiding Spark executors’ idle state.

Supported platforms

aws
google cloud platform
Alibaba Cloud Logo
Kubernetes
kubeflow
TensorFlow
spark-apache

News & Reports

FinOps and MLOps

A full description of OptScale as a FinOps and MLOps open source platform to optimize cloud workload performance and infrastructure cost. Cloud cost optimization, VM rightsizing, PaaS instrumentation, S3 duplicate finder, RI/SP usage, anomaly detection, + AI developer tools for optimal cloud utilization.

FinOps, cloud cost optimization and security

Discover our best practices: 

  • How to release Elastic IPs on Amazon EC2
  • Detect incorrectly stopped MS Azure VMs
  • Reduce your AWS bill by eliminating orphaned and unused disk snapshots
  • And much more deep insights

FinOps and cloud cost optimization for ML/AI workloads

Join our live demo on 27th 
March and discover how OptScale allows running ML/AI or any type of workload with optimal performance and infrastructure cost.