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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

An open source FinOps solution with ML/AI profiling and optimization capabilities

Enhance ML/AI profiling process by getting optimal performance and minimal cloud costs for ML/AI experiments
OptScale ML-AI Optimization
Hystax-OptScale-ML-task-profiling-optimization

ML/AI task profiling and optimization

OptScale performance improvement recommendations

Dozens of tangible ML/AI performance improvement recommendations

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

Runsets to simulate ML/AI  model training 

Optscale minimal cloud cost

Minimal cloud cost for ML/AI experiments and development

ML/AI task profiling and optimization

With OptScale ML/AI and data engineering teams get an instrument for tracking and profiling ML/AI model training and other relevant tasks. OptScale collects a holistic set of both inside and outside performance indicators and model-specific metrics, which assist in providing performance enhancement and cost optimization recommendations for ML/AI experiments or production tasks.

OptScale integration with Apache Spark makes Spark ML/AI task profiling process more efficient and transparent.

Hystax OptScale ML-AI profiling and optimization
OptScale-tangible-performance-improvement-recommendations

Dozens of tangible performance improvement recommendations

By integrating with an ML/AI model training process OptScale highlights bottlenecks and offers clear recommendations to reach ML/AI performance optimization. The recommendations include utilizing Reserved/Spot instances and Saving Plans, rightsizing and instance family migration, Spark executors’ idle state, and detecting CPU/IO, and IOPS inconsistencies that can be caused by data transformations or model code inefficiencies.

Runsets to simulate ML/AI model training on different environments and hyperparameters

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

OptScale-runsets_ML_model_training_simulation_on_different_environment_hyperparameters
OptScale-minimal-cloud-cost-for-ML-experiments-and-development

Minimal cloud cost for ML/AI experiments and development

After profiling ML/AI model training, OptScale provides dozens of real-life optimization recommendations and an in-depth cost analysis, which help minimize cloud costs for ML/AI experiments and development. The tool delivers ML/AI metrics and KPI tracking, providing complete transparency across ML/AI teams.

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.