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Ebook 'De FinOps a estratégias comprovadas de gerenciamento e otimização de custos de nuvem'
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OptScale — FinOps
Visão geral do FinOps
Otimização de custos:
AWS
Microsoft Azure
Google Cloud
Nuvem Alibaba
Kubernetes
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OptScale — MLOps
Perfil de ML/IA
Otimização de ML/IA
Criação de perfil de Big Data
PREÇOS OPTSCALE
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Acura – migração para nuvem
Visão geral
Nova plataforma de banco de dados
Migração para:
AWS
Microsoft Azure
Google Cloud
Nuvem Alibaba
VMware
Pilha aberta
KVM
Nuvem pública
Migração de:
Na premissa
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Acura – DR e backup na nuvem
Visão geral
Migração para:
AWS
Microsoft Azure
Google Cloud
Nuvem Alibaba
VMware
Pilha aberta
KVM

Otimização de custos de ML

Aprimore o processo de criação de perfil de ML/IA obtendo desempenho ideal e custos mínimos de nuvem para experimentos de ML/IA
ML cost optimization-OptScale

Otimização RI/SP

RI-SP optimization OptScale

The RI/SP usage dashboard allows OptScale users to forecast guaranteed usage, utilize recommendations for better RI/SP utilization, and save a double-digit percentage of monthly cloud spend.

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 Savings Plans, which help minimize cloud costs for ML/AI experiments and development.

Strategically combining Savings Plans, Reserved Instances, and Spot Instances enables organizations to strike a balance between cost efficiency and flexibility, maximizing the value of machine learning processes.

Unused resource and bottleneck identification

unused resource and bottleneck identification OptScale

By integrated profiling, OptScale highlights bottlenecks of every experiment run and offers clear optimization recommendations to reach performance enhancement. The recommendations include utilizing Reserved/Spot Instances and Savings Plans, rightsizing and instance family migration, and detecting CPU/IO and IOPS inconsistencies caused by data transformations or model code inefficiencies.

Unused and overlooked resources are contributed to a company cloud bill, and users don’t even expect that they’re paying for them.

OptScale allows ML specialists to identify and clean up orphaned snapshots to keep cloud costs under control.

Power Schedules

power schedules OptScale

OptScale’s Power Schedules feature allows the scheduled shutdown of instances; users can automatically start and stop VMs to avoid the risk of human error and the burden of manual management.

With a single solution that works across multiple cloud platforms, customers have a consistent and streamlined experience, no matter where their resources are hosted.

Object storage optimization: S3 duplicate object finder

object-storage-optimization-OptScale

The Duplicate Object Finder for AWS S3 delivers significant cloud cost reduction by illuminating the duplicated objects.

The labyrinth of AWS S3 often houses duplicate objects, a silent menace that, over time, bloats cloud expenditures. OptScale scans and fishes out duplicates from a great number of S3 objects. Dispelling the constraints of singular account connectivity, OptScale’s Duplicate S3 Object Finder empowers users to link unlimited AWS cloud accounts.

VM Rightsizing: Optimal instance type and family selection

Choosing the optimal instance type and family involves selecting VMs from a cloud provider’s offerings that best meet the performance and cost requirements of your ML workloads. 

OptScale enables balancing performance needs with cost considerations by selecting instances that provide the required resources at the lowest cost.

By continuously monitoring, analyzing, and adjusting VM configurations based on workload requirements, OptScale enhances the performance of ML workflows while minimizing costs.

VM-rightsizing-OptScale

Gerenciamento de custos do Databricks

databricks cost management OptScale

With OptScale, users Improve visibility and control over Databricks expenses and get details on which experiments the costs are distributed

Supporting Databricks by the OptScale platform allows ML specialists to identify how the Databricks costs are distributed across ML experiments and tasks. The connected Databricks data source is managed in the same way as other data sources.

OptScale captures metadata from Databricks resources, such as name, tags, and region, enabling effective cost allocation.

Instrumentação S3 e Redshift

With OptScale, users get the complete picture of S3 and Redshift API calls, usage, and cost for their ML model training or data engineering experiments. The platform provides users with metrics tracking and visualization, as well as performance and cost optimization recommendations.

S3 Redshift instrumentation OptScale

Plataformas suportadas

aws
MS Azure
google cloud platform
Alibaba Cloud
Kubernetes
databricks
PyTorch
kubeflow
TensorFlow
spark-apache

Novidades e Relatórios

Plataforma de código aberto MLOps

Uma descrição completa do OptScale como uma plataforma de código aberto MLOps.

Melhore o processo de ML na sua empresa com Recursos de OptScale, incluindo

  • Placares de ML/IA
  • Rastreamento de experimentos
  • Ajuste de hiperparâmetros
  • Controle de versão de conjunto de dados e modelo
  • Otimização de custo de nuvem

Como usar o OptScale para otimizar o uso de RI/SP para equipes de ML/IA

Descubra como: 

  • aprimore a utilização de RI/SP por equipes de ML/IA com OptScale
  • veja cobertura RI/SP
  • obtenha recomendações para uso ideal de RI/SP

Por que o MLOps é importante

Abordamos neste artigo como preencher a lacuna entre aprendizado de máquina e operações:

  • Os fatores determinantes para MLOps
  • As questões sobrepostas entre MLOps e DevOps
  • Os desafios únicos em MLOps em comparação com DevOps
  • As partes integrantes de uma estrutura MLOps