Whitepaper 'FinOps and cost management for Kubernetes'

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How to optimize RI/SP usage for ML/AI teams with OptScale →

Ebook 'From FinOps to proven cloud cost management & optimization strategies'

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

ML/AI automation

Effortlessly monitor and schedule model training jobs, manage dependencies, and orchestrate ML workflows

ML Tasks, models, artifacts and datasets

Metrics tracking and visualization

Cost and performance tracking

OptScale integration with Airflow, Jenkins, and GitHub Actions in MLOps is intended to automate the end-to-end machine learning lifecycle.

With OptScale, users can orchestrate the ML workflow, schedule and manage model training jobs, ensuring that they run periodically or in response to specific triggers.

OptScale triggers the necessary jobs in the appropriate tools with the required parameters for various operations, such as training or retraining a model, deploying a model, generating a dataset, and more.

Supported platforms

News & Reports

MLOps open source platform

A full description of OptScale as an MLOps open source platform.

Enhance the ML process in your company with OptScale capabilities, including

ML/AI Leaderboards
Experiment tracking
Hyperparameter tuning
Dataset and model versioning
Cloud cost optimization

How to use OptScale to optimize RI/SP usage for ML/AI teams

Find out how to:

enhance RI/SP utilization by ML/AI teams with OptScale
see RI/SP coverage
get recommendations for optimal RI/SP usage

Why MLOps matters

Bridging the gap between Machine Learning and Operations, we’ll cover in this article:

The driving factors for MLOps
The overlapping issues between MLOps and DevOps
The unique challenges in MLOps compared to DevOps
The integral parts of an MLOps structure

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