Whitepaper 'FinOps and cost management for Kubernetes'
Please consider giving OptScale a Star on GitHub, it is 100% open source. It would increase its visibility to others and expedite product development. Thank you!
Webinar 'FinOps and cloud cost optimization for ML/AI workloads.' Register here →
Ebook 'From FinOps to proven cloud cost management & optimization strategies'
OptScale — FinOps
FinOps overview
Cost optimization:
MS Azure
Google Cloud
Alibaba Cloud
OptScale — MLOps
ML/AI Profiling
ML/AI Optimization
Big Data Profiling
Acura — Cloud migration
Database replatforming
Migration to:
MS Azure
Google Cloud
Alibaba Cloud
Public Cloud
Migration from:
Acura — DR & cloud backup
Migration to:
MS Azure
Google Cloud
Alibaba Cloud

MLOps maturity levels: the most well-known models


Like most IT processes, MLOps has maturity levels. They help companies understand where they are in the development process and what needs to be changed in their ML approaches to move to the next level (if that is their goal). Using commonly accepted maturity level methodologies also allows companies to determine their place among competitors.

Google Model

Google has its own model of MLOps maturity levels. It appeared as one of the first models, is concise, and consists of three levels:

Level 0: Manual process
Level 1: ML pipeline automation
Level 2: CI/CD pipeline automation

It is difficult to escape the thought that this model resembles instructions for drawing an owl. First, do everything manually, then build an ML pipeline, and then automate MLOps. However, it is well described.

Azure Model

Today, many large companies using ML have created their own maturity models. Azure also has a similar approach to identifying levels. However, they have more levels than Google:

Level 0: No MLOps
Level 1: DevOps but no MLOps
Level 2: Automated Training
Level 3: Automated Model Deployment
Level 4: Full MLOps Automated Operations

GigaOm Model

Also, one of the most detailed and understandable models is from the analytical firm GigaOm. Of all the models, it is the closest to Capability Maturity Model Integration (CMMI). This is a set of process improvement methodologies in organizations, which also consists of five levels from 0 to 4.


* image source:  https://research.gigaom.com/report/delivering-on-the-vision-of-mlops

In the GigaOm model, each maturity level is described through five categories: strategy, architecture, modeling, processes, and management.

By using this model in the early stages of ML system development and implementation, important aspects can be considered in advance and the chances of failure can be reduced. In fact, moving from one maturity level to a higher one presents new challenges to the team, which they may not have been aware of before.

Free cloud cost optimization & enhanced ML/AI resource management for a lifetime


All of the models outlined converge on one thing: at level 0, there is a lack of any ML practices, and at the highest level, there is the automation of MLOps operations. In the center, there is always something unique that is somehow related to the gradual automation of machine learning operations. For Azure, this is the automation of the training and deployment process for models.

💡 You might be also interested in our article ‘What are the main challenges of the MLOps process?’

Discover the challenges of the MLOps process, such as data, models, infrastructure, and people/processes, and explore potential solutions to overcome them → https://hystax.com/what-are-the-main-challenges-of-the-mlops-process

✔️ OptScale, a FinOps & MLOps open source platform, which helps companies optimize cloud costs and bring more cloud usage transparency, is fully available under Apache 2.0 on GitHub → https://github.com/hystax/optscale.

Enter your email to be notified about new and relevant content.

Thank you for joining us!

We hope you'll find it usefull

You can unsubscribe from these communications at any time. Privacy Policy

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 7th 
May and discover how OptScale allows running ML/AI or any type of workload with optimal performance and infrastructure cost.