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What is MLOPs?

Machine Learning Operations (MLOps) is a practice for collaboration and communication between data scientists and operations professionals to help manage production Machine Learning (ML) lifecycles. It involves the principles of DevOps in the ML lifecycle to streamline and automate the process from model development to deployment and monitoring. The intention of MLOps is to develop faster deployment and scaling of ML models in a structured and efficient manner.


                                         1 - Source: Microsoft

The Five Levels of MLOps Maturity

Microsoft assumes that there are five levels of MLOps maturity ranging from no MLOps to fully automated processes. This is just a short summary to help you to identify your current level. For more details checkout the link.

Level 0: No MLOps

  • Challenges: Managing the full machine learning model lifecycle is difficult. Teams operate in silos, and releases are painful.
  • Characteristics:
    • Training models manually.
    • No centralized tracking of model performance.
    • Manual builds, deployments, and testing.
    • Most systems exist as “black boxes.”

Level 1: DevOps but no MLOps

  • Challenges: While releases are less painful than at Level 0, organizations still rely on the Data Team for every new model.
  • Characteristics:
    • Automated builds and tests for application code.
    • Limited feedback on model performance in production.

Level 2: Automated Training

  • Characteristics:
    • Centralized tracking of model training performance.
    • Automated model training.
    • Easy model reproducibility.
    • Fully managed and traceable training environment.

Level 3: Automated Model Deployment

  • Characteristics:
    • Automated tests for all code.
    • Integrated A/B testing of model performance.
    • Full traceability from deployment back to original data.
    • Low-friction, automatic releases.

Level 4: Full MLOps Automated Operations

  • Characteristics:
    • Automated model training and testing.
    • Approaching a zero-downtime system.
    • Production systems provide insights for continuous improvement.
    • Fully automated and easily monitored system.

Why level up?

A higher MLOps maturity level has several advantages. It improves efficiency, reduces risk, enhances model quality, and provides a competitive edge. Let’s delve into the reasons why you should strive for higher maturity:

  • Efficiency and Productivity: As your organization progresses to higher levels, you will streamline processes, automate repetitive tasks, and reduce manual interventions. Consequently, automated model training, deployment, and monitoring lead to faster development cycles and quicker time-to-market for your products. See how Perplexity AI reduced time to market with the help of Azure AI Studio.
  • Risk Mitigation: Higher maturity levels ensure better governance and risk management. Traceability, version control, and automated testing minimize the chances of errors or unexpected behavior in production.
  • Scalability and Agility: Mature MLOps practices allow organizations to scale their machine learning initiatives. Automated processes enable teams to handle multiple models and projects simultaneously. See how Delivery Hero can run their models in 70+ countries, all with varying regulations with MLOps in Google Cloud.
  • Quality Assurance: Rigorous testing, A/B experiments, and continuous monitoring at higher maturity levels improve model quality. Organizations can confidently deploy models with minimal risk of performance degradation.
  • Cost Optimization: Automation reduces operational costs by minimizing manual effort. Efficient resource utilization and optimized infrastructure contribute to cost savings.
  • Competitive Edge: Organizations with mature MLOps practices can innovate faster, respond to market changes, and gain a competitive advantage. Reliable, high-quality models enhance customer satisfaction and trust.

How to level up?

After supporting various of our customers with their MLOps journey, we have realized that the most crucial aspect is that there is no `best` MLOps approach and that the optimal MLOps design varies from company to company.

If you want to improve your workflows and become more mature with MLOps start with identifying your key bottlenecks and how they impact your company's AI vision. 

After clarifying your individual needs, we highly recommend developing a concept based on your company’s requirements first and then choosing the proper tooling for the jobs, afterward. That also means that you might not need to go all in, it always depends on how heavily your business relies on model availability and validity.

This helps you to focus on the bigger picture without getting lost in details.

That also means that you might not need to go all in, it always depends on how heavily your business relies on Model availability and validity.



Do you have any questions, or could you use some help with your MLOps journey?
Contact us. We are happy to help you level up!

Get in touch!




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Laurenz Reitsam
Laurenz is a data scientist with a keen interest in DevOps and infrastructure as well as machine learning and data analytics. It is his firm belief that a model is only a good model if it succeeds in making its way into production.
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