For example, you have a function that accepts a categorical data column and you encode the function as aone-hot feature. The start and end date, time, and how long the pipeline took to complete each of the steps. Producing evaluation metric values using the trained model on a test dataset to assess the model’s predictive quality. CT is a new property, unique to ML systems, that’s concerned with automatically retraining and serving the models. All Storage Products Cloud-based storage services for your business. Apigee Healthcare APIx FHIR API-based digital service production.

If you correlate test coverage with change traceability you can start practicing risk based testing for better value of manual exploratory testing. At the advanced level some organizations might also start looking at automating performance tests and security scans. The design and architecture of your products and services will have an essential impact on your ability to adopt continuous delivery. If a system is built with continuous delivery principles and a rapid release mind set from the beginning, the journey will be much smoother. However, an upfront complete redesign of the entire system is not an attractive option for most organizations, which is why we have included this category in the maturity model.

– Scrum of Scrums yields Build of Builds

To that end, the purpose of continuous delivery is to ensure that it takes minimal effort to deploy new code. Another characteristic of advanced continuous delivery maturity is the use of quantitative measures of software performance and quality, along with metrics that track the health and consistency of the CD process. Identify and monitor key performance indicators for better control over software acceptance and rollback criteria in test and in live production. For example, continually monitored application performance KPIs enable an CD system to automatically roll back a release that exhibits problems in production. Advanced practices include fully automatic acceptance tests and maybe also generating structured acceptance criteria directly from requirements with e.g. specification by example and domains specific languages.

continuous delivery maturity model

These are questions that inevitably will come up when you start looking at implementing Continuous Delivery. The principles and methods of Continuous Delivery are rapidly gaining recognition as a successful strategy for true business agility. ” How do you start with Continuous Delivery, and how do you transform your organization to ensure sustainable results. This Maturity Model aims to give structure and understanding to some of the key aspects you need to consider when adopting Continuous Delivery in your organization. Continuous Delivery Maturity Models provide frameworks for assessing your progress towards adopting and implementing continuous integration, delivery and deployment (CI/CD).


For online prediction, the prediction service can fetch in a batch of the feature values related to the requested entity, such as customer demographic features, product features, and current session aggregation features. For continuous training, the automated ML training pipeline can fetch a batch of the up-to-date feature values of the dataset that are used for the training task. Making sure that you test your model for deployment, including infrastructure compatibility and consistency with the prediction service API.

continuous delivery maturity model

The model also defines five categories that represent the key aspects to consider when implementing Continuous Delivery. Each category has it’s own maturity progression but typically an organization will gradually mature over several categories rather than just one or two since they are connected and will affect each other to a certain extent. Leslie Miley discusses how the road to ubiquitous AI is clouded by the dangers of the inherent bias in Large Language Models and the increased CO2 emissions that come with deployment at scale. Database migration and rollback is automated and tested for each deploy.

Continuous Delivery: Beyond the Team Build

This continuous delivery model allows the business to receive a return on investment as soon as possible and also reduce risky and repetitive tasks. The pros and cons of the continuous delivery maturity model will help the company decide whether its implementation is the right step at this time. For a rapid and reliable update of the pipelines in production, you need a robust automated CI/CD system.

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Maturing the Continuous Delivery Pipeline

With extremely short cycle time and a mature delivery pipeline, such organizations have the confidence to adopt a strict roll-forward only strategy to production failures. At the intermediate level you will achieve more extended team collaboration when e.g. DBA, CM and Operations are beginning to be a part of the team or at least frequently consulted by the team. Multiple processes are consolidated and all changes, bugs, new features, emergency fixes, etc, follow the same path to production. Decisions are decentralized to the team and component ownership is defined which gives teams the ability to build in quality and to plan for sustainable product and process improvements. To address the challenges of this manual process, MLOps practices for CI/CD and CT are helpful.

This requires the pipeline to be arranged so that maximum efficiency is achieved for delivery. The purpose is to identify what is not needed in the delivery pipeline and remove it. 5s implementation involves working through each phase in a methodical way.

– This is the key to CI

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continuous delivery maturity model

Dev and ops teams use a common set of tools but don’t have visibility into each others’ work. For more reference architectures, diagrams, and best practices, explore theCloud Architecture Center. Validating the data either for retraining or batch prediction. Testing that your model training doesn’t produceNaN values due to dividing by zero or manipulating small or large values. The following diagram shows the implementation of the ML pipeline using CI/CD, which has the characteristics of the automated ML pipelines setup plus the automated CI/CD routines.

platforms / configurations

CI is no longer only about testing and validating code and components, but also testing and validating data, data schemas, and models. An ML system is a software system, so similar practices apply to help guarantee that you can reliably build and operate ML systems at scale. To develop and operate complex systems like these, you can apply DevOps principles to ML systems . This document covers concepts to consider when setting up an MLOps environment for your data science practices, such as CI, CD, and CT in ML.

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