To develop and operate complex systems like these, you can apply DevOps
principles to ML systems (MLOps). 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. While there is no single standard for CDMM, most models proposed in the industry consist of five levels, with Level 1 being the lowest level of maturity and Level 5 being the highest.

Similar to Build & Deploy, maturity in this category will involve tools and automation. However, it is also important to constantly increase the test-coverage of the application to build up the confidence in speed with frequent releases. Usually test involves verifying expected functionality according to requirements in different ways but we also want to emphasize the importance of verifying the expected business value of released features. These teams use tools to improve software robustness, detect rare failure conditions before they affect users and automate complex big data systems. To maintain a consistent release train, the team must automate test suites that verify software quality and use parallel deployment environments for software versions.

Former Head of Development at one of europes largest online gaming company. Tobias is currently implementing Continuous Delivery projects at several customers. Andreas Rehn is an Enterprise Architect and a strong advocate for Continuous Delivery, DevOps, Agile and Lean methods in systems development. Some tools specifically handle the integration (CI) side, some manage development and deployment (CD), while others specialize in continuous testing or related functions. CI/CD relies on automation to speed the processes of development, deployment, and testing.

Each level represents a set of capabilities that an organization must have in order to achieve that level of maturity. Tobias Palmborg, Believes that Continuous Delivery describes the vision that scrum, XP and the agile manifesto once set out to be. Continuous Delivery is not just about automating the release pipeline but how to get your whole change flow, from grain to bread ,in a state of the art shape.

Expert practices will include zero touch continuous deployment to production where every commit can potentially make it all the way to production automatically. At beginner level, the monolithic structure of the system is addressed by splitting the system into modules. Modules give a better structure for development, build and deployment but are typically not individually releasable like components.

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. CI/CD introduces ongoing automation and continuous monitoring throughout the lifecycle of apps, from integration and testing phases to delivery and deployment. Another commonly used open source project is Tekton, part of the Continuous Delivery Foundation. Tekton provides the ability to describe delivery pipelines declaratively using Kubernetes concepts and execute them on-demand in containers. We see DevOps as a lifecycle with each phase flowing into the other to break down silos and inform key stakeholders along the way.

By organization type

A DevOps culture brings a sense of shared responsibility across teams, yields faster time to market and faster resolution times, and helps mitigate unplanned work. At this stage in the model, the participants might be in a DevOps team, or simply developers and IT operations collaborating on a joint project. The principles and methods of Continuous Delivery are rapidly gaining recognition as a successful strategy for true business agility.

After making any javascript or css changes, optimize the project using RequireJS Optimizer. Optimizer combines related scripts together into build layers and minifies them via UglifyJS (the default). It might be time to check in on how your teams are doing and identify areas for improvement. 40% of teams practice ChatOps for conversation driven development during remediation. If you just said “huh, what is ChatOps?” or “I think I’m doing ChatOps, maybe?” – check out a real life scenario and pro-tips. Culture is the foundation on which every successful team is built and is a core ingredient of a DevOps implementation.

• This is why we created the Continuous Delivery Maturity Model, to give structure and understanding to the implementation of Continuous Delivery and its core components.
• In looking at the three ways of DevOps – flow, amplify feedback, and continuous learning and experimentation – each phase flows into the other to break down silos and inform key stakeholders.
• As shown in the following diagram, only a small fraction of a real-world ML
  system is composed of the ML code.
• The design and architecture of your products and services will have an essential impact on your ability to adopt continuous delivery.
• It was created in light of recent trends and best practices in software development, such as cloud native and DevOps.

For example, continually monitored application performance KPIs enable an CD system to automatically roll back a release that exhibits problems in production. A maturity model describes milestones on the path of improvement for a particular type of process. In the IT world, the best known of these is the capability maturity model (CMM), a five-level evolutionary path of increasingly organized and systematically more mature software development processes. Continuous Delivery 3.0 Maturity Model (CD3M) is a framework for assessing an organization’s maturity in implementing continuous delivery practices, created by the Netherlands National Institute for the Software Industry (NISI). It was created in light of recent trends and best practices in software development, such as cloud native and DevOps. The CDMM can be used to identify areas for improvement and guide an organization’s efforts to implement continuous delivery practices.

DevOps Maturity Model

This setup is suitable when
you deploy new models based on new data, rather than based on new ML ideas. Resist the tendency to treat a maturity model as prescriptive directions instead of generalized guidelines — as a detailed map instead of a tour guidebook. Also, this continuous delivery maturity model shows a linear progression from regressive to fully automated; activities at multiple levels can and do happen simultaneously.

And I can also disseminate my learnings to the wider tech community and understand how the technologies are used in the real world. A DevOps engineer has a unique combination of skills and expertise that enables collaboration, innovation, and cultural shifts within an organization. For your security, if you’re on a public computer and have finished using your Red Hat services, please be sure to log out. Your Red Hat account gives you access to your member profile, preferences, and other services depending on your customer status. This section discusses the components that you need to add to the architecture
to enable ML continuous training.

DevOps teams need to learn more advanced techniques and tools while they master the basics. Therefore, start by defining a basic CD process and developing some simple scripts, but simultaneously research, learn and test more complicated processes and advanced tools. The next level in the continuous delivery maturity model entails defining the activities for the entire move-to-production process, along with the file and system locations plus tooling to automate it. The goal is to increase release cycles’ consistency, not their speed, although the intermediate stage is typically when organizations can stick to regular releases on a defined schedule, such as nightly or weekly. The entire CD process should be automated, launched with a single command.

Test automation tools include pipeline software like Jenkins; test automation systems like Selenium or Cypress; and cloud services, including AWS CodePipeline or Microsoft Azure DevTest Labs. In this category we want to show the importance of handling this information correctly when adopting Continuous Delivery. Information must e.g. be concise, relevant and continuous delivery maturity model accessible at the right time to the right persons in order to obtain the full speed and flexibility possible with Continuous Delivery. Apart from information directly used to fulfill business requirements by developing and releasing features, it is also important to have access to information needed to measure the process itself and continuously improve it.

The deployment process is manual or semi-manual with some parts scripted and rudimentarily documented in some way. 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.

DevOps, a concept that combines the practices of “development” and “operations,” is an approach to culture, automation, and platform design intended to deliver increased business value and responsiveness through rapid, high-quality service delivery. 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. In addition to offline model validation, a newly deployed model
undergoes online model validation—in a canary deployment or an A/B testing
setup—before it serves prediction for the online traffic.

What’s the difference between continuous delivery and continuous deployment?

Cloud services and CD automation simplify the task to create and manage redundant environments for production, beta and developer code. New releases nondisruptively roll into production after a suitable testing cycle with the help of parallel setups. Testing illustrates the inherent overlap between continuous integration and continuous delivery; consistency demands that software passes acceptance tests before it is promoted to production.

The best way to include the whole organization in the change is to establish a solid platform with some important prerequisites that will enable the organization to evolve in the right direction. This platform includes adopting specific tools, principles, methods and practices that we have organized into five key categories, Culture & Organization, Design & Architecture, Build & Deploy, Test & Versification and Information & Reporting. Structuring Continuous Delivery implementation into these categories that follows a natural maturity progression will give you a solid base for a fast transformation with sustainable results. Assuming that new implementations of the pipeline aren’t frequently deployed
and you are managing only a few pipelines, you usually manually test the
pipeline and its components. You also submit the tested source code for the pipeline to
the IT team to deploy to the target environment.

At this level real time graphs and other reports will typically also include trends over time. At intermediate level, builds are typically triggered from the source control system on each commit, tying a specific commit to a specific build. Tagging and versioning of builds is automated and the deployment process is standardized over all environments. Built artifacts or release packages are built only once and are designed to be able to be deployed in any environment. The standardized deployment process will also include a base for automated database deploys (migrations) of the bulk of database changes, and scripted runtime configuration changes. A basic delivery pipeline is in place covering all the stages from source control to production.

Automatic reporting and feedback on events is implemented and at this level it will also become natural to store historical reports connected to e.g. builds or other events. This gives management crucial information to make good decisions on how to adjust the process and optimize for e.g. flow and capacity. At this stage it might also become necessary to scale out the build to multiple machines for parallel processing and for specific target environments. Techniques for zero downtime deploys https://forexhero.info/ can be important to include in the automated process to gain better flexibility and to reduce risk and cost when releasing. At this level you might also explore techniques to automate the trailing part of more complex database changes and database migrations to completely avoid manual routines for database updates. The goal of level 1 is to perform continuous training of the model by
automating the ML pipeline; this lets you achieve continuous delivery of model
prediction service.

Optimizations reduce the cycle time for code releases; eliminate software errors and resulting rollbacks; and support more complex, parallel release pipelines for multiple, concurrent software versions, including A/B experimental releases. Advanced CD implementations have almost completely automated code’s journey from integration testing through various stages of test deployments onto production systems. So, if the entire CD process can launch with one command, why are there still two higher levels of CD maturity? Although testing is automated, many organizations are reluctant to cede control over the release to production, and, thus, might require a manual approval step before code gets promoted to the next stage of deployment. Continuous deployment, on the other hand, covers some additional steps through the release process of the new software. It usually includes the process of automatically releasing a developer’s changes from the repository to production, where it is usable by customers.

With DevOps, developers, usually coding in a standard development environment, work closely with testers and IT operations teams to speed software builds, code commits, unit tests, and releases—without sacrificing reliability. For a rapid and reliable update of the pipelines in production, you need a
robust automated CI/CD system. This automated CI/CD system lets your data
scientists rapidly explore new ideas around feature engineering, model
architecture, and hyperparameters. They can implement these ideas and
automatically build, test, and deploy the new pipeline components to the target
environment.

• In addition to providing me with a platform to share learning with a global community of software developers, InfoQ’s peer-to-peer review system has significantly improved my writing.
• Therefore, start by defining a basic CD process and developing some simple scripts, but simultaneously research, learn and test more complicated processes and advanced tools.
• To use IaC sample data, rename the file to data_radar.js; it will be automatically included in the build.
• However, it is also important to constantly increase the test-coverage of the application to build up the confidence in speed with frequent releases.

It is designed to guide organizations in their efforts to improve their software development process and ultimately achieve continuous delivery. Moving to expert level in this category typically includes improving the real time information service to provide dynamic self-service useful information and customized dashboards. As a result of this you can also start cross referencing and correlating reports and metrics across different organizational boundaries,.

With continuous integration, new code changes to an app are regularly built, tested, and merged into a shared repository. It’s a solution to the problem of having too many branches of an app in development at once that might conflict with each other. The following section discusses the typical steps for training and evaluating
an ML model to serve as a prediction service. The application is a browser-based tool, which uses the D3.js JavaScript library. The tools and technology your teams use can drive better automation and collaboration between teams.

It might seem strange to state that verifying expected business result is an expert practice but this is actually something that is very rarely done as a natural part of the development and release process today. Verifying expected business value of changes becomes more natural when the organization, culture and tooling has reached a certain maturity level and feedback of relevant business metrics is fast and accessible. As an example the implementation of a new feature must also include a way to verify the expected business result by making sure the relevant metrics can be pulled or pushed from the application. The definition of done must also be extended from release to sometime later when business has analyzed the effects of the released feature or change.. At this level the work with modularization will evolve into identifying and breaking out modules into components that are self-contained and separately deployed.

These composite applications also include more sophisticated components, notably databases, that are complicated to deploy and test. 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 (KPIs) for better control over software acceptance and rollback criteria in test and in live production.