Model deployment is only concerned with the prediction service (typically a REST API), not the entire MLOps system, and there is no active production monitoring. Automating your processes through CI/CD pipelines and large scale testing are all practices that thrive within the DevOps maturity framework. So a great area to focus on when assessing your maturity level is understanding whether automation is sufficiently used or if manual work is still a big part of your daily processes. Automating repetitive tasks will result in more free time for team members to focus on finding innovative solutions and more reliable software. In the fourth stage, your DevOps model has reached maturity and the teams are working according to this framework’s principles and best practices. An organization that has reached this level of maturity can now start to measure the success of their implemented model through specific metrics and use the collected data to improve their processes.
- Different architecture styles can support these goals, such as microservices, service-oriented or event-driven architectures.
- We recommend that organisations at an intermediate level of Agile maturity start to adopt a broad view of ‘quality’ to include both the software output and the quality and security of the delivery process itself.
- This manual effort leaves room for error and wastes the valuable time of data science teams.
- The DevOps Maturity Assessment Framework is used to evaluate an organization’s DevOps practices and identify areas for improvement.
- 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.
- Once the DevOps model has attained this maturity level, this will have an impact on the entire deployment and monitoring processes.
I’ve often heard objections to continuous deployment on the grounds that “we don’t have the right culture for it”. The assumption seems to be that you need to establish the right culture before you can start. In a nutshell, a maturity model helps you evaluate your current performance in a specific area, and suggests what capabilities you should pick up next to improve. The reduction in downtime and an overall improved product lead to happy end-users. Improved customer satisfaction is a useful indicator of DevOps maturity and a great way to connect the importance of DevOps implementation back to business results.
Why Is Continuous Delivery Maturity Model Needed?
The continuous delivery maturity model lays out the five increasingly intense — and capable — levels of the process. At the advanced level you will have split the entire system into self contained components and adopted a strict api-based restaurant app builder approach to inter-communication so that each component can be deployed and released individually. The first maturity (Level 0) can be described as applying each step in the pipeline, from data analysis to serving the model, manually.
But businesses that make the investment in time, resources, and tooling see concrete advantages. DevOps organizations that adopt CD have been found to ship code faster and outperform other companies by 4-5x. But since this is not always practical, I’ve sorted the dimensions from top to bottom in the order that you should probably start. For example, you’ll benefit immediately from improved test automation and observability — but increased deployment automation will have limited impact if you don’t have the other two. If you break down the process into a series of incremental steps, it’s easier to make progress, and you’ll start to see the benefit of these changes long before you reach the end goal.
Go lean with Agile & Git
However, most frameworks lead users through a similar progression—let’s look at the most common stages of DevOps maturity. The DevOps Maturity Assessment Framework is used to evaluate an organization’s DevOps practices and identify areas for improvement. It provides a structured way to assess the organization’s current state and to determine what changes need to be made to achieve desired outcomes.
QCon San Francisco brings together the world’s most innovative senior software engineers across multiple domains to share their real-world implementation of emerging trends and practices. I like the idea a lot and would like to use that model for us to evaluate our own maturity. The organization and it’s culture are probably the most important aspects to consider when aiming to create a sustainable Continuous Delivery environment that takes advantage of all the resulting effects. Software frameworks greatly amplify a team’s productivity, but also make implicit decisions. The benefits and limitations must be understood because of the impact on the resulting system architecture.
A continuous deployment pipeline model
Although this is the highest level of maturity, your organization should work constantly towards continuously improving the implementation of this model to maintain the results and optimize performance. NISI has recently released the Continuous Delivery 3.0 maturity model, or CD3M. The Maturity Model guides the improvements of Continuous Delivery pipelines and/or software development processes in software organizations.
Our experience shows that typically you can expect to increase deployments per day (per pipeline) by 15% through a better understanding of the root-cause of Build Failures and of Deployment Cycle Time. As per Figure 2 below, the Cycle Time metric view allows teams to understand time spent in each ticket status within the development cycle. Analytics tools that offer filtering enable analysis by Status, Issue Type, or Epic (and any other standard or custom ticket field) all plotted over any time range required. Schedule a demo today to understand how we can help you on your path of implementing and optimizing your DevOps maturity model. Collaboration is an important pillar of the DevOps model and a key team performance indicator.
It’s never too late or early to start something
The process of conducting a thorough assessment may seem overwhelming at first, but narrowing down the search to specific target areas helps. The tools listed aren’t necessarily the best available nor the most suitable for your specific needs. You still need to do the necessary due diligence to ensure you pick the best tools for your environment. A detailed explanation of what each level of GitOps maturity looks like in practice.
This setup is suitable when
you deploy new models based on new data, rather than based on new ML ideas. MLOps level 0 is common in many businesses that are beginning to apply ML to
their use cases. This manual, data-scientist-driven process might be sufficient
when models are rarely changed or trained. The models fail to adapt to changes in the
dynamics of the environment, or changes in the data that describes the
environment. For more information, see
Why Machine Learning Models Crash and Burn in Production.
The fundamentals of continuous deployment in DevOps
CI stands for continuous integration, a fundamental DevOps best practice where developers frequently merge code changes into a central repository where automated builds and tests run. A helpful way to think about the difference between continuous delivery and continuous deployment is what each does. In a continuous delivery practice, software is built in a way where it can be manually released at any time. Automation is used to ensure code changes are reviewed, merged, tested, packaged, and moved to a production environment so the software is ready to be shipped to customers.
DevOps isn’t a destination, it’s a journey towards a frequent and more reliable release pipeline, automation and stronger collaboration between development, IT and business teams. This maturity model is designed to help you assess where your team is on their DevOps journey. As an extensible open-source MLOps framework to create production-ready machine learning pipelines, ZenML building a world where CI/CT/CD paradigms for ML pipelines are supported from the get-go. We do this by automating the model preparation and model training and model deployment. 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.
What is Continuous Delivery?
With this model we aim to be broader, to extend the concept beyond automation and spotlight all the key aspects you need to consider for a successful Continuous Delivery implementation across the entire organization. Investing time and money into moving up the DevOps maturity model is also highly beneficial. Each subsequent stage leads to faster time-to-market, improved reliability, reduced IT costs, and better-performing teams. Our CEO recently wrote about cloud maturity models that guide companies through the five stages of adopting cloud computing. To mature to the next level, organizations need to automate their DevOps processes and start to measure the success of their DevOps practices.
You plan the work, then build it, continuously integrate it, deploy it, finally support the end product and provide feedback back into the system. Once we deploy those features at once, many changes happen simultaneously, increasing risk exponentially, and once things go wrong, which feature caused the problem? Sometimes isn’t even a single feature issue, but a combination of multiple changes that were not tested together.
DevOps vs MLOps
It requires a mixture of rigorous testing, deep cross-team collaboration, advanced tooling, and workflow processes across the application design and development process. It allows you to quickly and safely iterate on software in production, while gaining realtime insights about your operations and your customer needs. As a result, it supports a culture of high ownership and cross-functional collaboration. DevOps will drastically improve the way your engineering and operations teams work. Understanding DevOps maturity models ensures that the framework is implemented robustly, so your team sees the results as quickly as possible. Parallel software deployment environments don’t require cloud services, but they are much easier to set up when infrastructure is delivered instantly as a service.