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Increasingly utilizing AI to improve the CD 3.0 development cycle.
- I started writing news for the InfoQ .NET queue as a way of keeping up to date with technology, but I got so much more out of it.
- The Codefresh platform is a complete software supply chain to build, test, deliver, and manage software with integrations so teams can pick best-of-breed tools to support that supply chain.
- At this stage it will also be natural to start migrating scattered and ad-hoc managed application and runtime configuration into version control and treat it as part of the application just like any other code.
- It can also be used to benchmark the organization’s maturity level and track its progress over time.
Practicing MLOps means that you advocate for automation and
monitoring at all steps of ML system construction, including integration,
testing, releasing, deployment and infrastructure management. Delivering new software is the single most important function of businesses trying to compete today. Many companies get stuck with flaky scripting, manual interventions, complex processes, and large unreliable tool stacks across diverse infrastructure.
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Continuous delivery and continuous deployment, while closely related concepts, are sometimes used separately to specify just how much automation is happening. Continuous delivery makes up part of CI/CD, a method to frequently deliver software by automating some of the stages of app development. It establishes a process through which a developer’s changes to an application can be pushed to a code repository or container registry through automation. 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. Currently, the CD Maturity Model data is stored in the js/data/data_radar.js file, as an array of JavaScript object literals. It would be very easy to convert the project to use a data source, such as a static JSON or YAML file, or MongoDB database.
You don’t have to immediately move all of your processes
from one level to another. You can gradually implement these practices to help
improve the automation of your ML system development and production. The lowest maturity level is sometimes called the initial or regressive state because it is highly inefficient.
The result is a system that is totally reproducible from source control, from the O/S and all the way up to application. Doing this enables you to reduce a lot of complexity and cost in other tools and techniques for e.g. disaster recovery that serves to ensure that the production environment is reproducible. Instead of having a separate process, disaster recovery is simply done by pushing out the last release from the pipeline like any other release.
How is continuous delivery related to CI/CD?
You plan the work, then build it, continuously integrate it, deploy it, finally support the end product and provide feedback back into the system. To do so, you need a strong continuous integration pipeline that tests, packages, and delivers your releases. Eric Minick is a lead consultant at UrbanCode where he helps customers implement continuous delivery. Eric has been at the forefront of continuous integration and delivery for 8+ years as a developer, tester and consultant. Eric Minick discusses continuous delivery challenges in the enterprise where large projects, distributed teams or strict governance requirements have resulted in increased automation efforts throughout the life cycle. I discovered InfoQ’s contributor program earlier this year and have enjoyed it since then!
Automation brings the CI/CD approach to unit tests, typically during the development stage and integration stage when all modules are brought together. Containers are a common runtime destination for CI/CD pipelines, and if they’re in use at this first stage of the continuous delivery maturity model, development teams have usually adopted Docker images defined by a Dockerfile. A Continuous Delivery Maturity Model (CDMM) is a framework for assessing an organization’s maturity in implementing continuous delivery practices.
Software teams are left scrambling to understand their software supply chain and discover the root cause of failures. At beginner level, you start to measure the process and track the metrics for a better understanding of where improvement is needed and if the expected results from improvements are obtained. Reporting at this stage would typically include static analysis of code and quality reports which might be scheduled so that the latest reports are always accessible to facilitate decisions on quality and where improvements are needed. A typical organization will have one or more legacy systems of monolithic nature in terms of development, build and release. Many organizations at the base maturity level will have a diversified technology stack but have started to consolidate the choice of technology and platform, this is important to get best value from the effort spent on automation.
The “CD” in CI/CD can refer to continuous deployment or continuous delivery, which describe ways to automate further stages of the pipeline. The data analysis step is still a manual process for data scientists before
the pipeline starts a new iteration of the experiment. An optional additional component for level 1 ML pipeline automation is a
feature store. A feature store is a centralized repository where you
standardize the definition, storage, and access of features for training and
serving. A feature store needs to provide an API for both high-throughput batch
serving and low-latency real-time serving for the feature values, and to support
both training and serving workloads.
Doing this will also naturally drive an API managed approach to describe internal dependencies and also influence applying a structured approach to manage 3rd party libraries. At this level the importance of applying version control to database changes will also reveal itself. At expert level some organizations choose to make a bigger effort and form complete cross functional teams that can be completely autonomous. 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. It’s an answer to the problem of poor visibility and communication between dev and business teams. To that end, the purpose of continuous delivery is to ensure that it takes minimal effort to deploy new code.
Jump start the journey
This information lets you broaden the perspective for continuous improvement and more easy verify expected business results from changes. Moving to intermediate the level of automation requires you to establish a common information model that standardizes the meaning of concepts and how they are connected. This model will typically give answers to questions like; what is a component?
The first stage of maturity in continuous delivery entails extending software build standards to deployment. The team should define some repeatable, managed processes that get code to production. Developers shift build and deployment activities off of personal workstations — the usual location for ad hoc chaos — and onto a central, managed system available to all developers and the IT operations team. At expert level, some organizations will evolve the component based architecture further and value the perfection of reducing as much shared infrastructure as possible by also treating infrastructure as code and tie it to application components.
The first step in moving to DevOps is to pull from agile principles – people first, then process and tools. TESTING
Automatically testing newly developed features to avoid tedious work. INTEGRATION
Automatically building your software to shorten the development cycle. It’s built on Argo for declarative continuous delivery, making modern software delivery possible at enterprise scale. Writing for InfoQ has opened many doors and increased career opportunities for me. I was able to deeply engage with experts and thought leaders to learn more about the topics I covered.
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. This means no manual testing or verification is needed to pass acceptance but typically the process will still include some exploratory testing that feeds back into automated tests to constantly improve https://forexhero.info/ the test coverage and quality. 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 journey that started with the Agile movement a decade ago is finally getting a strong foothold in the industry.
The purpose of the maturity model is to highlight these five essential categories, and to give you an understanding of how mature your company is. Your assessment will give you a good base when planning the implementation of Continuous Delivery and help you identify initial actions that will give you the best and quickest effect from your efforts. The model will indicate which practices are essential, which should be considered advanced or expert and what is required to move from one level to the next. Even the most mature organizations that have complex multi-environment CI/CD pipelines continue to look for improvements. Feedback about the pipeline is continuously collected and improvements in speed, scale, security, and reliability are achieved as a collaboration between the different parts of the development teams. MLOps level 0 is common in many businesses that are beginning to apply ML to
their use cases.
It addresses the problem of overloading operations teams with manual processes that slow down the app delivery process. It builds on the benefits of continuous delivery by automating the next stage in the pipeline. To summarize, implementing ML in a production environment doesn’t only mean
deploying your model as continuous delivery maturity model an API for prediction. Rather, it means deploying an ML
pipeline that can automate the retraining and deployment of new models. Setting
up a CI/CD system enables you to automatically test and deploy new pipeline
implementations. This system lets you cope with rapid changes in your data and
business environment.
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. Find real-world practical inspiration from the world’s most innovative software leaders.
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. Another way to excel in ‘flow’ is by moving to distributed version control systems (DVCS) like Git, which is all about quick iterations, branching and merging – all things you need in a lean DevOps environment. 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. One small but impactful way to initiate culture change is to run workshops that identify areas of improvement between your dev & ops teams. One of the best known open source tools for CI/CD is the automation server Jenkins. Jenkins is designed to handle anything from a simple CI server to a complete CD hub.
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. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in their teams. It can help organizations identify initial actions that provide the most significant effect, while indicating which practices are essential, and which should be considered advanced or expert.
To automate the process of using new data to retrain models
in production, you need to introduce automated data and model validation steps
to the pipeline, as well as pipeline triggers and metadata management. The level of automation of these steps defines the maturity of the ML process,
which reflects the velocity of training new models given new data or training
new models given new
implementations. The following sections describe three levels of MLOps, starting
from the most common level, which involves no automation, up to automating both
ML and CI/CD pipelines. 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.
At this stage, when automation is applied to application delivery, it’s often ad hoc and isolated — usually instituted by a single workgroup or developer and focused on a particular problem. Nevertheless, organizations starting down the continuous delivery path have often standardized portions of software development, such as the build system using CMake, Microsoft Visual Studio or Apache Ant and a code repository, like GitHub. At this stage, DevOps teams — continuous delivery experts all adopt some form of DevOps structure — have fully automated a code build, integration and delivery pipeline. They’ve also automated the infrastructure deployment, likely on containers and public cloud infrastructure, although VMs are also viable. Hyper-automation enables code to rapidly pass through unit, integration and functional testing, sometimes within an hour; it is how these CD masters can push several releases a day if necessary.
- This maturity model will give you a starting point and a base for planning the transformation of the company towards Continuous Delivery.
- CDMM provides a structured way for organizations to assess and improve their ability to implement continuous delivery practices, which can lead to increased efficiency, quality, and stakeholder satisfaction.
- 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.
- 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.
- It establishes a process through which a developer’s changes to an application can be pushed to a code repository or container registry through automation.
- While they can serve as a starting point, they should not be considered as essential models to adopt and follow.
” 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. We’re the world’s leading provider of enterprise open source solutions—including Linux, cloud, container, and Kubernetes. We deliver hardened solutions that make it easier for enterprises to work across platforms and environments, from the core datacenter to the network edge. A CI/CD pipeline is a series of steps performed in order to deliver a new version of software.
In any ML project, after you define the business use case and establish the
success criteria, the process of delivering an ML model to production involves
the following steps. These steps can be completed manually or can be completed
by an automatic pipeline. The most effective improvement processes, whether they streamline manufacturing operations or speed up software development, describe the path to desired improvements — not just the end state. Continuous improvement processes never focus on the end state, because perfection, however it’s defined, can only be incrementally approached, never fully achieved. Continuous Delivery presents a compelling vision of builds that are automatically deployed and tested until ready for production. This deck presents a model for scoring yourself on the continuum and examples of how companies can decide what parts of CD to adopt first, later and not at all.
The Maturity Model guides the improvements of Continuous Delivery pipelines and/or software development processes in software organizations. The CD3M maturity model has five levels from Foundation level (1) towards Expert level (5). In each maturity level a number of practices needs to be implemented to advance the CD 3.0 pipeline.
A CI/CD pipeline introduces monitoring and automation to improve the application development workflow, particularly at the integration and testing phases, as well as during delivery and deployment. The Maturity Model Gap Analysis Tool is applicable to many discipline, not only Continuous Delivery. The application is built to be fully configurable and easily adaptable, by modifying the data file (js/data/data_radar.js). The default data file contains a sample data set, based on a fictions financial institution’s gap analysis.