Cloud environments have become ever more complex, with an increasingly interconnected set of services. To tame this complexity and deliver differentiated digital experiences, IT, development, security, and business teams need automated workflows throughout these cloud ecosystems. But to be scalable, they also need low-code/no-code solutions that don’t require a lot of spin-up or engineering expertise.

IT leaders know that managing cloud environments through traditional manual monitoring practices will no longer suffice. According to recent Dynatrace data, 59% of CIOs say the increasing complexity of their technology stack could soon overload their teams without a more automated approach to IT operations and automated workflows.

Further, according to a Gartner® report, by 2025, 95% of decisions that currently use data will be at least partially automated.*

Development teams need automated workflows so they’re not stuck manually monitoring all stages of the software development lifecycle in their cloud environments. Similarly, security teams need to discover and automatically route security vulnerabilities to the right people to ensure prompt action. And operations teams need to forecast cloud infrastructure and compute resource requirements, then automatically provision resources to optimize digital customer experiences.

With the Dynatrace modern observability platform, teams can now use intuitive, low-code/no-code toolsets and causal AI to extend answer-driven automation for business, development and security workflows.

Low-code/no-code AutomationEngine fuels workflow automation

The Dynatrace platform brings modern observability and workflow automation to the fore.

With AutomationEngine, IT teams can now use their observability, security, and business data to automate workflows throughout hybrid and multicloud ecosystems.

For example, with AutomationEngine, teams can automate remediation and progressive delivery to continuously evaluate application performance against specific, measurable service-level objectives.

AutomationEngine also enables automated routing of security vulnerabilities to the proper teams while reducing false positives to ensure prompt action.

But automating workflows requires precise, accurate, and real-time answers from data that’s trustworthy.

This is the role of Dynatrace causal AI, fueled by Davis and topological mapping through Smartscape. With Davis and Smartscape, the Dynatrace platform provides teams with precise answers about the source of problems and incidents. Then, teams can use the AutomationEngine and its easy low-code/no-code interface to create automated workflows to enable various tasks that previously required manual work. An automated workflow, for example, might identify an application issue, send an alert to ServiceNow, generate a help desk ticket, and even trigger a remediation step.

All these tasks can take place seamlessly—ultimately resolving issues or identifying them proactively—and without needing to interrupt an engineer from a more strategic task.

Using Dynatrace AutomationEngine, teams can forecast future requirements and automate the provisioning of cloud infrastructure and compute resources to optimize the user experience. In addition, they can automatically route precise answers about performance and security anomalies to relevant teams to ensure action in a timely and efficient manner.

Bringing precise, answer-driven automation to observability, security, and business

As IT operations teams, security, DevOps, and others look to automate workflows, they need a solid platform foundation that enables data in context as well as provides precise and trustworthy answers.

To date, traditional observability tools ingest and process only partial data in silos which makes them ineffective to truly address application performance or security issues. Others lack causal AI to process data in full context that can actually pinpoint the root cause of problems.

With a data lakehouse, DevSecOps and business teams can aggregate, store, and centralize structured and unstructured data in one cost-efficient repository without predetermining which data is going to be necessary to generate insights in the future.

The Dynatrace Grail data lakehouse enables teams to ingest logs, metrics, traces, business events, and other data to get a full picture of their hybrid and multicloud environments. Because all the data is already contextualized and centralized, teams no longer have to manually sift through data and alerts to identify the team responsible for the problem. In turn, AutomationEngine uses this data in context to eliminate manual tasks and enable data-driven, automated workflows.

The low-code/no-code AutomationEngine brings several benefits to customers.

1. AutomationEngine enables user-friendly low-code/no-code automation

Even non-automation engineers can easily create ad-hoc automated workflows using a low-code/no-code interface. For example, teams can schedule a routine task or routing alerts with a visual drag-and-drop workflow creation experience. Operations teams can visualize their runbook automation as boxes and lines with flow logic for operational tasks. For example, using drag-and-drop on visual elements, teams can create an automation that queries a ticketing system like Jira or sends a Slack message to a specific person upon discovering an issue or vulnerability.

Cloud-native engineers benefit by creating GitOps-style automation-as-code and can evaluate Kubernetes events for faster issue remediation. And, ultimately, platform and site reliability engineers can provide answer-driven automation and higher-quality software with automated security and quality gates.

2. Tool consolidation reduces complexity

AutomationEngine reduces the number of home-grown tools teams need to rely on and improves the interoperability of ecosystem tools. As a result, it becomes easier to automate processes and reduce complexity. Further, AutomationEngine provides a centralized platform for managing and optimizing automation processes. This helps organizations chart a path to automation, improving efficiency and reducing the risk of errors or inconsistencies.

3. Compliance and data privacy are built into automated workflows

With AutomationEngine woven into the Dynatrace platform, it reduces the risk of sensitive data leaving the system. Further, all automated workflows are governed by an audit trail, access control, SSO, and security protection. This unburdens teams so they can concentrate on the actual automation task. Finally, a dedicated EdgeConnect component enables secure integrations for on-premises systems.

4. Answer-driven automation

When automation is based on false positives, it creates a situation of garbage in, garbage out. AutomationEngine is integrated within the Dynatrace platform, including Grail and Davis AI. Trustworthy, accurate answers and insights are a prerequisite for reliable security, business, and IT automation, especially when implementing feedback loop-based automation. This is true for workflow-based automation, as well as modern, event-driven cloud-native automation. With the ability to initiate and orchestrate automated actions throughout the ecosystem of collaboration—including ITSM, DevOps, and security tools–answer-driven automation delivers immediate and significant value to the organization.

In addition to the low-code/no-code AutomationEngine, Dynatrace announced a host of platform enhancements at Perform 2023 in Las Vegas. These enhancements further enable IT, DevOps, and security pros to move from data in context to insights on which they can execute with confidence, including the following:

• expanded Grail data lakehouse capabilities to enable new data types and graph analytics;
• a new user experience and interface and updated dashboards to more easily visualize trends;
• data Notebooks for petabyte-scale data exploration and analytics for real-time insights;
• AppEngine to create custom, compliant data-driven apps for answers and automation.

For more on AutomationEngine, visit our website.

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* ^{Gartner, “Emerging Tech: Venture Capital Growth Insights for Decision Intelligence Platforms,” Aakanksha Bansal, Alys Woodward, Akhil Singh, 10 February 2023.}

^{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.}

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According to Gartner, “the long-term impact of AIOps on IT operations will be transformative.”

AIOps will have a long-term and transformative impact on IT operations. The research firm predicts a significant uptick in AIOps investments over the next two years as organizations look for ways to improve IT outcomes, without breaking budgets or overworking technology staff.

The challenge? While some AIOps tools offer significant benefits over manual processes, not all of them can deliver the results organizations expect. To recognize both immediate and long-term benefits, organizations must deploy intelligent solutions that can unify management, streamline operations, and reduce overall complexity.

Here’s how.

What is AIOps and what are the challenges?

Artificial intelligence operations (AIOps) is an approach to software operations that combines AI-based algorithms with data analytics to automate key tasks and suggest solutions for common IT issues, such as unexpected downtime or unauthorized data access. In practice, AI-driven solutions help reduce the burden on IT teams by allowing them to offload routine monitoring and management tasks so they can focus on mission-critical concerns.

What challenges do AIOps tools address?

Consider data from our recent 2020 Global CIO Report, which found that 86% of organizations are now using cloud-native technologies and orchestration platforms such as microservices, containers, and Kubernetes to meet growing expectations from stakeholders, customers and employees. Despite all the benefits of modern cloud architectures, 63% of CIOs surveyed said the complexity of these environments has surpassed human ability to manage. To tame this complexity, organizations now use an average of 10 different monitoring tools. Despite these investments, these organizations have complete visibility into just 11% of the applications and infrastructure in their environments.

AIOps solutions offer the potential to increase observability, automate processes, and enhance value at scale.

Choosing the right AIOps tools for your needs

As reported by Forbes, AIOps is “moving from marketing hype to a useful tool being adopted across the enterprise.” While broader business deployment stems from increasingly sophisticated AI algorithms and the growing speed at which they’re able to discover new data relationships, it’s also a recognition of a new IT reality: AIOps is here to stay and improving quickly.

But not all approaches to AI are the same, and some are more effective than others for AIOps in modern environments.

The traditional machine learning approach relies on statistics to compile metrics and events and produce a set of correlated alerts. While this statistics-based approach can find and prioritize many alerts, it still relies on humans to analyze the output and determine the root cause of any anomalies or errors. It takes times to train statistics-based machine learning solutions, and this approach doesn’t scale easily with modern, dynamic cloud-native environments.

Another approach is deterministic AI, which uses systematic fault-tree analysis to immediately determine the root cause of a problem. This approach instantly detects anomalies — a service responding slowly, for example — and examines all its dependencies and all their dependencies (and so on) to pinpoint exactly what’s happening and where, in real time. Accompanied by an easily visualized map of the original malfunction’s route through all downstream processes, the deterministic AI approach can find the exact point the problem was triggered and its downstream effects, so analysts can focus on implementing solutions, automating responses, and developing new innovations.

As DevOps teams evaluate what AIOps solution to adopt, it’s important to know exactly what each does and how, since they don’t all provide the same level of autonomous insight.

What are the benefits of AIOps tools?

In theory, implementing an AIOps solution across enterprise IT environments can improve efficiency, drive better overall business value, and improve customer success. But what does this look like in practice?

Effectively deployed, potential benefits of AIOps initiatives include:

Improved alert management

Many IT teams now suffer from “alert fatigue” as the volume, velocity, and variety of alerts increase exponentially in multicloud environments. As noted by CDO Trends, however, effective AIOps implementation can help reduce false alarms by up to 90% and reduce the impact of redundant or irrelevant notifications.

Enhanced event prioritization

Which alerts demand priority response, and which can wait? The sheer volume of data sources and potential security concerns makes this challenging for any IT team — add in multiple cloud environments and open-source resources, and effective management becomes almost impossible. AIOps solutions that use advanced algorithms based on fault-tree analysis can immediately identify the alerts that matter so teams can respond rapidly, automate more processes, and stay focused on what drives the business.

Reduced IT spend

According to our research, IT and cloud operations teams spend 44% of their time just “keeping the lights on” — ensuring tools and technologies work as expected and on-demand. By applying automated, AIOps tools, however, companies could save an average of $4.8 million each year.

Streamlined digital transformation

Digital transformation now drives business success, but only if organizations can manage the complexity of their modern environments with a common language and a single source of truth for all aspects of digital performance. Here, AIOps tools can help companies accelerate their digital transformation by consuming and analyzing the ever-increasing amount, diversity, and velocity of data in their multi-cloud environments, and applying AI analytics to streamline and automate their operations workflows.

What is the impact of AIOps on the business?

While AIOps tools offer a host of potential benefits for IT teams, the biggest practical impact for organizations can be summed up simply: reducing the need for humans to perform manual tasks.

Here’s why it matters: for many businesses, increasing investments in cloud and mobile technologies are critical to retaining a competitive market edge at the cost of increased management complexity for IT teams. Standard management strategy is to expand the number of monitoring tools in use, which boosts the amount of actionable data available, but also puts IT teams in the unenviable position of trying to sort through disparate reports, alerts, and recommendations manually. This is a tough task for even large, experienced teams at the best of times, and almost impossible with the increasing use of third-party and open-source tools and technologies.

AIOps solutions offer a way to deliver on IT operations priorities such as complete observability across multi-cloud environments, accurate and reliable business metrics, and prioritized alerting that reduces the need for manual intervention.

Streamlining Success: a single AIOps platform

Despite the increasing maturity and availability of AI-driven tools, just 19% of operational processes for digital experience management and observability have been automated on average. So, what’s holding companies back? Forty-eight percent of CIOs point to lack of internal technical skill, while 43% highlight the absence of a common data model to enable accurate and consistent AI decisions. Furthermore, 42% percent cite the lack of existing frameworks to effectively implement automation.

To break through this barrier to automation, organizations need a single source of software intelligence they can rely on. AIOps tools that use statistical, correlation-based machine learning can’t scale with ever-increasing IT complexity. That’s why we built the Dynatrace platform to use deterministic AI with fault-tree analysis, which can provide immediate and accurate answers with no guessing or time-consuming model training.

Automatic, end-to-end observability of the entire software stack from a single source provides automated insights teams can use to automate and streamline operations so they can focus their time on solving issues that matter.

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Self-Service Progressive Delivery of Microservices, Automated SLI/SLO based Quality Gates, Continuous Feedback through ChatOps and Automatic Remediation of Production Issues are some of the capabilities you expect from a modern cloud-native software delivery platform.

In December Dynatrace announced Keptn, an open-source pluggable control plane enabling autonomous software delivery and operations for cloud-native applications. Mid-January, Keptn was included in the CNCF Landscape for Continuous Delivery and at the same time, Avodaq presented the How Keptn solves their challenges moving to Cloud Native & K8S (watch it on YouTube).

The recent improvements released in Keptn 0.6, a strong community with contributions from partners like Avodaq, Citrix or Neotys make it easier for everyone to build an autonomous cloud platform powered by Keptn. To get started – just follow the new Keptn Quickstart with a special gift from our friends at Google Cloud Platform!

Keptn Quickstart on GKE with $500 GCP credits

Run your Keptn installation for free on GKE! If you sign up for a Google Cloud account, Google gives you an initial $300 credit. For deploying Keptn you can apply for an additional $200 credit, which you can use towards that GKE cluster needed to run Keptn.

“Google GKE helps developers run advanced apps on a secured and managed Kubernetes service” said John Tripier, Partner Co-innovation Solution Lead at Google Cloud. “By launching these new tools, Dynatrace is enabling developers to further streamline and simplify their experience on GKE, ultimately helping businesses bring new services and products to their customers more quickly”

We want to say “Thank you Google” for your support on our mission towards Autonomous Cloud and helping us grow the user base of Keptn!

After you claimed your credit follow these 5 steps to test drive Keptn:

Step 1: Setup your GKE Cluster

Just follow the Keptn sizing and setup guidelines for GKE

Step 2: Install Keptn through the Keptn CLI

Just follow the Install Keptn instructions. It’s as easy as downloading the CLI and executing the following command:

$ keptn install --platform=gke

Step 3: Setup Progressive Multi-Stage Continuous Delivery for a Service

One of Avodaq’s challenges was having to maintain many pipeline scripts that implemented the logic of build, deploy, test, validate and promote. Keptn follows a declarative approach that eliminates the need for putting processes into scripts.

In order to set up a new Keptn project and onboard a service that will then be automatically delivered through your multi-stage delivery pipeline just follow the use case instructions on onboarding a custom service or watch the following video.

Congratulations: You have a self-service platform that allows you to deploy new versions of your service through a fully automated delivery pipeline with a single command:

$ keptn send event new-artifact --project=sockshop --service=carts --image=docker.io/keptnexamples/carts --tag=0.10.1

Step 4: Add SLO-based Quality Gates to your Delivery Pipeline

Automated promotion of an artifact from one stage to the next is great. But – what if the new version has performance, scalability or architectural flaws and therefore should never reach production? Keptn has a built-in quality gate capability that will evaluate a list of SLIs (Service Level Indicators) against SLOs (Service Level Objectives). Depending on the level of quality of the new artifact Keptn will either promote or stop an artifact. Keptn currently supports Dynatrace, Prometheus, and Neoload as SLI data sources.

To add a quality gate to your delivery pipeline simply follow the use case instructions on Deployments with Quality Gates or watch the following video.

Congratulations: You have a self-service platform that stops bad artifacts based on SLOs your developers define and store as an artifact in their Git repo. This is the end of bad deployments ending in production!

Step 5: Enable Auto-Remediation of Production Issues

Not every problem can be detected prior to the production environment. Some problems occur due to change in load patterns in production, an issue of the infrastructure or because of problems with individual features that are enabled through feature flagging frameworks. Keptn has a built-in capability for automating remediations based on problems detected by your production monitoring such as Dynatrace or Prometheus.

To add auto-remediation to your Keptn project simply follow the use case instructions for Self-Healing with Keptn or watch the following video.

Congratulations: You now have a self-service platform that auto-remediates problems in production. Gone are the times where you remote into machines during off-hours to fix problems manually.

More Keptn Use Cases

If you want to learn more check out our Keptn YouTube Channel where we can find more tutorials, demos as well as recordings of our Keptn community meetings.

Automate Keptn through the CLI and API

As you walk through the Use Cases you will notice that you can automate the interaction with Keptn either through the Keptn CLI or the Keptn’s API, e.g: push a new artifact through the progressive delivery pipeline. This allows you to easily integrate Keptn with other tools your using, e.g: Jenkins or GitLab Pipelines can build artifacts and then notify Keptn to deploy that new artifact!

Extend Keptn with custom services

If you want to extend Keptn with additional services or SLI providers check out the existing Keptn contributions and read up on how you can develop your own Keptn service.

Keptns Bridge

By now you have seen the Keptn’s Bridge which gives you full transparency into every stage, every deployment, every test, every quality gate evaluation, every promotion to another stage and how problems have been remediated. The following screenshot shows the early adopter version of the new Keptn’s bridge which you can easily enable by following the instructions here. We would be more than happy to get early feedback:

Got Feedback? Got Questions?

If you run into any problems or have questions, suggestions, ideas … explore these options

1. Keptns Documentation
2. Join our conversation on Slack
3. Send us a Tweet
4. Report a problem or enhancement idea on GitHub
5. Join our future community meetings

Thanks for joining our ride to the Autonomous Cloud and Keptn as a project that gets us there faster!

The post Quickstart to Autonomous Cloud with Keptn on GKE appeared first on Dynatrace news.

“Keptn is an open source enterprise-grade control plane for cloud-native continuous delivery and automated operations.” That’s the high-level introduction you will find on www.keptn.sh. But what does this really mean? Let me explain it in my words – or – just watch my latest YouTube Performance Clinic Tutorial on “Getting Started with keptn and Dynatrace” I ran with Dirk Wallerstorfer who leads the keptn Dev Team.

In our performance clinic, we pushed a simple node.js based microservice app we had available in 4 different builds on Docker hub through a two-stage Kubernetes environment. Keptn is taking care of:

1. Automated Deployments and Automated Tests
2. Automated Quality Gates through Pitometer – a Quality Gate as Code Service
3. Automated Operations by integrating with full stack monitoring such as Dynatrace

If you want to see keptn pushing notifications to Slack as an artifact is promoted through its release lifecycle or see the integration with Dynatrace on how keptn pushes deployment, test execution, and quality gate information to the correct Dynatrace entities, then its time to click on the play button to watch our keptn Performance Clinic!

Why we must build keptn?

We must build keptn because we have seen too many organizations struggle to deliver the promise of cloud native: delivery business value, deliver it fast & reliable and deliver it with better quality!

And we have facts to prove this thanks to our Autonomous Cloud Survey conducted by my colleague Katalin. Less than 5% of surveyed enterprises that work on cloud native projects deliver fast & with good quality:

First: Keptn solves Enterprise-Wide Continuous Delivery!

The first problem keptn solves is enterprise-wide automated continuous delivery! What does that mean? Most of the teams we talked with told us that they spend too much time and resources on building pipelines, custom integrations between tools and managing their own data stores. In large organizations, we have seen MANY teams spread across labs or geographies, building their own CD pipelines and spend a significant amount of time maintaining and nurturing this “soon to become monolithic legacy pipeline code”!

Second: Keptn is not just another CD Tool! It solves CD & CO!

After presenting the above slide, I often get to hear this comment: “How does this differ to JenkinsX, GitLab, Harness, Argo, Tekton, CodeFresh, … – isn’t this just another CD Tool?”

Keptn not only orchestrates Continuous Deployment, but it also orchestrates Continuous or Automated Operations. I really like the analogy that Alois Reitbauer uses when he explains the two core principles of keptn. He compares it with Launch and Mission Control at NASA when they launch a rocket:

Launch Control == Continuous Deployment

At NASA, Launch Control is responsible until the launch timer hits 00:00. Until that point, they can abort the mission based on the data they have!

With Continuous Deployment, the release team is responsible to safely bring an artifact (e.g: a new container image) into production. Along the way they have the chance to “abort” that deployment through different means, e.g: automated quality gates or by leveraging blue/green or canary releases in production. At any point though, they can stop an artifact from being rolled out to the whole user base.

Keptn, therefore, supports automated multi-stage unbreakable delivery pipelines with automated quality gates and the support for production-safe deployments!

Mission Control == Automated Operations

At NASA, Mission Control takes over once the rocket lifts off. They can’t abort the mission but need to handle any problem that comes their way to ensure that astronauts are safely getting up and down from space.

With Automated Operations, the operations team is responsible to do whatever it takes to minimize the impact to end users in case a problem arises. That will happen by e.g: scaling up in case more resources are needed, redirect traffic for certain users, clear log directories, … In order to react fast, it is mandatory to automate as many of these tasks.

Keptn, therefore, supports event-driven runbook automation to remediate problems detected in production as fast as possible to minimize the impact on end users.

I hope this answers the question that keptn is not just another CD tool!

Now – if you want to – you can use your existing CD tools and let them do the CD part. Then you can either pass control to keptn once CD is done – or – let keptn simply talk with your existing CD tool once a new artifact is ready to be deployed. The event-driven keptn architecture allows all these combinations – giving you the freedom to pick the tools that make the most sense for your organization, technology stack, and existing investment! We already have a team in Germany that is building a GitLabs keptn service – watch out for this and other contributions on https://github.com/keptn-contrib

Now – let’s talk a bit more about the architecture!

Keptn Architecture Explained

I have spent the past couple of weeks following the instructions on the keptn’s doc pages installing pretty much every version from 0.1.0 until the latest 0.2.2 (as of the writing of this blog). Since version 0.2.0 keptn builds on top of Knative, a platform for container-based serverless workloads. Knative allows keptn to interact with the actual DevOps tools to deploy, test, evaluate, promote, configure, notify, auto-remediate … as keptn pushes an artifact through your multi-stage delivery pipelines. This brings me to first slides I typically use explaining two core concepts of keptn: Uniform (=Tooling) and Shipyard (=Pipeline Definition):

Technically, uniforms are already available with keptn 0.2.x – but – it is not yet convenient to tell keptn which tools are part of your uniform. A more convenient way of defining a Uniform is coming in one of the next keptn releases (check out the keptns backlog on GitHub).

Uniforms are a VERY POWERFUL concept as it defines which tools you want to use for your pipelines without having to integrate these tools with each other as keptn is taking care of this. Even better is that you can update your uniforms and therefore add / remove tools as time goes by, e.g: add a security tool or replace your deployment tool with a single keptn CLI command. This for me is a very compelling feature and eliminates maintaining custom pipeline code!

In my presentations, I always show how the keptn CLI command for “wearing” a uniform will most likely look like (stay tuned and follow the progress keptn makes). The following animation shows how you will be able to define and apply a uniform and how you currently create a new project:

Now – let me give you a bit of background information and narrative to this animation:

• Uniform: as of keptn 0.2.x you don’t have to define your keptn uniform. When following the installation instructions in the doc keptn comes pre-installed with all available keptn services such as GitHub, Slack, Jenkins, ServiceNow and Pitometer.
• Keptn services will be installed in the keptn Kubernetes namespaces and will automatically be subscribed to the relevant knative channels so that these tools get triggered when keptn sends different events such as new-artifact, deploy, run-test, … If you want to write your own keptn service in order to integrate your own tools check out the documentation around Write your own keptn service.
• Shipyard: The example above shows the shipyard file that is part of the sockshop sample application that the keptn team is using for all their hands-on tutorials. Simply follow the onboarding a service (keptn 0.2.2 link) use case in the keptn doc where you will use that same file creating a stage pipeline (dev, stage, prod) with different deployment and testing strategies.
• Istio: Istio gets automatically installed by keptn. Keptn uses Istio for traffic routing to allow keptn to support the different deployment strategies such as blue/green, direct … keptn ensures that Istio is correctly installed and will also automatically create all helm charts for your services. All these helm charts and configuration files get automatically stored in your Git – following the GitOps approach!
• Git: Keptn follows GitOps. What does this mean? When you install keptn you have to give it a GitHub Organization and GitHub credentials. When you create a new project keptn will create a new GitHub repository for that project storing the shipyard file in the master branch. Keptn will also create branches in that GitHub repo, one for each stage in your shipyard file, e.g: dev, stage, prod. Any configuration file created or updated by keptn will be stored in the respective branch. This ensures that Git always reflects the source of truth. And as you will see later on – keptn will be triggered when config file change and keptn will do changes to the Git repo in order to promote or rollback artifacts.

Keptn in Action: Give it a try

If you follow the instructions on the keptn website or the keptn GitHub repo, you end up creating a new project and onboarding a new service. Feel free to either do it with the carts service that the keptn doc uses, feel free to follow the example I used in my keptn Performance Clinic or simply use your own services. Once you have your service onboarded, you can tell keptn about a new artifact which will kick-off the full Continuous Delivery and Automated Operations workflow as shown in the following animation:

Join the keptn Community

Last but not least: Our goal is to contribute keptn to CNCF (Cloud Native Computing Foundation) and for that we need to build a strong community. Help us with feedback, join the keptn Slack channel, build your own services and tell us about them so we can put them into our keptn-contrib GitHub org. Also, make sure to check out our community meetings as the team is regularly giving a LIVE update from the keptn headquarters! 😊

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