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    <title>lifecycle</title>
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    <updated>2026-08-13T13:02:56+00:00</updated>
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            <id>https://links.biapy.com/links/1458</id>
            <title type="text"><![CDATA[Kargo]]></title>
            <link rel="alternate" href="https://kargo.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1458"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Multi-Stage GitOps Continuous Promotion.  Application lifecycle orchestration.
Seamlessly orchestrate stage-to-stage deployments, 
without custom scripts or CI pipelines.

Kargo is a next-generation continuous delivery and application lifecycle orchestration platform for Kubernetes. It builds upon GitOps principles and integrates with existing technologies, like Argo CD, to streamline and automate the progressive rollout of changes across the many stages of an application&amp;#039;s lifecycle.

- [Kargo @ GitHub](https://github.com/akuity/kargo).

Related contents:

- [Change Management with the Pulumi Kubernetes Operator and Kargo @ Pulumi Blog](https://www.pulumi.com/blog/pulumi-kubernetes-operator-and-kargo/).
- [GitOps architecture, patterns and anti-patterns @ Platform Engineering](https://platformengineering.org/blog/gitops-architecture-patterns-and-anti-patterns).
- [From Commit to Production: Hands-On GitOps Promotion with GitHub Actions, Argo CD, Helm, and Kargo @ freeCodeCamp](https://www.freecodecamp.org/news/from-commit-to-production-hands-on-gitops-promotion-with-github-actions-argo-cd-helm-and-kargo/).]]>
            </summary>
            <updated>2026-04-15T06:08:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5073</id>
            <title type="text"><![CDATA[knifecycle]]></title>
            <link rel="alternate" href="https://github.com/nfroidure/knifecycle" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5073"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Manage your NodeJS processes&amp;#039;s lifecycle automatically with an unobtrusive dependency injection implementation.]]>
            </summary>
            <updated>2025-08-29T06:03:29+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6533</id>
            <title type="text"><![CDATA[MLflow]]></title>
            <link rel="alternate" href="https://mlflow.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6533"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An open source platform for the machine learning lifecycle.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud).

- [MLflow @ GitHub](https://github.com/mlflow/mlflow).]]>
            </summary>
            <updated>2025-08-29T10:05:36+00:00</updated>
        </entry>
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