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Conference: KubeCon + CloudNativeCon North America 2022
Authors: Éamon Ryan, Hedley Simons
2022-10-28
Most people are well-aware of the benefits of GitOps for various workflows especially as it relates to Kubernetes deployments - it allows easy integration of approvals, verifiable change history and automation hooks. However, getting users to adopt a different way of working can be challenging - especially if it involves tools and methods that would be convenient for you, the administrator, but inconvenient for the end-user. So, what do you do when your end-users are not directly using Git in their day-to-day work, but you still want to add the benefits of GitOps to your deployments without slowing them down? Simple - you implement GitOps but hide the entire process from them! In this session, Éamon and Heds will take you through how they took an internal Grafana environment that had grown increasingly messy and unreliable due to usage by a rapidly expanding internal team - and transformed it into a repeatable, promotable, process-driven well-oiled machine fueled by GitOps, Kubernetes, Terraform and more - all without the end-users having to learn or interact with Git at all!
Conference: KubeCon + CloudNativeCon North America 2022
Authors: Saravanan Balasubramanian, Savin Goyal
2022-10-27
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The presentation discusses the challenges of introducing machine learning into applications and the need for infrastructure that can provide end-to-end solutions for the entire life cycle of machine learning. It also covers the importance of workflow orchestration and reproducibility in machine learning.
- Infrastructure that can provide end-to-end solutions for the entire life cycle of machine learning is necessary for successful implementation of machine learning into applications
- Workflow orchestration is important for productionizing machine learning workflows
- Reproducibility is important for ensuring trust in machine learning models
- Model deployment can mean many different things depending on the business context
Conference: Cloud Native SecurityCon North America 2022
Authors: Stephen Giguere
2022-10-25
tldr - powered by Generative AI
The presentation discusses the security challenges faced by open source projects and GitOps workflows, particularly in relation to GitHub Actions workflows. The speaker demonstrates potential abuses and vulnerabilities in GitHub Actions workflows and highlights the importance of implementing best practices to protect against attacks.
- Open source projects and GitOps workflows are vulnerable to security threats
- GitHub Actions workflows can be abused by malicious actors to gain access to sensitive information
- Best practices, such as implementing environmental protection and short-lived tokens, can help protect against attacks
Conference: OpenAI + Data Forum 2022
Authors: Bhakti Radharapu
2022-06-23
How do I measure fairness? Is my ML model biased? How do I remediate bias in my model? This talk presents an overview of the main concepts of identifying, measuring and remediating bias in ML systems at scale. We begin by discussing how to measure fairness in production models and causes of algorithmic bias in systems. We then deep-dive into performing bias remediation at all steps of the ML life-cycle: data collection, pre-processing, in-training, and post-processing. We will focus on a gamut of open source tools and techniques in the ecosystem that can be used to create comprehensive fairness workflows. These have not only been vetted by the academic ML community but have also scaled very well for industry-level challenges. We hope that by the end of this talk, ML developers will not only be able to "flag" fairness issues in ML but also "fix" them by incorporating these tools and techniques in their ML workflows.
Conference: OpenAI + Data Forum 2022
Authors: Christian Kadner
2022-06-23
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The Q4 Pipelines team proposes a new component registry to address problems with authoring, publishing, and maintaining components. The registry will have a unified YAML format, versioning and tagging capabilities, and direct integration with the Q4 Pipelines SDK. Third-party registries can also implement the server-side of the API. The Machine Learning Exchange is an example of a registry that is implementing the new protocol. It offers various asset types, including pipelines, components, models, data sets, and notebooks. Watson Studio Pipelines is also in open beta and provides a canvas for running experiments and integrating notebooks.
- Q4 Pipelines proposes a new component registry to address problems with authoring, publishing, and maintaining components
- The registry will have a unified YAML format, versioning and tagging capabilities, and direct integration with the Q4 Pipelines SDK
- Third-party registries can also implement the server-side of the API
- The Machine Learning Exchange is an example of a registry that is implementing the new protocol and offers various asset types
- Watson Studio Pipelines is in open beta and provides a canvas for running experiments and integrating notebooks
Conference: SupplyChainSecurityCon 2022
Authors: Laurent Simon, Asra Ali
2022-06-21
tldr - powered by Generative AI
The presentation discusses the importance of artifact attestation or salsa provenance in ensuring the authenticity of build artifacts and creating strong links between artifacts and their source repositories. It also highlights the various use cases of artifact attestation in the supply chain.
- Artifact attestation creates a strong link between build artifacts and their source repositories, ensuring authenticity and enabling the creation of policies.
- Artifact attestation can be used to enforce policies at different stages of the supply chain, including control plane, build time, and installation time.
- GitHub's dependency API and S-BOM API can benefit from artifact attestation to ensure the authenticity of dependencies and S-BOMs.
- Artifact attestation can be used to prove to third parties that S-BOMs are authentic and created without cheating or hiding vulnerabilities.
- Artifact attestation can be used for any kind of metadata, including static analysis tool results.
Conference: KubeCon + CloudNativeCon North America 2021
Authors: Matt Schallert, Dominik Tornow
2021-10-15
tldr - powered by Generative AI
The presentation discusses how declarative solutions, such as Operators, along with imperative solutions, such as Temporal, can be used to automate the operations of complex software systems on Kubernetes.
- Kubernetes can be characterized as an operation automation platform that implements operation automation with the help of Kubernetes controller and Kubernetes resources
- Controllers perform reconciliation, which is the transition of a system from its current state to its desired state
- Temporal is an OSS Workflow Orchestration Platform that guarantees that workflow execution cannot fail
- Temporal and Operators work in tandem and mitigate each other's limitations
- The M3DB Operator is an example of how Temporal and Operators can be combined to make managing M3DB easier
Conference: KubeCon + CloudNativeCon North America 2021
Authors: Alexander Matyushentsev, Jesse Suen
2021-10-14
tldr - powered by Generative AI
Argo CD is a lightweight and stable project that allows users to manage Kubernetes applications. The project is extensible and allows for the addition of new features through annotations. The Argo Proj Labs is a sister organization that hosts ecosystem projects from the community that complement the core projects. These ecosystem projects enhance the Argo CD experience and provide users with more options to manage their applications.
- Annotations allow for the expansion of a resource's spec without implementing functionality into the core controller logic
- Adding features through annotations allows for independent projects, higher development velocity, and earlier access to features
- Argo Proj Labs hosts ecosystem projects that complement the core projects and enhance the Argo CD experience
- The Argo CD Image Updater tool monitors container registries for new image tags and updates the git repo for Argo CD to deploy them
- Application Sets automate the creation of many applications, making it easier to manage hundreds of clusters or monorepos