metaflow
starter-workflows
metaflow | starter-workflows | |
---|---|---|
24 | 262 | |
7,607 | 8,437 | |
1.2% | 1.2% | |
9.2 | 8.6 | |
1 day ago | 6 days ago | |
Python | TypeScript | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
metaflow
- FLaNK Stack 05 Feb 2024
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
- In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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Needs advice for choosing tools for my team. We use AWS.
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.
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Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
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[OC] Gender diversity in Tech companies
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).
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Model artifacts mess and how to deal with it?
Check out Metaflow by Netflix
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Going to Production with Github Actions, Metaflow and AWS SageMaker
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.
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Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
starter-workflows
- Say Goodbye to Manual Deployments: Automate Your EC2 Autoscaling with CodeDeploy and GitHub Actions
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Level Up Your Projects with GitHub Actions & CI/CD
GitHub, as one of the leading web-based Git repository hosting service, provides a powerful suite of CI/CD tools in the form of GitHub Actions. These are directly integrated into the platform which empowers developers to increase the speed, efficiency and reliability of delivering products. In this brief article, we will take a look at what CI/CD is, why we should use it, as well as some of its applications in my projects.
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How to Manage Terraform with GitHub Actions
GitHub Actions is a modern CI/CD tool integrated natively on GitHub. Itenables the rapid automation of build, test, deployment, and other custom workflows on GitHub with no need for external tools.
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Kubernetes CI/CD Pipelines
GitHub Actions is GitHub's CI/CD solution. You can use it to run automated tasks each time you change your code. Although the platform lacks a built-in Kubernetes integration, third-party plugins such as Azure's Deploy to Kubernetes Cluster action can automate deployments and manage different rollout strategies.
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Most Useful CI/CD Tools for DevOps
GitHub Actions is a feature-rich CI/CD platform embedded within GitHub, enabling developers to automate, customize, and execute software development workflows directly in their repositories. An Action inside GitHub Actions is a discrete unit of automation that performs a specific task within a workflow. All the Actions are reusable, and there are many to choose from. You can even create your own reusable ones.
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Awesome GitHub Action Workflows
actions/starter-workflows
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Laravel code-quality tools
The real power of using PHP code-quality tools is when it’s added to your continuous integration process, which means it automatically checks the code every time someone makes a push or pull request to your project repo. In this section, we'll be looking at how to do just that. GitHub actions is available for free so we'll use it for demo purposes. Note that there are some limits to private repos, so set your test repo to public if you can.
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Elevate Your GitHub README Game
You can even automate the running of this script — hence the directory name automation — to happen every time the data changes, using GitHub Actions.
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GitHub Status Checks and Branch Protection Made Easy
# Based on https://github.com/actions/starter-workflows/blob/main/ci/node.js.yml name: CI on: pull_request: branches: - main jobs: ci: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-node@v2 with: node-version: lts/* cache: 'npm' - run: npm ci - run: npm run build --if-present - run: npm test
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GitHub Actions for Perl Development
You might remember that I’ve been taking an interest in GitHub Actions for the last year or so (I even wrote a book on the subject). And at the Perl Conference in Toronto last summer I gave a talk called “GitHub Actions for Perl Development” (here are the slides and the video).
What are some alternatives?
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
argocd-image-updater - Automatic container image update for Argo CD
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
CppCon2020 - Slides and other materials from CppCon 2020
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
NewPipe - A libre lightweight streaming front-end for Android.
kedro-great - The easiest way to integrate Kedro and Great Expectations
react-native-dotenv - Load react native environment variables using import statements for multiple env files.
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
nnn - n³ The unorthodox terminal file manager
dvc - 🦉 ML Experiments and Data Management with Git
Real_Time_Image_Animation - The Project is real time application in opencv using first order model