zenml
serverless-graphql
zenml | serverless-graphql | |
---|---|---|
33 | 215 | |
3,674 | 2,708 | |
2.2% | 0.1% | |
9.8 | 0.0 | |
4 days ago | over 1 year ago | |
Python | JavaScript | |
Apache License 2.0 | MIT License |
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.
zenml
- FLaNK AI - 01 April 2024
- What are some open-source ML pipeline managers that are easy to use?
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[P] I reviewed 50+ open-source MLOps tools. Hereβs the result
Currently, you can see the integrations we support here and it includes a lot of tools in your list. I also feel I agree with your categorization (it is exactly the categorization we use in our docs pretty much). Perhaps one thing missing might be feature stores but that is a minor thing in the bigger picture.
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[P] ZenML: Build vendor-agnostic, production-ready MLOps pipelines
GitHub: https://github.com/zenml-io/zenml
- Show HN: ZenML β Portable, production-ready MLOps pipelines
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[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
Hey everyone! At ZenML, we released today an integration that allows users to train and deploy models from pipelines in a simple way. I wanted to ask the community here whether the example we showcased makes sense in a real-world setting:
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How we made our integration tests delightful by optimizing our GitHub Actions workflow
As of early March 2022 this is the new CI pipeline that we use here at ZenML and the feedback from my colleagues -- fellow engineers -- has been very positive overall. I am sure there will be tweaks, changes and refactorings in the future, but for now, this feels Zen.
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Ask HN: Who is hiring? (March 2022)
ZenML is hiring for a Design Engineer.
ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows.
Weβre looking for a Design Engineer with a multi-disciplinary skill-set who can take over the look and feel of the ZenML experience. ZenML is a tool designed for developers and we want to delight them from the moment they land on our web page, to after they start using it on their machines. We would like a consistent design experience across our many touchpoints (including the [landing page](https://zenml.io), the [docs](https://docs.zenml.io), the [blog](https://blog.zenml.io), the [podcast](https://podcast.zenml.io), our social media, the product itself which is a [python package](https://github.com/zenml-io/zenml) etc).
A lot of this job is about communicating complex ideas in a beautiful way. You could be a developer or a non-coding designer, full time or part-time, employee or freelance. We are not so picky about the exact nature of this role. If you feel like you are a visually creative designer, and are willing to get stuck in the details of technical topics like MLOps, we canβt wait to work with you!
Apply here: https://zenml.notion.site/Design-Engineer-m-f-1d1a219f18a341...
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How to improve your experimentation workflows with MLflow Tracking and ZenML
The best place to see MLflow Tracking and ZenML being used together in a simple use case is our example that showcases the integration. It builds on the quickstart example, but shows how you can add in MLflow to handle the tracking. In order to enable MLflow to track artifacts inside a particular step, all you need is to decorate the step with @enable_mlflow and then to specify what you want logged within the step. Here you can see how this is employed in a model training step that uses the autolog feature I mentioned above:
- ZenML helps data scientists work across the full stack
serverless-graphql
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Testing AWS Lambda Functions (Serverless Framework) with OpenTelemetry and Tracetest
Since then, the ecosystem has changed. Using the Serverless Framework makes deployment simpler. We released the managed Tracetest App making any serverless-based systems simpler to instrument and test. You can now test public-facing apps with no infra overhead!
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The 2024 Web Hosting Report
We see some great results from using these in conjunction with frameworks such as SST or Serverless, and also some real spaghetti from people who organically proliferate 100βs of functions over time and lose track of how they relate to each other or how to update them safely across time and service. Buyer beware!
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Deploy app to AWS by using Serverless Framework
When we think about AWS serverless service, the first thing that comes to our mind is Lambda function. Yes, the quickest way to deploy this backend Express JS app to AWS is to deploy it as a Lambda function. The easiest way is using Serverless Framework.
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Lambda Scheduling & Event Filtering with EventBridge using Serverless Framework
Serverless Framework: https://www.serverless.com/
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The Top 10 GitHub Repositories Making Waves ππ
Github | Website
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Instrumenting AWS Lambda functions with OpenTelemetry SDKs
In this example, we're using the serverless framework to quickly set up the Lambda function along with an API gateway for the entry point. The lambda function is a simple Koa REST API with a few functional endpoints.
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A Beginner's Guide to the Serverless Application Model (SAM)
Naturally, there are several options available to declare your cloud resources. The options with the most popularity are the CDK, AWS CloudFormation, SST, Serverless framework, Terraform, and AWS SAM. There are others, but when talking about Infrastructure as Code (IaC), these are the ones you hear about most often.
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π₯ The Best Serverless Framework in 2023: A Data-Driven Showdown for AWS Projects
1 - Serverless + AWS CDK + Lift: An integration that amps up the traditional Serverless Framework with Lift's static frontend construct and CDK's robust infra definition.
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Serverless Semantic Search, Free tier only
It's a bit easier in Python if you use tools like https://www.serverless.com/. I'm not sure if Rust has something similar yet.
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Trace-based Testing AWS Lambda with Tracetest, ECS Fargate, and Terraform
Serverless
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
LocalStack - π» A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
Serverless-Boilerplate-Express-TypeScript - πππ Boilerplate and Starter for Serverless framework, ExpressJS, TypeScript, Prisma and MongoDB β‘οΈ Made with developer experience first: Serverless framework + Live reload + Offline support + ExpressJS + TypeScript + ESLint + Prettier + Husky + Commitlint + Lint-Staged + Jest + Dotenv + esbuild + VSCode
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
copilot-cli - The AWS Copilot CLI is a tool for developers to build, release and operate production ready containerized applications on AWS App Runner or Amazon ECS on AWS Fargate.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
terraform - Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
Poetry - Python packaging and dependency management made easy
electrodb - A DynamoDB library to ease the use of modeling complex hierarchical relationships and implementing a Single Table Design while keeping your query code readable.
pulsechain-testnet
chalice - Python Serverless Microframework for AWS