base on Open Source AI/ML Platform  # Metaflow Metaflow is a human-friendly library that helps scientists and engineers build and manage real-life data science projects. Metaflow was [originally developed at Netflix](https://netflixtechblog.com/open-sourcing-metaflow-a-human-centric-framework-for-data-science-fa72e04a5d9) to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. For more information, see [Metaflow's website](https://metaflow.org) and [documentation](https://docs.metaflow.org). ## From prototype to production (and back) Metaflow provides a simple, friendly API that covers foundational needs of ML, AI, and data science projects: <img src="./docs/prototype-to-prod.png" width="800px"> 1. [Rapid local prototyping](https://docs.metaflow.org/metaflow/basics), [support for notebooks](https://docs.metaflow.org/metaflow/visualizing-results), and [built-in experiment tracking and versioning](https://docs.metaflow.org/metaflow/client). 2. [Horizontal and vertical scalability to the cloud](https://docs.metaflow.org/scaling/remote-tasks/introduction), utilizing both CPUs and GPUs, and [fast data access](https://docs.metaflow.org/scaling/data). 3. [Managing dependencies](https://docs.metaflow.org/scaling/dependencies) and [one-click deployments to highly available production orchestrators](https://docs.metaflow.org/production/introduction). ## Getting started Getting up and running is easy. If you don't know where to start, [Metaflow sandbox](https://outerbounds.com/sandbox) will have you running and exploring Metaflow in seconds. ### Installing Metaflow in your Python environment To install Metaflow in your local environment, you can install from [PyPi](https://pypi.org/project/metaflow/): ```sh pip install metaflow ``` Alternatively, you can also install from [conda-forge](https://anaconda.org/conda-forge/metaflow): ```sh conda install -c conda-forge metaflow ``` If you are eager to try out Metaflow in practice, you can start with the [tutorial](https://docs.metaflow.org/getting-started/tutorials). After the tutorial, you can learn more about how Metaflow works [here](https://docs.metaflow.org/metaflow/basics). ### Deploying infrastructure for Metaflow in your cloud <img src="./docs/multicloud.png" width="800px"> While you can get started with Metaflow easily on your laptop, the main benefits of Metaflow lie in its ability to [scale out to external compute clusters](https://docs.metaflow.org/scaling/remote-tasks/introduction) and to [deploy to production-grade workflow orchestrators](https://docs.metaflow.org/production/introduction). To benefit from these features, follow this [guide](https://outerbounds.com/engineering/welcome/) to configure Metaflow and the infrastructure behind it appropriately. ## [Resources](https://docs.metaflow.org/introduction/metaflow-resources) ### [Slack Community](http://slack.outerbounds.co/) An active [community](http://slack.outerbounds.co/) of thousands of data scientists and ML engineers discussing the ins-and-outs of applied machine learning. ### [Tutorials](https://outerbounds.com/docs/tutorials-index/) - [Introduction to Metaflow](https://outerbounds.com/docs/intro-tutorial-overview/) - [Natural Language Processing with Metaflow](https://outerbounds.com/docs/nlp-tutorial-overview/) - [Computer Vision with Metaflow](https://outerbounds.com/docs/cv-tutorial-overview/) - [Recommender Systems with Metaflow](https://outerbounds.com/docs/recsys-tutorial-overview/) - And more advanced content [here](https://outerbounds.com/docs/tutorials-index/) ### [Generative AI and LLM use cases](https://outerbounds.com/blog/?category=Foundation%20Models) - [Infrastructure Stack for Large Language Models](https://outerbounds.com/blog/llm-infrastructure-stack/) - [Parallelizing Stable Diffusion for Production Use Cases](https://outerbounds.com/blog/parallelizing-stable-diffusion-production-use-cases/) - [Whisper with Metaflow on Kubernetes](https://outerbounds.com/blog/whisper-kubernetes/) - [Training a Large Language Model With Metaflow, Featuring Dolly](https://outerbounds.com/blog/train-dolly-metaflow/) ## Get in touch There are several ways to get in touch with us: - [Slack Community](http://slack.outerbounds.co/) - [Github Issues](https://github.com/Netflix/metaflow/issues) ## Contributing We welcome contributions to Metaflow. Please see our [contribution guide](https://docs.metaflow.org/introduction/contributing-to-metaflow) for more details. ", Assign "at most 3 tags" to the expected json: {"id":"12061","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"