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https://github.com/raptor-ml/raptor

Transform your pythonic research to an artifact that engineers can deploy easily.
https://github.com/raptor-ml/raptor

ai-infra data-engineering data-science dataops feature-engineering feature-extraction feature-platform featurestore kubeflow kubernetes machine-learning ml mlops model-deployment production raptor raptor-ml reactive-ml

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Transform your pythonic research to an artifact that engineers can deploy easily.

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README 

        



[![Go Report Card][go-report-card-shield]][go-report-card-url]

[![Go Reference][godoc-shield]][godoc-url]

[![E2E Tests][e2e-tests-shield]][e2e-tests-url]

[![CII Best Practices][best-practices-shield]][best-practices-url]

[![Forks][forks-shield]][forks-url]

[![Stargazers][stars-shield]][stars-url]

[![Issues][issues-shield]][issues-url]

[![MIT License][license-shield]][license-url]

[![LinkedIn][linkedin-shield]][linkedin-url]








    

        RaptorML - Production-ready feature engineering

    





    


From notebook to production


    Transform your data science to production-ready artifacts







Raptor frees data scientists and ML engineers to build and deploy operational models and ML-driven functionality,

*without learning backend* engineering.



It **compiles** your python research code to production artifacts, and takes care of the engineering concerns such as

scalability and reliability using best-practices on Kubernetes.



[**Explore the docs »**][docs-url]



[**Getting started in 5 minutes »**][colab-url] · [Report a Bug][issues-url] · [Request a Feature][issues-url]






[![RaptorML Screen Shot][product-screenshot]][colab-url]



## 🧐 What is Raptor?



Raptor frees data scientists and ML engineers to **focus on the data science and research work**, and build operational

models and ML-driven functionality **without learning backend engineering**. Focus on what you're good at, increase your

end-to-end velocity, and **close the gap between research and production**.



With Raptor, you can export your Python research code as **standard production artifacts**, and deploy them to

Kubernetes. Once they deployed, Raptor optimizes data processing and feature calculation for production, deploys models

to Sagemaker or Docker containers, and connects to your production data sources, scaling, high availability, caching,

monitoring, and all other backend concerns.



[![Colab][colab-button]][colab-url]



## 😍 Why people *love* Raptor? and how does it change their lives?



**Raptor is made by and for data scientists and ML engineers**. We know how hard it is to build and deploy models to be

an integral part of your products, and we want to make it easier.



Before Raptor, data scientists had to work closely with backend engineers to build a "production version" of their work:

connect to data sources, transform their data with Flink/Spark or even Java, create APIs, dockerizing the model, handle

scaling and high availability, and more.



![High-level view of Raptor](.github/simplified-high-level.png)



With Raptor, data scientists can focus *only* on their research and model development, then export their work to

production. Raptor takes care of the rest, including connecting to data sources, transforming the data, deploying and

connecting the model, etc. This means data scientists can focus on what they do best, and Raptor handles the rest.



### ⭐️ Key Features



* **Focus on _your_ work**: Raptor frees data scientists and ML engineers to focus on the model, without

  learning backend engineering. Stop worrying about the engineering concerns, and focus on what you're good at.

* **Eliminate serving/training skew**: You can use the same code for training and production to avoid training serving

  skew.

* **Real-time/on-demand**: Raptor optimizes feature calculations and predictions to be performed at the time of the

  request.

* **Seamless caching and storage**: Raptor uses an integrated caching system, and store your historical data for

  training purposes. So you won't need any other data storage system such as "Feature Store".

* **Turns data science work into production artifacts**: Raptor implements best-practice functionalities of Kubernetes

  solutions, such as scaling, health, auto-recovery, monitoring, logging, and more.

* **Integrates with R&D team**: Raptor extends existing DevOps tools and infrastructure and allows you to connect your

  ML research to the rest of your organization's R&D ecosystem, utilizing tools such as CI/CD and monitoring.



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## 🚀 Getting Started



To start, install [Raptor LabSDK](https://pypi.org/project/raptor-labsdk/). The LabSDK is a Python package that help

you develop models and features in notebooks or IDEs.



```console

pip install raptor-labsdk

```



### ⚡ Quick Example



```python

import pandas as pd

from raptor import *

from typing_extensions import TypedDict



@data_source(

    training_data=pd.read_csv(

        'https://gist.githubusercontent.com/AlmogBaku/8be77c2236836177b8e54fa8217411f2/raw/hello_world_transactions.csv'),

    production_config=StreamingConfig()

)

class BankTransaction(TypedDict):

    customer_id: str

    amount: float

    timestamp: str



# Define features 🧪

@feature(keys='customer_id', data_source=BankTransaction)

@aggregation(function=AggregationFunction.Sum, over='10h', granularity='1h')

def total_spend(this_row: BankTransaction, ctx: Context) -> float:

    """total spend by a customer in the last hour"""

    return this_row['amount']



@feature(keys='customer_id', data_source=BankTransaction)

@freshness(max_age='5h', max_stale='1d')

def amount(this_row: BankTransaction, ctx: Context) -> float:

    """total spend by a customer in the last hour"""

    return this_row['amount']



# Train the model 🤓

@model(

    keys='customer_id',

    input_features=['total_spend+sum'],

    input_labels=[amount],

    model_framework='sklearn',

    model_server='sagemaker-ack',

)

@freshness(max_age='1h', max_stale='100h')

def amount_prediction(ctx: TrainingContext):

    from sklearn.linear_model import LinearRegression

    df = ctx.features_and_labels()

    trainer = LinearRegression()

    trainer.fit(df[ctx.input_features], df[ctx.input_labels])

    return trainer



amount_prediction.export()  # Export to production 🎉

```



This will generate a bunch of artifacts in the `out` directory. The `out` directory also includes a `Makefile` that can

be used for integration in any CI/CD pipeline, or even invoked manually.



[![Colab][colab-button-expand]][colab-url]



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## 🥊 How does Raptor different than ___ ?



### MLOps platforms (MLFlow, Kubeflow, Metaflow, Sagemaker, VertexAI, etc.)



Traditional MLOps platforms are focused on managing the ML resources lifecycle and are not designed for building

operational

models and features. Raptor is designed for building operational models and features, and can be integrated with MLOps

platforms.



### Feature Stores (Hopsworks, Feast, etc.)



Feature store is a data storage system that stores pre-computed features for training and online purposes. That means

you need to orchestrate the pre-computation of the features, store them, connect them to your model, and write ad-hoc

backend code.



Raptor takes a radically different approach. You focus on the model, and Raptor takes care of the rest. Raptor has a

built-in caching system that allows you to achieve similar results to a feature store but without the need to

orchestrate the data pipeline and the model deployment directly.



### Model Servers (Sagemaker, BentoML, KServe, etc.)



Model servers are designed for serving models in production. They are not designed for building models and features for

production. In fact, Raptor integrates seamlessly with Model Servers(such as Sagemaker, BentoML, etc.) to serve your

models.



## 💡 How does it work?



The work with Raptor starts in your research phase in your notebook or IDE. Raptor allows you to write your ML work in a

translatable way for production purposes.



Models and Features in Raptor are composed of a declarative part(via Python's decorators) and a function code. This

way, Raptor can translate the heavy-lifting engineering concerns(such as aggregations or caching) by implementing the

"declarative part", and optimizing the implementation for production.



![Features are composed from a declarative part and a function code][feature-py-def]



After you are satisfied with your research results, "export" these definitions, and deploy it to Kubernetes using

standard tools; Once deployed, Raptor Core(the server-side part) is extending Kubernetes with the ability to implement

them. It takes care of the engineering concerns by managing and controlling Kubernetes-native resources such as

deployments to connect your production data sources and run your business logic at scale.



You can read more about Raptor's architecture in [the docs][docs-url].



(back to top)



## ⎈ Production Installation



**Raptor installation is not required for training purposes**.

You only need to install Raptor *when deploying to production* (or staging).



Learn more about production installation at [the docs][docs-url].



### 🏗️ Prerequisites



1. Kubernetes cluster (including EKS, GKE, etc.)

2. Redis server (> 2.8.9)

3. Optional: Snowflake or S3 bucket (to record historical data for retraining purposes)



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## 🏔 Roadmap



- [ ] S3 historical storage plugins

    - [x] S3 storing

    - [ ] S3 fetching data - Spark

- [ ] Deploy models to model servers

    - [x] Sagemaker ACK

    - [ ] VertexAI

    - [ ] Seldon

    - [ ] Kubeflow

    - [ ] KFServing

    - [ ] Standalone

- [ ] Large-scale training

- [ ] Support more data sources

    - [x] Kafka

    - [x] GCP Pub/Sub

    - [x] Rest

    - [ ] Snowflake

    - [ ] BigQuery

    - [ ] gRPC

    - [ ] Redis

    - [ ] Postgres

    - [ ] GraphQL



See the [open issues](issues-url]) for a full list of proposed features (and known issues)

.



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## 👷‍ Contributing



Contributions make the open-source community a fantastic place to learn, inspire, and create. Any contributions you make

are **greatly appreciated** (not only code! but also documenting, blogging, or giving us feedback) 😍.



Please fork the repo and create a pull request if you have a suggestion. You can also simply open an issue and choose "

Feature Request" to give us some feedback.



**Don't forget to give the project [a star](#top)! ⭐️**



For more information about contributing code to the project, read the [`CONTRIBUTING.md`](./CONTRIBUTING.md) file.



(back to top)



## 📃 License



Distributed under the Apache2 License. Read the `LICENSE` file for more information.



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## 👫 Joining the community



You can join the Raptor community on [Slack][community], follow us

on [Twitter][twitter], and participate in the issues and pull requests.



**Don't forget to give the project [a star](#top)! ⭐️**



(back to top)



[godoc-shield]: https://pkg.go.dev/badge/github.com/raptor-ml/raptor.svg



[godoc-url]: https://pkg.go.dev/github.com/raptor-ml/raptor



[contributors-shield]: https://img.shields.io/github/contributors/raptor-ml/raptor.svg?style=flat



[contributors-url]: https://github.com/raptor-ml/raptor/graphs/contributors



[forks-shield]: https://img.shields.io/github/forks/raptor-ml/raptor.svg?style=flat



[forks-url]: https://github.com/raptor-ml/raptor/network/members



[stars-shield]: https://img.shields.io/github/stars/raptor-ml/raptor.svg?style=flat



[stars-url]: https://github.com/raptor-ml/raptor/stargazers



[issues-shield]: https://img.shields.io/github/issues/raptor-ml/raptor.svg?style=flat



[issues-url]: https://github.com/raptor-ml/raptor/issues



[e2e-tests-shield]: https://img.shields.io/github/actions/workflow/status/raptor-ml/raptor/test-e2e.yml?label=tests



[e2e-tests-url]: https://github.com/raptor-ml/raptor/actions/workflows/test-e2e.yml



[license-shield]: https://img.shields.io/github/license/raptor-ml/raptor.svg?style=flat



[license-url]: https://github.com/raptor-ml/raptor/blob/master/LICENSE.txt



[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat&logo=linkedin&colorB=555



[linkedin-url]: https://linkedin.com/company/raptor-ml



[go-report-card-shield]: https://goreportcard.com/badge/github.com/raptor-ml/raptor



[go-report-card-url]: https://goreportcard.com/report/github.com/raptor-ml/raptor



[best-practices-shield]: https://bestpractices.coreinfrastructure.org/projects/6406/badge



[best-practices-url]: https://bestpractices.coreinfrastructure.org/projects/6406



[colab-button]: https://img.shields.io/badge/-Getting_started_with_Colab-blue?style=for-the-badge&logo=googlecolab



[colab-button-expand]: https://img.shields.io/badge/-see_advanced_example_notebook-blue?style=for-the-badge&logo=googlecolab



[colab-url]: https://colab.research.google.com/github/raptor-ml/docs/blob/master/docs/docs/getting-started.ipynb



[docs-url]: https://raptor.ml/



[product-screenshot]: .github/demo.gif



[feature-py-def]: .github/feature-py-def.png



[community]: https://raptor.ml/docs/community



[twitter]: https://twitter.com/RaptorML