Data Science is at the top of the IT world. It has increased in popularity over the last few years, but it’s still required a large number of skilled people to help integrate it into your business.
Python has become the language of choice for data science and machine learning, so it’s no surprise that data scientists and machine learning engineers have started using Python to build APIs. But what is the best way to build a data science API using Python?
There are a few options out there, but the most popular one at the moment is FastAPI. It’s easy to use, has good documentation, and is built in modern Python, which means you can use type hints and async/await.
But firstly, what is API ?
An Application Programming Interface (API) is a set of rules and guidelines provided by a software program for other software programs or web applications to communicate with it and access its features or data. It specifies how software components should interact and APIs are used when programming graphical user interface (GUI) components.
Building a data science application is hard but not impossible
When you’re starting, it can be especially daunting to build your first data science application. You can’t just use a simple model and load it into production. You need a platform that allows you to develop at scale, deploy models quickly and efficiently, and manage the components of your application in production.
FastAPI is an API framework for building APIs with Python 3.6+ based on standard Python type hints. It is fast and easy to use, and it is the standard library for deploying large data science applications quickly, which makes FastAPI perfect for building machine learning models that are deployed into production.
For building a data science model, a tool is required which can perform the following tasks:
- Data collection from multiple sources
- Data preprocessing
- Modeling
- Deployment
There are many tools which can be used for building an API for data science models, but FastAPI stands out because of its speed and simplicity
FASTAPI is an API framework that allows you to build robust data science code with Python. It’s specifically built to handle heavy lifting, and model building and validation, so you can easily reuse your model for multiple use cases and save your work for later on.
It was inspired by the performance of NodeJS and Go, and created for building high-performance applications.
Why use FastAPI?
There are many frameworks available to create an API, such as Flask, Django, and Tornado. However, the main challenge with these frameworks is that they are built for general purpose applications and therefore can be inefficient when it comes to building data science APIs.
FastAPI is a modern framework that takes advantage of new Python features such as type hints and async/await syntax in order to provide fast performance with impressive reliability.Some of the benefits of using FastAPI include:
- Automatic documentation and schema generation
- Built-in support for OpenAPI and JSON Schema standards (useful for testing and validation)
- Built-in dependency injection system
- Support for typing and asynchronous code (async/await)
- Faster than other frameworks such as Flask
Benefits of using FastAPI for Data Science projects:
- Very fast to code
- Easy to learn and use
- Open source and free to use
- High performance as it uses ASGI server under the hood (uvicorn) and uvicorn is one of the fastest ASGI servers out there.
The key features are:
-Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available.
-Fast to code: Increase the speed to develop features by about 200% to 300%.
-Fewer bugs: Reduce about 40% of human (developer) induced errors.
-Intuitive: Great editor support. Completion everywhere. Less time debugging.
-Dependency injection: Make writing tests easy. Ready for production, right first time.
-Almost automatic interactive documentation with JSON Schema and OpenAPI (Swagger). Easy integration with frontend apps and other backends.The most common use case for FastAPI is serving machine learning models. But FastAPI has a lot more to offer, including:
- Background tasks
- WebSockets
- GraphQL (with Graphene)
- Authentication and authorization
- Dependency injection with Pydantic and type annotations
The disadvantages of using FastAPI for data science
There are some other tools that were designed specifically for data science and machine learning. These are tools like Flask, Django, or HuggingFace’s Transformers. However, if you’re looking for a “batteries included” option that has been battle-tested in production environments, then FastAPI is one of the best options available.
Before we get started, you should have the following installed:
Prerequisites:
Python 3.7+ and Pip installed on your machine.
Basic knowledge of SQL databases and SQLAlchemy ORM.
PostgreSQL installed on your machine and running or a free Heroku account.
Lets see FastAPI implementation to build Data Science Applications :
#Installing libraries:
pip install fastapi
pip install uvicorn
#create main file in same directory where data science model is saved
# main.py
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
async def root():
return {"message": "Hello "}
#running below in CMD or Shell, Run the live server using Uvicorn:
$ uvicorn main:app --reload
Now open http://127.0.0.1:8000/docs in your browser.See detailed steps and examples at this GitHub Repo.
The core purpose of Data Science is to enable the users to take insights from the available data and draw a conclusion. Collecting various data sources and relevant information for the business, that would help in resolving the day-to-day problems-that’s what every organization wants.
FASTAPI is a great open source for data scientists to create apps and work with Big data, The community of FASTAPI had been creating a wide variety of applications in various fields ,Currently there are several more concrete applications based on FASTAPI under development in diverse fields like finance, music, education, environment etc. These applications are better than currently available commercial products in several ways. Hope you liked this article at MLDots.
