FastAPI is an innovative web framework in Python that presents immense applicability in the deployment of machine learning models. This report delves into insights on how FastAPI can be utilized for machine learning, outlining its features and integration with machine learning models. By providing an in-depth exploration, the document aims to serve as a guiding manual to utilize FastAPI for integrating machine learning.
Introduction
Success in the deployment of machine learning models rests fundamentally on the suitability of the chosen framework. As the conduits between models and their users, frameworks should ideally be scalable, secure, and efficient, providing seamless service while ensuring optimal utilization of the model. FastAPI warrants special mention as a highly efficient Python-based web framework. It is fundamentally superior in terms of performance, typified by shorter latency and higher throughput, compared to Flask and Django, other renowned Python frameworks. The web framework’s higher performance can be attributed to its asynchronous capabilities and the Python 3.7 await and async features, which inform its built (BetterDataScience, 2021; GeeksforGeeks, 2021; Towards Data Science, 2021).
Understanding FastAPI
FastAPI is a cutting-edge, speedy-to-write Python web framework used to build APIs with Python 3.6+ types hints. Its key features include automatic interactive API documentation, easy testing with the FastAPI test client, and straightforward exception handling. Its compatibility with modern Python versions, enhanced support for async and parallel computing, and Python 3.7’s async and await features sets it apart from other frameworks like Flask or Django (Towards Data Science, 2021;Dev.to, 2021).
FastAPI in Machine Learning
The web framework stands out for its consistent high performance and it’s comparably as fast as NodeJS and Go, making it ideally suited to handle high computational requirements typically associated with machine learning tasks. Another advantage of FastAPI is it’s supported by Python’s wide array of libraries, facilitating its integration with machine learning models (Towards Data Science, 2021). Furthermore, FastAPI is used by renowned companies like Netflix and Uber for their APIs (GeeksforGeeks, 2021).
For instance, integrating FastAPI with machine learning models is relatively simple, as demonstrated in various tutorials. Typically, the process begins with creating a new project or directory, followed by installing FastAPI and uvicorn, which acts as an ASGI server to run your application. Once installed, you then proceed to developing a basic API that returns a simple message. This API can be run using a simple command on the terminal (GeeksforGeeks, 2021). Moreover, transitioning from the initial API, the next step involves training a machine learning model; in this case, it is commonly the Iris dataset for instance. The model is then integrated into the API to expose its predictive functionality (BetterDataScience, 2021).
Deploying Machine Learning Models with FastAPI
FastAPI enables the integration of machine learning models as REST APIs. A common use-case involves deploying a spam detection model by building an API with FastAPI and running it in a Docker container. However, this tutorial doesn’t provide specific implementation details, instead of providing general details about using FastAPI as a tool to deploy machine learning models (Towards Data Science, 2021).
In a tutorial by GeeksforGeeks (2021), they employ the GaussianNB model and the Iris dataset, offering a more detailed guide to deploying a machine learning model using FastAPI. The process includes defining the data format for making predictions via the API.
Pydantic modules help to define the data format sent to the API. Following this, an endpoint is added to the API that predicts the class of the provided data and returns it as a response. The machine learning model is therefore successfully deployed as an API using FastAPI.
For large language models, FastAPI, AWS Lambda, and AWS CDK are used for deploying the model. This method is particularly useful as the model could be scaled according to the workload, and one only pays for the compute time they consume. The model could even adjust to continuous loads, which is critical when scaling machine learning models (AWS, 2021).
Furthermore, building a machine learning microservice with FastAPI is another use-case. The process generally involves packaging the model as a docker container, which is particularly easy as FastAPI can be quickly deployed using Docker (Nvidia, 2021).
Comparative Analysis between FastAPI and Other Frameworks
Comparatively, FastApi outperforms other popular Python frameworks such as Django and Flask. This is primarily due to its focus on performance, scalability, development speed, testing, easy exception handling, and simple deployment, which makes it an ideal choice for machine learning applications (Dev.to, 2021).
A comparison between FastAPI and other frameworks such as Flask demonstrated that FastAPI provides a concise and efficient development experience where fewer lines of code are required to create a basic API. FastAPI also offers an integrated testing feature, making it easy for Test Driven Development (TDD) practices (Dev.to, 2021).
In another comparative analysis between FastAPI and BentoML for machine learning model deployment, FastAPI was found to be a favorite among developers primarily because of its role in building modern web applications. It has become one of the most loved web frameworks, surpassing competitors such as Django and Flask according to the Stack Overflow 2022 Developer Survey (TowardsAI, 2021).
Conclusion
FastAPI provides a modern, fast, and well-documented solution for accessing machine learning models through REST APIs. Its high performance, easy testing, and straightforward exception handling make it an ideal choice for deploying machine learning models. Its simplicity and efficiency clearly represent its potential to become a key player in the deployment of machine learning models as APIs in the near future.
References
BetterDataScience. (2021). Deploy a Machine Learning Model with FastAPI. Available at: https://betterdatascience.com/deploy-a-machine-learning-model-with-fastapi/
GeeksforGeeks. (2021). Deploying ML Models as API using FastAPI. Available at: https://www.geeksforgeeks.org/deploying-ml-models-as-api-using-fastapi/
Towards Data Science. (2021a). How you can quickly deploy your ML models with FastAPI. Available at: https://towardsdatascience.com/how-you-can-quickly-deploy-your-ml-models-with-fastapi-9428085a87bf
Towards Data Science. (2021b). How to Deploy a Machine Learning Model With FastApi, Docker, and GitHub Actions. Available at: https://towardsdatascience.com/how-to-deploy-a-machine-learning-model-with-fastapi-docker-and-github-actions-13374cbd638a
Medium. (2021). Why FastAPI. Available at: https://medium.com/featurepreneur/why-fastapi-69fb172756b7
Dev.to. (2021). FastAPI: The Good, The Bad and The Ugly. Available at: https://dev.to/fuadrafid/fastapi-the-good-the-bad-and-the-ugly-20ob
Towards Data Science. (2021c). How to Build and Deploy a Machine Learning Model with FastAPI. Available at: https://towardsdatascience.com/how-to-build-and-deploy-a-machine-learning-model-with-fastapi-64c505213857
AWS. (2021). Deploy a serverless ML inference endpoint. Available at: https://aws.amazon.com/blogs/machine-learning/deploy-a-serverless-ml-inference-endpoint-of-large-language-models-using-fastapi-aws-lambda-and-aws-cdk/
Nvidia. (2021). Building a Machine Learning Microservice with FastAPI. Available at: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/