This content originally appeared on DEV Community and was authored by mark mwendia
Introduction:
FastAPI is a modern web framework for building APIs in Python, known for its speed and ease of use. In this article, you will learn how to implement a machine learning model and expose it as an API using FastAPI.
Key Sections:
Setting Up FastAPI: This section provides a brief introduction to FastAPI and its advantages for ML-based APIs. It also shows how to install and set up FastAPI.
Code Example: Basic FastAPI Application
from fastapi import FastAPI
app = FastAPI()
@app("/")
def read_root():
return {"Hello": "World"}
Training a Machine Learning Model: This section explains how to use a simple machine learning model (e.g., a scikit-learn regression model) and describes the training process.
Code Example: Training an ML Model
from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
model = LinearRegression().fit(X, y)
Exposing the Model as an API: This section demonstrates how to use FastAPI to create an endpoint that accepts input data and returns predictions.
Code Example: API Endpoint for Model Prediction
from pydantic import BaseModel
class PredictionInput(BaseModel):
x1: float
x2: float
@app("/predict")
def predict(input: PredictionInput):
prediction = model.predict([[input.x1, input.x2]])
return {"prediction": prediction[0]}
Conclusion: This section recaps how easy it is to integrate machine learning models into web services using FastAPI.Introduction:
FastAPI is a cutting-edge web framework designed for creating APIs in Python, recognized for its exceptional speed and user-friendly nature. In this guide, we will delve into the process of implementing a machine learning model and exposing it as an API using FastAPI.
Key Sections:
Setting Up FastAPI: This section offers an overview of FastAPI and its benefits for ML-based APIs, while also providing a step-by-step guide on installing and configuring FastAPI.
Code Example: Basic FastAPI Application
from fastapi import FastAPI
app = FastAPI()
@username_3("/")
def read_root():
return {"Hello": "World"}
Training a Machine Learning Model: Here, we will explore the process of utilizing a simple machine learning model, such as a scikit-learn regression model, and explain the training process in detail.
Code Example: Training an ML Model
from sklearn.linear_model import LinearRegression
import numpy as np
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
model = LinearRegression().fit(X, y)
Exposing the Model as an API: This section will illustrate how to utilize FastAPI to create an endpoint that can accept input data and provide predictions in return.
Code Example: API Endpoint for Model Prediction
from pydantic import BaseModel
class PredictionInput(BaseModel):
x1: float
x2: float
@username_3("/predict")
def predict(input: PredictionInput):
prediction = model.predict([[input.x1, input.x2]])
return {"prediction": prediction[0]}
Conclusion: In this section, we will summarize the seamless process of integrating machine learning models into web services using FastAPI.
This content originally appeared on DEV Community and was authored by mark mwendia
mark mwendia | Sciencx (2024-09-25T20:38:08+00:00) Implementing Machine Learning APIs with Python and FastAPI. Retrieved from https://www.scien.cx/2024/09/25/implementing-machine-learning-apis-with-python-and-fastapi/
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