MLOps Portfolio Ace

Showcasing End-to-End Machine Learning Operations Expertise

Problem Statement Rewriter

Refine your ideas into succinct, focused narratives using AI. Provide context from past projects and company goals for better results.

Technical Skills

Proficiency levels in key MLOps technologies and frameworks.

End-to-End MLOps Pipeline

A comprehensive overview of the full model lifecycle, from development to production monitoring.

Data Ingestion

Collecting and storing raw data.

Data Validation

Ensuring data quality and integrity.

Model Training

Developing models on prepared data.

Model Evaluation

Assessing model performance with metrics.

Containerization

Packaging models into Docker containers.

CI/CD Automation

Automating the build and test pipeline.

Cloud Deployment

Deploying models to scalable infrastructure.

Monitoring

Tracking model performance in production.

ML Model Hosting via API

Serving pre-trained models through a robust FastAPI endpoint for seamless integration into applications.

This example demonstrates how a scikit-learn model, saved as a .pkl file, can be loaded and served using a FastAPI application. This method creates a lightweight, high-performance microservice.

The API exposes a /predictendpoint that accepts feature inputs in a JSON format and returns the model's prediction. Pydantic is used for data validation, ensuring that the input conforms to the expected schema.

This approach decouples the model from the application logic, allowing for independent updates and scaling.

from fastapi import FastAPI
from pydantic import BaseModel
import joblib

# Load the pre-trained model from a .pkl file
model = joblib.load('sklearn_model.pkl')

# Initialize FastAPI app
app = FastAPI(title="ML Model API")

# Define the input data schema using Pydantic
class ModelInput(BaseModel):
    feature1: float
    feature2: float
    feature3: float

# Define the prediction endpoint
@app.post("/predict")
def predict(data: ModelInput):
    """
    Takes feature inputs and returns a model prediction.
    """
    features = [[data.feature1, data.feature2, data.feature3]]
    prediction = model.predict(features)
    return {"prediction": prediction.tolist()[0]}

@app.get("/")
def read_root():
    return {"message": "ML Model API is running."}