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Deploying AI-Powered Product Recommendations for E-commerce

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Overview

Creating and deploying recommendation systems for e-commerce platforms involves handling large datasets, feature engineering, model selection, and optimization. Delivering accurate, scalable, and performant solutions in production is critical.

Plexe simplifies deployment, enabling tuning and customization while leveraging advanced model-building capabilities. It adapts to workflows from raw data to quick prototypes.

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Quick Start

Install the Plexe library:

pip install plexe

Here’s a basic example to get started:

import plexe

model_version = plexe.build(
    goal="""Develop a product recommendation system for e-commerce to suggest complementary products based on browsing, purchase history, and cart data.""",
    model_name="ecommerce-recsys",
    data_files=["product_data.csv", "user_behavior.json"]
)

status = plexe.get_status(
    model_name="ecommerce-recsys", 
    model_version=model_version
)

recommendations = plexe.infer(
    model_name="ecommerce-recsys",
    model_version=model_version,
    input_data={
        "user_id": "12345",
        "current_cart": ["product_001", "product_005"],
        "context": "holiday season"
    }
)

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Authentication

Set up your API key using one of these methods:

  1. Environment variable:
export PLEXE_API_KEY=your-api-key
  1. Direct client initialization:
from plexe import PlexeAI
client = PlexeAI(api_key="your-api-key")

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Fine-Tuning and Customization

Update your model with new data and custom parameters:

from plexe import PlexeAI

client = PlexeAI(api_key="your-api-key")

upload_id = client.upload_files([
    "updated_product_data.csv",
    "new_user_activity.json"
])

new_model_version = client.build(
    goal="Fine-tune the recommendation system with updated product and user activity data.",
    model_name="ecommerce-recsys",
    upload_id=upload_id
)

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Batch Processing

Process multiple recommendations efficiently:

recommendations = plexe.batch_infer(
    model_name="ecommerce-recsys",
    model_version=model_version,
    inputs=[
        {
            "user_id": "12345",
            "current_cart": ["product_001", "product_005"],
            "context": "holiday season"
        },
        {
            "user_id": "67890",
            "current_cart": ["product_010"],
            "context": "back-to-school"
        }
    ]
)

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Production Implementation

Deploy with FastAPI for production-ready async support:

import asyncio
from fastapi import FastAPI
from plexe import PlexeAI

app = FastAPI()
client = PlexeAI(api_key="your-api-key")

@app.post("/recommendations")
async def get_recommendations(data: dict):
    recommendations = await client.ainfer(
        model_name="ecommerce-recsys",
        model_version="your_model_version",
        input_data=data
    )
    return recommendations

@app.on_event("shutdown")
async def shutdown_event():
    await client.async_client.aclose()

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Key Features

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Data Processing

  • Automated preprocessing (categorical encoding, normalization)
  • Support for diverse data formats (JSON, CSV)
  • Adaptive learning based on customer behavior

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Model Capabilities

  • Natural language instruction-based model building
  • Fine-tuning with custom datasets
  • Contextual awareness (events, seasonality)
  • Domain-specific optimization

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Production Features

  • Asynchronous APIs for high-volume inference
  • Batch processing capabilities
  • Scalable to millions of requests per day
  • Real-time e-commerce platform integration

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Getting Started Guide

  1. Install Plexe

    • Use pip to install the library
    • Set up authentication

  2. Prepare Your Data

    • Format your product data
    • Collect user behavior information
    • Organize contextual data

  3. Train and Deploy

    • Upload your datasets
    • Define your model goals
    • Test and iterate on results

  4. Monitor and Optimize

    • Track model performance
    • Update with new data
    • Fine-tune based on results
Hotels Demand Forecast