This tutorial walks you through the essential steps to create and use a machine learning model with the Plexe Platform API.

Base URL: https://api.plexe.ai

​
Prerequisites

  1. Account: Sign up at console.plexe.ai
  2. API Key: Generate one from your account settings
  3. HTTP Client: Such as curl, Postman, or Python with requests

​
1. Authentication

All API requests require your API key in the x-api-key header:

import requests, os, time, json

# Load API key from environment
api_key = os.getenv("PLEXE_API_KEY")
if not api_key:
    raise ValueError("Please set the PLEXE_API_KEY environment variable.")

headers = {
    "x-api-key": api_key,
    "Content-Type": "application/json"
}
base_url = "https://api.plexe.ai"

​
2. Upload Data

While optional, providing data yields better results. Use a pre-signed URL approach:

# Request pre-signed URL
file_path = "housing_data.csv"
file_name = os.path.basename(file_path)

try:
    # Get upload URL
    response = requests.post(
        f"{base_url}/uploads",
        headers=headers,
        json={"filename": file_name, "content_type": "text/csv"}
    )
    response.raise_for_status()
    upload_info = response.json()
    presigned_url = upload_info.get("presigned_url")
    temp_upload_id = upload_info.get("upload_id")
    s3_key = upload_info.get("key")
    
    # Upload file to S3
    with open(file_path, 'rb') as f:
        requests.put(presigned_url, data=f, headers={'Content-Type': 'text/csv'}).raise_for_status()
    
    # Confirm upload completion
    confirm_response = requests.post(
        f"{base_url}/uploads/status",
        headers=headers,
        json={"upload_id": temp_upload_id, "filename": file_name, "s3_key": s3_key}
    )
    confirm_response.raise_for_status()
    upload_id = confirm_response.json().get("upload_id")
    print(f"Upload confirmed. ID: {upload_id}")

except Exception as e:
    print(f"Upload error: {e}")
    # Fallback to public dataset
    upload_id = "https://raw.githubusercontent.com/plotly/datasets/master/housing_new-york.csv"

The two-step upload process (pre-signed URL → direct upload) enables secure and efficient handling of large files.

​
3. Build the Model

Submit a build request with your model name, goal, and data reference:

model_name = "api-quickstart-housing"
response = requests.post(
    f"{base_url}/models/{model_name}",
    headers=headers,
    json={
        "goal": "Predict house prices in USD based on sqft, beds, baths.",
        "upload_id": upload_id,
        "metric": "rmse"  # Optional: suggest optimization metric
    }
)
response.raise_for_status()
model_id = response.json().get("model_id")
print(f"Build requested. Model ID: {model_id}")

​
4. Monitor Build Status

Model building happens asynchronously. Poll until complete:

if model_id:
    # Extract name and version from model_id (format: name:version)
    m_name, m_version = model_id.split(':')
    status = "pending"
    
    # Poll until completed or failed
    while status in ["pending", "processing", "building"]:
        time.sleep(15)  # Wait between checks
        
        try:
            response = requests.get(
                f"{base_url}/models/{m_name}/{m_version}/status", 
                headers=headers
            )
            response.raise_for_status()
            status_result = response.json()
            status = status_result.get("status")
            print(f"Status: {status}")
            
            if status == "completed":
                print("Build successful!")
                break
            elif status == "failed":
                print(f"Build failed: {status_result.get('error', 'Unknown error')}")
                model_id = None
                break
                
        except Exception as e:
            print(f"Status check error: {e}")
            time.sleep(30)  # Longer wait on error

​
5. Make Predictions

Once the model is ready, use the inference endpoint:

if model_id and status == "completed":
    try:
        # Prepare sample input matching your data schema
        prediction = requests.post(
            f"{base_url}/models/{m_name}/{m_version}/infer",
            headers=headers,
            json={
                "sqft": 1950.0,
                "beds": 3,
                "baths": 2.5
            }
        ).json()
        
        print(f"Prediction result: {prediction}")
        
    except Exception as e:
        print(f"Inference error: {e}")

This completes the basic API workflow. Explore the Platform API Reference for details on all available endpoints and parameters.