Mastering Deployment of AI Pipelines 

Objective

Build, containerize, and deploy the Jarvid API pipeline as a production-grade application. Ensure the API is scalable and documented with clear instructions for setup and deployment.

Step 1: Develop the Jarvid API

1.1 Application Structure

Directory Structure:

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jarvid-api/

├── app/

│   ├── main.py

├── requirements.txt

├── Dockerfile

├── serverless.yml

├── README.md

1.2 API Code

File: app/main.py

python

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from fastapi import FastAPI, HTTPException

app = FastAPI()

@app.get("/")

def read_root():

    return {"message": "Welcome to the Jarvid API"}

@app.post("/generate-embedding")

def generate_embedding(text: str):

    if not text:

        raise HTTPException(status_code=400, detail="Text input is required")

    # Placeholder for embedding logic

    return {"embedding": [0.1, 0.2, 0.3]}

@app.post("/search-similarity")

def search_similarity(embedding: list, top_k: int = 5):

    if not embedding:

        raise HTTPException(status_code=400, detail="Embedding input is required")

    # Placeholder for similarity search logic

    return {"results": [{"id": 1, "name": "Alice", "similarity": 0.95}]}

File: requirements.txt

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fastapi

uvicorn

Step 2: Dockerize the Application

2.1 Dockerfile

File: Dockerfile

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# Use an official Python runtime as the base image

FROM python:3.9-slim

# Set the working directory

WORKDIR /app

# Copy application files

COPY requirements.txt .

COPY app/ ./app

# Install dependencies

RUN pip install --no-cache-dir -r requirements.txt

# Expose the application port

EXPOSE 8000

# Define the entry point

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

2.2 Build and Test Docker Image

Build the Docker image:
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docker build -t jarvid-api .

Run the Docker container:
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docker run -p 8000:8000 jarvid-api

2. 
   

3. Test the API:

   • Access http://localhost:8000/docs for interactive API documentation.

   • Test endpoints with curl or Postman.

Step 3: Deploy to AWS Lambda

3.1 Configure Serverless Framework

File: serverless.yml

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service: jarvid-api

provider:

  name: aws

  runtime: python3.9

  region: us-east-1

functions:

  api:

    image:

      name: jarvid-api

      uri: <your_ecr_repository_uri>

3.2 Push Docker Image to AWS ECR

Tag the Docker image:
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docker tag jarvid-api:latest <your_ecr_repository_uri>:latest

Push the Docker image:
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aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <your_ecr_repository_uri>

docker push <your_ecr_repository_uri>:latest

3.3 Deploy the Application

Deploy with Serverless Framework:
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serverless deploy

1. 
   

2. Retrieve API Gateway URL:

   • Use the output from serverless deploy to find the API endpoint.

Step 4: Document the Deployment

File: README.md

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# Jarvid API Deployment Guide

## Overview

This guide describes the steps to build, containerize, and deploy the Jarvid API.

### API Endpoints

- `GET /`: Returns a welcome message.

- `POST /generate-embedding`: Generates an embedding for a given text.

- `POST /search-similarity`: Searches for similar items based on an embedding.

---

## Docker Setup

1. Build the Docker image:

   ```bash

   docker build -t jarvid-api .

Run the Docker container:
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docker run -p 8000:8000 jarvid-api

Deployment to AWS Lambda

Tag the Docker image:
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docker tag jarvid-api:latest <your_ecr_repository_uri>:latest

Push the image to ECR:
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aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <your_ecr_repository_uri>

docker push <your_ecr_repository_uri>:latest

Deploy using Serverless Framework:
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serverless deploy

3. 
   

4. Test the deployed API:

Root endpoint:
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curl <api_gateway_url>/

Health check endpoint:
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curl <api_gateway_url>/health

Monitoring and Logging

• Use AWS CloudWatch for monitoring and debugging.

View logs:
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serverless logs -f api

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---

### **Expected Outcomes**

1. **Functional API:**

   - The Jarvid API is containerized and includes endpoints for embedding generation and similarity search.

2. **Deployed and Scalable:**

   - The API is deployed to AWS Lambda using the Serverless Framework, ensuring scalability.

3. **Comprehensive Documentation:**

   - Clear setup and deployment instructions are included in the `README.md`.