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Development & Deployment
Build, deploy, and scale AI applications with modern frameworks and cloud platforms
🦜
LangChain
Comprehensive framework for building applications with large language models,
providing tools for chaining, memory, and agent creation.
Key Features:
- LLM abstraction and chaining
- Document loaders and text splitters
- Vector store integrations
- Agent and tool ecosystems
- Memory management
⚡
FastAPI
Modern, fast web framework for building APIs with Python, ideal for
creating high-performance AI application backends.
Key Features:
- Automatic API documentation
- Type hints and validation
- Async/await support
- High performance
- Easy deployment
🤗
Hugging Face
Platform and library ecosystem for machine learning models, datasets,
and deployment tools with extensive pre-trained model hub.
Key Features:
- Pre-trained model hub
- Transformers library
- Dataset management
- Model hosting and inference
- Gradio for demos
📊
Streamlit
Rapid prototyping framework for creating interactive web applications
and dashboards for machine learning projects.
Key Features:
- Pure Python development
- Interactive widgets
- Real-time updates
- Easy sharing and deployment
- Rich visualization support
🐳
Docker
Containerization platform for packaging AI applications with all
dependencies for consistent deployment across environments.
Key Features:
- Environment consistency
- Scalable deployments
- Isolation and security
- Easy orchestration
- Resource efficiency
☸️
Kubernetes
Container orchestration platform for managing and scaling AI applications
in production environments with automated deployment and healing.
Key Features:
- Auto-scaling and load balancing
- Service discovery
- Rolling updates
- Self-healing systems
- Resource management
AI Application Deployment Pipeline
Development
Build and test your AI application locally with proper version control and documentation
Containerization
Package application with Docker for consistent deployment across environments
Testing
Implement automated testing including unit tests, integration tests, and model validation
CI/CD
Set up continuous integration and deployment pipelines with automated testing and deployment
Cloud Deployment
Deploy to cloud platforms with scalability, monitoring, and load balancing
Monitoring
Implement logging, metrics, and alerting for performance and reliability monitoring
Cloud Platforms
AWS
Amazon Web Services offers comprehensive AI/ML services including SageMaker,
Bedrock, and extensive compute resources for training and inference.
Key Services: SageMaker, EC2, Lambda, Bedrock, ECS/EKS
Google Cloud
Google Cloud Platform provides Vertex AI, AutoML, and powerful TPU infrastructure
for machine learning workloads.
Key Services: Vertex AI, Compute Engine, Cloud Run, GKE, AutoML
Microsoft Azure
Azure Machine Learning studio and cognitive services provide comprehensive
tools for AI development and deployment.
Key Services: Azure ML, Cognitive Services, Container Instances, AKS
Hugging Face Spaces
Easy deployment platform for ML demos and applications with integrated
model hub and community features.
Features: Free hosting, GPU support, Gradio/Streamlit integration
Railway
Simple deployment platform with automatic builds from Git repositories,
ideal for rapid prototyping and small applications.
Features: Git-based deployment, automatic scaling, database hosting
Vercel
Frontend-focused platform excellent for deploying AI-powered web applications
with serverless functions.
Features: Edge functions, automatic HTTPS, global CDN
Code Examples
FastAPI AI Application
from fastapi import FastAPI
from pydantic import BaseModel
from transformers import pipeline
import uvicorn
app = FastAPI(title="AI Text Classifier API")
# Load model at startup
classifier = pipeline("sentiment-analysis",
model="cardiffnlp/twitter-roberta-base-sentiment-latest")
class TextInput(BaseModel):
text: str
class PredictionOutput(BaseModel):
label: str
confidence: float
@app.post("/predict", response_model=PredictionOutput)
async def predict_sentiment(input_data: TextInput):
result = classifier(input_data.text)[0]
return PredictionOutput(
label=result['label'],
confidence=result['score']
)
@app.get("/health")
async def health_check():
return {"status": "healthy"}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)
Docker Configuration
# Dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
# docker-compose.yml
version: '3.8'
services:
ai-api:
build: .
ports:
- "8000:8000"
environment:
- MODEL_NAME=cardiffnlp/twitter-roberta-base-sentiment-latest
volumes:
- ./models:/app/models
restart: unless-stopped
Kubernetes Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: ai-api-deployment
spec:
replicas: 3
selector:
matchLabels:
app: ai-api
template:
metadata:
labels:
app: ai-api
spec:
containers:
- name: ai-api
image: your-registry/ai-api:latest
ports:
- containerPort: 8000
resources:
requests:
memory: "1Gi"
cpu: "500m"
limits:
memory: "2Gi"
cpu: "1000m"
env:
- name: MODEL_NAME
value: "cardiffnlp/twitter-roberta-base-sentiment-latest"
---
apiVersion: v1
kind: Service
metadata:
name: ai-api-service
spec:
selector:
app: ai-api
ports:
- port: 80
targetPort: 8000
type: LoadBalancer
Best Practices
Security
Implement proper authentication, input validation, and secure model serving
to protect against attacks and data breaches.
Scalability
Design applications to handle varying loads with auto-scaling,
load balancing, and efficient resource utilization.
Performance
Optimize inference speed through model optimization, caching,
batching, and efficient data processing.
Monitoring
Implement comprehensive logging, metrics collection, and alerting
to ensure reliability and performance.
CI/CD
Automate testing, building, and deployment processes to ensure
consistent and reliable releases.
Cost Optimization
Monitor and optimize cloud costs through resource scheduling,
spot instances, and efficient architecture.