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AI Core Concepts (Part 20): AI Infrastructure

AI Infrastructure refers to the backend systems, tools, and environments that support the development, deployment, and scaling of AI models. For software engineers, mastering AI infra means enabling reliable, efficient, and production-ready AI applications.

1. Why AI Infrastructure Matters

Need	Infrastructure Role
Model Training	Access to GPUs/TPUs, data pipelines
Model Serving	Fast, scalable inference APIs
Experimentation	Version control, reproducibility
Monitoring	Logging, performance metrics, alerts
Scalability	Deploying across distributed environments
Cost Optimization	Resource auto-scaling, serverless inference

2. Core Components of AI Infrastructure

1. Compute Resources

GPUs (NVIDIA A100, V100), TPUs (Google)
CPU for lightweight tasks
On-premise, cloud (AWS, GCP, Azure), or hybrid

# Example: Provisioning a GPU instance with AWS CLI
aws ec2 run-instances \
  --image-id ami-xyz \
  --instance-type g4dn.xlarge \
  --key-name your-key \
  --count 1

2. Data Infrastructure

Storage: S3, GCS, Azure Blob
Preprocessing: Spark, Dask, Pandas
ETL Pipelines: Airflow, Prefect, Dagster

# Example: Airflow DAG for data preprocessing
from airflow import DAG
from airflow.operators.python_operator import PythonOperator

def preprocess():
    # Load, clean, transform data
    ...

dag = DAG('data_pipeline', ...)
preprocess_task = PythonOperator(task_id='preprocess', python_callable=preprocess, dag=dag)

3. Model Training Infrastructure

Frameworks: PyTorch, TensorFlow, JAX
Distributed Training: Horovod, PyTorch DDP
Resource Management: Kubernetes, Ray, Slurm

# Example: PyTorch DDP (Distributed Data Parallel)
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

dist.init_process_group("nccl", rank=rank, world_size=world_size)
model = DDP(model.to(device))

4. Model Deployment and Serving

REST/gRPC APIs: FastAPI, Flask, Triton Inference Server
Model Servers: TensorFlow Serving, TorchServe, BentoML, MLflow
Containerization: Docker, Kubernetes

# Example: Serving with FastAPI
from fastapi import FastAPI
import joblib

app = FastAPI()
model = joblib.load("model.pkl")

@app.get("/predict")
def predict(x: float):
    return {"prediction": model.predict([[x]])[0]}

5. Monitoring & Logging

Tools: Prometheus, Grafana, Loki, Sentry
Model Monitoring: Fiddler, WhyLabs, Arize, Evidently
Track drift, latency, failed requests

# Example: Logging inference latency
import time
start = time.time()
prediction = model.predict(input_data)
duration = time.time() - start
logger.info(f"Inference took {duration:.2f} seconds")

6. Experiment Tracking

Tools: MLflow, Weights & Biases, Neptune
Track: metrics, hyperparameters, model artifacts, code versions

# Example: MLflow experiment tracking
import mlflow

with mlflow.start_run():
    mlflow.log_param("lr", 0.001)
    mlflow.log_metric("accuracy", 0.92)
    mlflow.sklearn.log_model(model, "model")

7. Model Versioning

DVC, MLflow Models, Hugging Face Model Hub
Maintain reproducible snapshots of models + data

# Example: DVC model tracking
dvc add models/model.pkl
git add models/model.pkl.dvc
git commit -m "Track model with DVC"

3. Scalable Inference Patterns

Pattern	Description
Batch Inference	Run predictions on many records at once
Online Inference	Serve individual predictions via API
Serverless Inference	Scales to zero, cheaper
Streaming	Predict from live data (Kafka, Flink)

4. Security and Compliance

Data encryption (in transit + at rest)
Access control (IAM, service accounts)
Model explainability (for regulated domains)
Audit logging

5. Tools by Category

Category	Tools
Training	PyTorch, TensorFlow, JAX, Hugging Face
Deployment	BentoML, MLflow, TorchServe, SageMaker
Orchestration	Airflow, KubeFlow, Prefect
Monitoring	Prometheus, Grafana, Arize, WhyLabs
Experiment Tracking	Weights & Biases, Neptune, MLflow
Vector DBs	FAISS, Pinecone, Weaviate, Qdrant

6. Infrastructure-as-Code Example (Terraform for GCP AI Platform)

resource "google_ai_platform_model" "example" {
  name        = "my_model"
  regions     = ["us-central1"]
  labels      = { team = "ai" }
}

📚 Further Resources

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