AWS re:Invent 2024 Summary - Major Enhancements to Generative AI Features

AWS re:Invent 2024 Overview

AWS re:Invent 2024, held December 2-6, 2024, saw numerous new services and features announced, centered on generative AI.

Reference: AWS re:Invent 2024

Amazon Nova - New Foundation Models

Nova Model Family

AWS has introduced its own developed foundation models.

Usage Example

import boto3

bedrock = boto3.client('bedrock-runtime')

response = bedrock.invoke_model(
    modelId='amazon.nova-pro-v1:0',
    body={
        "messages": [
            {"role": "user", "content": "Tell me about AWS best practices"}
        ],
        "max_tokens": 1024
    }
)

Reference: Amazon Nova

Amazon Nova Canvas & Reel

Nova Canvas (Image Generation)

response = bedrock.invoke_model(
    modelId='amazon.nova-canvas-v1:0',
    body={
        "taskType": "TEXT_IMAGE",
        "textToImageParams": {
            "text": "Futuristic office building exterior at dusk"
        },
        "imageGenerationConfig": {
            "width": 1024,
            "height": 1024,
            "quality": "premium"
        }
    }
)

Nova Reel (Video Generation)

response = bedrock.invoke_model(
    modelId='amazon.nova-reel-v1:0',
    body={
        "taskType": "TEXT_VIDEO",
        "textToVideoParams": {
            "text": "Silhouette of a person walking on the beach, sunset"
        },
        "videoGenerationConfig": {
            "durationSeconds": 6,
            "fps": 24
        }
    }
)

Amazon Q Developer Enhancements

Agent Features

# Autonomous task execution with Amazon Q Developer
@workspace Add unit tests to this feature

@workspace Scan for and fix security vulnerabilities

@workspace Generate API documentation

New Features

Reference: Amazon Q Developer

SageMaker HyperPod

Overview

New infrastructure that streamlines large-scale model training.

# HyperPod Cluster Creation
import boto3

sagemaker = boto3.client('sagemaker')

response = sagemaker.create_cluster(
    ClusterName='my-hyperpod-cluster',
    InstanceGroups=[
        {
            'InstanceGroupName': 'training-nodes',
            'InstanceType': 'ml.p5.48xlarge',
            'InstanceCount': 64,
            'LifeCycleConfig': {
                'OnCreate': 's3://my-bucket/setup.sh'
            }
        }
    ]
)

Automatic Failure Recovery

# Automatic response to failures
- Node failure detection: Automatic
- Checkpoint restoration: Automatic
- Node replacement: Automatic
- Training resumption: Automatic

Reference: SageMaker HyperPod

Aurora DSQL

Serverless Distributed SQL

A PostgreSQL-compatible distributed database has arrived.

-- Automatic synchronization across multi-region
CREATE TABLE orders (
    id UUID PRIMARY KEY,
    customer_id UUID,
    total DECIMAL(10,2),
    created_at TIMESTAMP
);

-- Scales while maintaining strong consistency
SELECT * FROM orders WHERE customer_id = ?;

Features

Reference: Amazon Aurora DSQL

S3 Tables

Managed Apache Iceberg

import boto3

s3tables = boto3.client('s3tables')

# Create table
response = s3tables.create_table(
    tableBucketARN='arn:aws:s3tables:us-east-1:123456789:bucket/my-bucket',
    namespace='analytics',
    name='events',
    format='ICEBERG'
)

# Data analysis (from Athena)
# SELECT * FROM s3tables.analytics.events
# WHERE event_date >= '2024-01-01'

Performance

• Query speed: Up to 3x faster
• Storage: Up to 10x compression
• Automatic compaction supported

Lambda SnapStart for Python/.NET

Python Support

# Lambda function
import json
from my_heavy_module import initialize_model

# Execute during SnapShot time, not cold start
model = initialize_model()

def handler(event, context):
    return {
        'statusCode': 200,
        'body': json.dumps(model.predict(event['input']))
    }

Impact

Reference: Lambda SnapStart

Other Notable Announcements

Trainium2

• Next-generation ML chip
• 4x performance
• 2x energy efficiency

Graviton4

• Latest Arm processor
• 30% performance improvement
• 40% power efficiency improvement

EKS Auto Mode

# Auto scaling with EKS Auto Mode
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 3  # Automatically adjusted
  selector:
    matchLabels:
      app: my-app

Summary

AWS re:Invent 2024 made clear AWS’s massive investment in generative AI.

• Amazon Nova: AWS’s own foundation models
• Amazon Q Enhancements: Improved developer productivity
• Aurora DSQL: A new era of distributed SQL
• Infrastructure Evolution: Trainium2, Graviton4

These new services make AI/ML development on AWS more efficient.