Introduction of AWS Managed AI Services

• Introduction of AWS Managed AI Services
  • Why Use AWS AI Managed Services?
  • Examples of AWS AI Managed Services

Why Use AWS AI Managed Services?

• AWS Managed AI Services are pre-trained machine learning services designed to solve specific use cases without requiring you to build or train models from scratch.
• Using managed services helps you focus on your application logic rather than infrastructure, training, or scaling ML workloads.
• You can run machine learning manually on your own servers, but AWS managed services provide major advantages.
• Responsiveness & High Availability
  • Services are available across multiple AWS Regions.
  • Built to be highly responsive even under heavy load.
• Redundancy & Reliability
  • Deployed across multiple Availability Zones (AZs).
  • Keeps running even if one AZ experiences a failure.
• High Performance
  • Backed by optimized CPUs, GPUs, and accelerators.
  • Provides better performance and lower cost vs. building everything manually.
• Token-Based Pricing
  • You pay only for what you use (per request or per token).
  • No need to over-provision or operate servers.

Examples of AWS AI Managed Services

• Generative AI
  • Amazon Bedrock – Foundation models and GenAI apps
  • Amazon Q Business / Q Developer – Higher-level GenAI assistants
• Text & Document Processing
  • Amazon Comprehend – NLP, sentiment, key phrases
  • Amazon Translate – Language translation
  • Amazon Textract – Document OCR and extraction
• Vision
  • Amazon Rekognition – Image and video analysis
• Search
  • Amazon Kendra – Intelligent enterprise search
• Chatbots
  • Amazon Lex – Conversational interfaces
• Speech
  • Amazon Polly – Text to speech
  • Amazon Transcribe – Speech to text
• Recommendations
  • Amazon Personalize – Real-time recommendations
• Full ML Platform
  • Amazon SageMaker – Build, train, tune, deploy, and monitor ML models