Sample Tech Stack 1: For a Large-Scale NLP Application with Knowledge Graph Integration on AWS
Context Representation & Storage
- Knowledge Graph: Amazon Neptune (fully managed graph database service).
- Vector Embeddings:
- Consider Amazon SageMaker Feature Store for storing and serving embeddings.
- Use open-source libraries like FAISS or Annoy hosted on Amazon EC2 or within Amazon SageMaker Studio. For managed vector search, explore Amazon Kendra (intelligent search service) or build a custom solution with Amazon OpenSearch Service.
- Document Storage: Amazon DynamoDB (NoSQL key-value and document database) or Amazon S3 (object storage) with Amazon Kendra for intelligent search.
- Relational Database: Amazon RDS (managed MySQL, PostgreSQL, SQL Server, etc.).
Context Acquisition & Processing
- NLP Libraries: Python on Amazon EC2, AWS Lambda (serverless), or within Amazon SageMaker Studio using libraries like spaCy, NLTK, Hugging Face Transformers. Consider Amazon Comprehend for pre-trained NLP models.
- API Integration: Python’s `requests` on AWS compute services or using AWS Lambda for event-driven integrations. Consider Amazon API Gateway for managing and securing APIs.
- Stream Processing: Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, Amazon Managed Streaming for Apache Kafka (MSK).
Memory & Caching
- In-Memory Cache: Amazon ElastiCache (Redis and Memcached).
Infrastructure
Programming Languages
- Python (primary for ML/NLP), Java, Go, etc.
Machine Learning Frameworks
- Amazon SageMaker (TensorFlow, PyTorch, scikit-learn).
Sample Tech Stack 2: For a Robotics Application Focusing on Environmental Context on AWS
Context Representation & Storage
- Spatial Data: Amazon RDS for PostgreSQL with PostGIS extension, Amazon DynamoDB (for flexible schema), Amazon S3 for large point cloud datasets.
- Object Databases: Custom schemas within Amazon DynamoDB or Amazon RDS.
- Sensor Data Storage: Amazon Timestream (fast, scalable, fully managed time series database), Amazon DynamoDB.
Context Acquisition & Processing
- Sensor Data Ingestion: AWS IoT Core (managed service for IoT devices), custom ingestion pipelines with AWS Lambda or Amazon Kinesis Data Streams.
- Computer Vision: Run OpenCV, PyTorch Vision, TensorFlow Vision on Amazon EC2 or use Amazon SageMaker Ground Truth for data labeling and Amazon SageMaker for model training and deployment. Consider Amazon Rekognition for pre-trained vision models.
- Sensor Fusion: Implement custom fusion logic within Amazon EC2 or containerized applications on Amazon ECS or Amazon EKS.
Memory & State Management
- Amazon ElastiCache (Redis) for low-latency state access.
- Amazon DynamoDB or Amazon RDS for persistent state.
Infrastructure
- Amazon Web Services (AWS)
- Amazon Elastic Compute Cloud (EC2)
- Amazon Elastic Container Service (ECS), Amazon Elastic Kubernetes Service (EKS)
- AWS IoT Core.
- AWS RoboMaker (robotics development and simulation service).
Programming Languages
- Python, C++, Go.
Machine Learning Frameworks
Sample Tech Stack 3: For a Dialogue System with Personalized Context on AWS
Context Representation & Storage
- User Profiles: Amazon DynamoDB or Amazon RDS.
- Dialogue State Tracking: Application logic on Amazon EC2, AWS Lambda, or Amazon ECS/ Amazon EKS with Amazon ElastiCache (Redis) for caching.
- Conversation History: Amazon DynamoDB or Amazon S3.
Context Acquisition & Processing
- NLP Libraries: Python on AWS compute with Rasa (can be deployed on ECS/EKS), spaCy, NLTK. Consider Amazon Lex for conversational interfaces.
- STT & TTS: Amazon Transcribe, Amazon Polly.
- User Authentication: Amazon Cognito.
Memory & Caching
- Amazon ElastiCache (Redis).
Infrastructure
Programming Languages
- Python (primary for NLP), Node.js, Go.
Machine Learning Frameworks
