AI/ML & Intelligent Services Platform

What it is

An intelligent services platform that transforms data into actionable intelligence through ML model lifecycle management, real-time inference, and generative AI capabilities. It bridges the gap between data platforms and business applications by providing scalable, production-ready AI services.

Core Responsibilities

• ML model training, validation, and deployment pipelines
• Real-time and batch inference serving with auto-scaling
• Model registry and versioning with A/B testing capabilities
• Vector databases and embedding management for semantic search
• Generative AI services and LLM integration
• Continuous learning and model retraining automation

Model Lifecycle Management

• Experiment tracking and model versioning
• Automated training pipelines with hyperparameter tuning
• Model validation and performance monitoring
• Staged deployment with canary releases and rollbacks
• Model registry with metadata and lineage tracking
• Automated retraining triggers based on data drift

Inference & Serving

• High-performance model serving with GPU/CPU optimization
• Real-time inference APIs with sub-100ms latency
• Batch prediction processing for large datasets
• Auto-scaling based on traffic patterns and resource usage
• Multi-model serving and ensemble predictions
• Edge deployment for low-latency applications

Vector & Embedding Services

• Vector databases for similarity search and retrieval
• Embedding generation and management pipelines
• Semantic search and recommendation engines
• RAG (Retrieval-Augmented Generation) implementations
• Knowledge graph integration and entity linking
• Multi-modal embeddings for text, images, and audio

Generative AI & LLM Integration

• LLM deployment and fine-tuning infrastructure
• Prompt engineering and template management
• Multi-provider LLM gateway with cost optimization
• Content generation and summarization services
• Conversational AI and chatbot frameworks
• AI safety and content moderation pipelines

Data Integration & Feature Engineering

• Feature stores with real-time and batch feature serving
• Data pipeline integration with streaming and batch sources
• Feature engineering and transformation pipelines
• Data quality monitoring and drift detection
• Multi-source data fusion and enrichment
• Privacy-preserving ML with federated learning

Monitoring & Observability

• Model performance monitoring and alerting
• Data drift and concept drift detection
• Inference latency and throughput monitoring
• Model explainability and bias detection
• Resource utilization and cost tracking
• A/B testing and champion/challenger analysis

Security & Governance

• Model access control and authentication
• Data privacy and PII protection in ML pipelines
• Model audit trails and compliance reporting
• Adversarial attack detection and defense
• Bias detection and fairness monitoring
• Responsible AI governance and ethics frameworks

Architecture Patterns

• Microservices architecture for ML model serving
• Event-driven ML pipelines with real-time triggers
• Lambda architecture for batch and stream processing
• Model-as-a-Service (MaaS) with API-first design
• Multi-cloud and hybrid ML deployment strategies
• Edge computing for distributed inference

Tech Examples

• ML Platforms: MLflow, Kubeflow, SageMaker, Vertex AI
• Model Serving: TensorFlow Serving, TorchServe, Seldon, KServe
• Vector Databases: Pinecone, Weaviate, Qdrant, Chroma
• LLM Infrastructure: Hugging Face, Ollama, vLLM, LangChain
• Feature Stores: Feast, Tecton, Hopsworks
• Monitoring: Evidently, WhyLabs, Neptune, Weights & Biases

KPIs/SLIs/SLOs

• Model accuracy and performance metrics (precision, recall, F1)
• Inference latency: P50/P95/P99 response times
• Model serving availability and uptime (99.9%+)
• Data freshness and pipeline success rates
• Resource utilization and cost per inference
• Model drift detection and retraining frequency