Artificial Intelligence & Machine Learning

Architecture

A review ingestion API cleans text, extracts features, trains a classifier, and monitors sentiment drift after deployment.

Dataset

Use labeled customer reviews with balanced positive, neutral, and negative classes.

Deployment Notes

• Serve the model behind an API and log confidence scores for review.

Production Considerations

• Track language drift, sarcasm failures, and customer segment bias.

Architecture

User-item events feed feature pipelines, candidate generation, ranking, and feedback loops.

Dataset

Use interaction data such as views, clicks, purchases, ratings, and item metadata.

Deployment Notes

• Deploy ranking behind a service with online A/B testing and fallback recommendations.

Production Considerations

• Watch cold start, popularity bias, freshness, and feedback loops.

Architecture

Client uploads an image, an API stores it, a preprocessing worker normalizes it, a CNN or vision transformer predicts the class, and monitoring records confidence and drift.

Dataset

Use a labeled image dataset with train, validation, and test splits, plus augmentation for lighting, crop, rotation, and class imbalance.

Deployment Notes

• Export the model to an inference-friendly format and keep preprocessing identical between training and serving.
• Use batch inference for offline jobs and low-latency serving for user-facing uploads.

Production Considerations

• Monitor class imbalance, camera drift, confidence calibration, and false positives on high-impact classes.
• Keep human review for uncertain predictions or safety-sensitive labels.

Architecture

A chat UI sends messages to an API, the API manages conversation state and safety checks, calls an LLM provider, streams tokens, and logs feedback.

Dataset

Use prompt-response examples for evaluation, safety test cases, domain instructions, and conversation transcripts only when privacy permits.

Deployment Notes

• Implement streaming, rate limits, conversation storage, model fallback, and prompt versioning.
• Separate system instructions from user messages and validate structured outputs where needed.

Production Considerations

• Track token cost, latency, unsafe outputs, prompt injection attempts, and user satisfaction.
• Add abuse prevention, privacy controls, and retention policies for conversations.

Architecture

Documents are chunked and embedded into a vector database; user queries retrieve relevant chunks that are passed to an LLM with citations.

Dataset

Use product docs, runbooks, FAQs, or internal knowledge articles with metadata for permissions, freshness, source, and ownership.

Deployment Notes

• Build indexing jobs, retrieval APIs, reranking, context assembly, and answer citation display.
• Version embedding models and reindex when chunking or embedding strategy changes.

Production Considerations

• Measure retrieval recall, answer faithfulness, citation accuracy, and stale-document risk.
• Prevent unauthorized retrieval through metadata filters and access-aware search.

Architecture

A user gives a goal to an agent runtime that plans steps, calls tools through controlled schemas, observes results, and returns a final answer with an audit trail.

Dataset

Use task traces, tool-call examples, evaluation scenarios, permission policies, and failure cases for regression testing.

Deployment Notes

• Add tool registry, auth, budgets, timeouts, step logging, and human approval for risky actions.
• Use narrow tools and validate arguments before execution.

Production Considerations

• Monitor tool error rates, runaway loops, cost per task, unsafe actions, and completion quality.
• Design kill switches, audit logs, and escalation paths before launch.