Building Robust AI Backends

Module 1 — API Design

• Understanding use cases: inference APIs vs training / fine-tuning APIs
• Compare synchronous calls, queued background jobs, callbacks/webhooks, polling and streaming; implement a selected pattern.
• Handling large data payloads: chunking, streaming uploads/downloads, file storage vs inline data, memory limits
• Rate limits, quotas and backpressure to reduce abuse and overload risk.
• API versioning strategies: URL versioning, header versioning, backward compatibility, deprecation policies
• API documentation & contracts: OpenAPI / Swagger for AI endpoints, specifying error modes, result format, latency expectations, fallback behavior

Module 2 — Model Integration and Packaging

• Pre-trained models vs hosted vs custom models: trade-offs (cost, latency, control)
• Compare serving frameworks appropriate to the chosen model format and available support.
  • TensorFlow Serving as an example for compatible TensorFlow models.
  • TorchServe handler/archive concepts as legacy comparative coverage: the official project is no longer actively maintained and does not plan fixes or security patches; do not present it as the default for a new production service.
  • Compare an alternative such as Triton Inference Server, checking current model/backend and hardware requirements.
• Containerization with Docker: building reproducible images, handling dependencies, packaging model + server
• Kubernetes deployment, resources, replicas and rolling-update concepts through a prepared demonstration.
• Review CI/CD tests, versioned model artefacts and rollback requirements.

Module 3 — Data Pipelines

• Data ingestion: sources of data (logs, user inputs, sensors, etc.), batch vs streaming ingestion
• Data cleaning & preprocessing: missing values, outliers, normalization, feature extraction, tokenization for text, image preprocessing
• Handling structured vs unstructured data: relational / NoSQL databases, object storage, document stores, vector databases for embeddings
• Data storage optimized for AI workloads: formats (e.g. Parquet, Avro), storage systems, partitioning, indexing, retrieval efficiency
• Ensuring reproducibility and traceability in pipelines: data versioning, schema management, data lineage

• Build or inspect a small inference endpoint connected to a prepared model/data example.

Module 4 — Performance and Observability

• Cache only where model/version, data freshness and user/tenant boundaries make reuse valid.
• Efficient data serialization: JSON vs binary formats (e.g. Protobuf, MessagePack), streaming serialization when large data
• Parallel and asynchronous processing: worker queues, batching requests, handling concurrency to avoid blocking
• Explore warm starts, preloading and supported accelerator use; measure effects rather than assume faster inference.
• Measure latency, throughput, errors and resources; discuss model/data drift separately from infrastructure metrics.

Module 5 — Security, Scaling and Failure Handling

• Authentication, authorisation and structured input/output validation; sanitisation alone does not eliminate injection or adversarial-input risk.
• Review rate limits and abuse controls without duplicating the API exercise.
• Scaling strategies: autoscaling (horizontal scaling), load balancing, scaling model servers separately from frontend APIs, using queues / message brokers to smooth spikes
• Microservices architecture vs serverless for AI inference: trade-offs, when to choose each, operational overheads
• API gateways: using gateways for routing, monitoring, authorization, throttling, versioning, and handling cross-cutting concerns
• Bounded retries, circuit breakers and graceful degradation appropriate to operation semantics; avoid duplicated side effects.

Module 6 — Responsible AI Engineering

• Data minimisation, encryption and relevant privacy/regulatory review questions; engineering controls do not themselves guarantee GDPR/CCPA compliance.
• Review dataset and outcome bias, selected fairness metrics and mitigation trade-offs; no universal fairness guarantee.
• Explainability appropriate to the model/task, documented strengths/limits and human oversight; avoid logging sensitive data unnecessarily.
• Auditing & accountability: keeping version history of models & data, tracking feature changes, test cases for ethical edge cases
• Fallback behaviour, alerts and user communication when model outputs or services fail.

Module 7 — Architecture and Release Review

• Microservices vs monolithic architectures for AI: deployment, ops complexity, isolation of AI components; separation of concerns
• Serverless inference/preprocessing concepts with cold-start, memory, duration and model-size constraints.
• API gateways and service meshes: traffic management, security, observability across multiple AI services
• Review versioned infrastructure/configuration and environment parity using a selected example.
• Testing/staging and shadow/canary release concepts, with approval and rollback decisions.

• Evaluate the selected prototype, record constraints and identify additional checks before deployment.
• Compare alternative patterns against actual workload and support requirements rather than assume a single architecture is always best.