Model Deployment

The entry point and traffic manager for all inference requests. This component routes client requests to available backend servers and enforces system-wide policies.

• Load Balancing: Distributes requests across multiple inference server instances using algorithms like round-robin or least connections.
• Cross-Cutting Concerns: Handles authentication, authorization, rate limiting, SSL termination, and request logging.
• Health Checks: Probes backend servers to route traffic only to healthy instances. Tools like NGINX, Envoy, and cloud-native load balancers (AWS ALB, GCP Cloud Load Balancing) fulfill this role.

The control plane that manages the lifecycle of containerized inference services. It automates deployment, scaling, and recovery.

• Kubernetes is the de facto standard, using objects like Deployments and StatefulSets to declare the desired state.
• Scheduling: Places pods (containers) onto nodes based on resource constraints (GPU memory, CPU).
• Auto-scaling: Horizontally scales the number of inference server pods up or down based on metrics like CPU utilization or requests per second using the Horizontal Pod Autoscaler (HPA).

A versioned, centralized repository for trained model artifacts and their metadata. It is the single source of truth for models promoted to production.

• Stores model files (.pt, .pb, .onnx), code, and dependencies.
• Tracks Metadata: Training metrics, dataset version, hyperparameters, and lineage.
• Enables Governance: Access control, approval workflows, and audit trails. Examples include MLflow Model Registry, Weights & Biases Model Registry, and cloud-native solutions like Azure ML Model Registry.

The integrated suite of tools for monitoring, logging, and tracing the health and performance of the deployment.

• Metrics: Collects system (CPU, memory, GPU utilization) and business metrics (latency, throughput, error rate) via Prometheus.
• Logging: Aggregates structured logs from all components using the ELK Stack (Elasticsearch, Logstash, Kibana) or Loki.
• Distributed Tracing: Tracks a single request's path through the gateway, load balancer, and inference server using Jaeger or Zipkin to diagnose latency bottlenecks.

The automated workflow that tests, packages, and deploys a new model version into production. It bridges the gap between model development and serving.

• Stages typically include:
  1. Validation: Unit tests, model performance evaluation on a holdout set.
  2. Packaging: Containerizing the model and its runtime environment into a Docker image.
  3. Staging Deployment: Canary or blue-green deployment to a small percentage of traffic.
  4. Promotion: Full rollout upon validation of performance and stability. Tools like GitLab CI/CD, GitHub Actions, and Argo CD automate this pipeline.

The process of deploying a trained model into a production environment where it can receive input data, perform inference, and return predictions via a defined interface. It encompasses the software systems, APIs, and infrastructure required to make a model's capabilities available to users or other services. Core concerns include latency, throughput, scalability, and reliability.

A specialized software application designed to load machine learning models and execute inference requests at scale. It acts as the core runtime engine, handling:

• Model lifecycle management (loading, unloading, versioning)
• Request batching and scheduling for GPU efficiency
• Multi-framework support (e.g., PyTorch, TensorFlow, ONNX)
• Resource isolation and multi-tenancy

Examples include NVIDIA Triton Inference Server, TensorFlow Serving, and TorchServe.

Two fundamental serving patterns defined by latency requirements.

Online (Real-Time) Inference:

• Synchronous, low-latency responses to individual requests.
• Typical for user-facing applications (e.g., chat, recommendations).
• Prioritizes p99 latency and high availability.

Batch Inference:

• Asynchronous processing of large, pre-collected datasets.
• Used for offline predictions (e.g., generating daily forecasts, scoring customer segments).
• Optimizes for throughput and cost per prediction.

The standard method for packaging and scaling model services.

Containerization (e.g., Docker) packages the model, its dependencies, and the serving code into a portable, isolated unit.

Orchestration (e.g., Kubernetes) automates deployment, scaling, and management of containerized model services. Key concepts include:

• Deployments for declarative updates
• Services for network abstraction
• Horizontal Pod Autoscaling based on demand
• ConfigMaps & Secrets for environment management

Strategies for safely releasing new model versions with minimal risk.

Canary Deployment: A new model version is rolled out to a small percentage of production traffic (e.g., 5%). Performance is monitored for errors or drift before gradually increasing traffic to 100%.

Blue-Green Deployment: Two identical environments (Blue = old version, Green = new version) are maintained. All traffic is routed to Blue. After deploying the new model to Green, traffic is switched instantaneously, allowing for zero-downtime rollbacks.

The practice of continuously tracking a deployed model's health and performance. Critical metrics include:

• Performance Metrics: Prediction accuracy, precision, recall.
• Operational Metrics: Latency, throughput, error rates, GPU utilization.
• Data Drift: Statistical shift in live input data vs. training data.
• Concept Drift: Change in the relationship between inputs and the target variable.

Tools like Prometheus, Grafana, and specialized ML platforms (e.g., WhyLabs, Arize) are used to instrument services and set alerts.