Table of Contents
You have a model that works in a notebook. Now you need it in production - serving predictions under real-world load, handling failures, staying up-to-date. This is where most ML projects fail. Here's how to hire ML engineers who can actually deploy models.
Model Deployment Patterns
A qualified ML engineer knows:
- ✦ Online inference (REST API, gRPC, WebSocket) - for real-time predictions
- ✦ Batch inference (Spark, Flink, scheduled jobs) - for large-scale offline processing
- ✦ Streaming inference (Kafka, Kinesis) - for event-driven predictions
- ✦ Edge deployment (ONNX, TensorFlow Lite) - for mobile or IoT
- ✦ Model versioning and canary deployments
- ✦ Blue-green and shadow deployments for safe rollout
Must-Have Deployment Skills
- ✦ Containerization (Docker) and orchestration (Kubernetes)
- ✦ Model serving frameworks (TensorFlow Serving, TorchServe, BentoML, KServe)
- ✦ API frameworks (FastAPI for Python models)
- ✦ Infrastructure as code (Terraform, Pulumi, CloudFormation)
- ✦ CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
- ✦ Monitoring and alerting (Prometheus, Grafana)
- ✦ Model versioning and artifact storage (MLflow, DVC, S3)
Production Metrics They Should Be Able to Discuss
P95 latency
Why It Matters:
User experience and SLA compliance
Throughput (RPS)
Why It Matters:
Scaling capacity
Error rate
Why It Matters:
Reliability
Model load time
Why It Matters:
Deployment efficiency
Infrastructure cost per 1M predictions
Why It Matters:
Cost control
Model drift rate
Why It Matters:
Prediction quality
Experienced ML engineers can explain how deployment decisions affect these metrics.
Interview Questions That Work
Load balancing, horizontal scaling, auto-scaling based on CPU/latency. Model serving on Kubernetes. Optimize model size (quantization, pruning). CDN for static predictions.
Model optimization (quantization, pruning, distillation). Hardware (GPU vs CPU). Batching requests. Model parallelism. Caching frequent queries.
Red Flags
Walk away if they:
- ✦ Have only deployed models in Jupyter notebooks
- ✦ Can't explain the difference between online and batch inference
- ✦ No experience with containerization
- ✦ Never used a model serving framework
- ✦ Don't understand model versioning
Real Production Problems They Should Have Solved
- ✦ Model drift causing prediction quality degradation
- ✦ Inference latency spikes during peak traffic
- ✦ Failed deployment rollback
- ✦ GPU resource exhaustion
- ✦ Feature store synchronization issues
- ✦ Training-serving skew
Hire Deployment Experts
Model deployment is a specialized skill. Offline Pixel pre-vets ML engineers who have shipped models to production. Raise a request, talk to candidates, fund the project, and approve payment when the work is done.
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