ML Model Deployment
Serve models reliably: inference APIs, batching, autoscaling, GPU utilization, model optimization, and hitting a latency target without overspending.
Course Overview
Deployment is where a model stops being a research artifact and starts being a service with a latency budget, an error rate, and a monthly bill. This course focuses narrowly and practically on that transition: how to wrap a trained model in a service that is fast enough, cheap enough, and reliable enough to keep.
You will build inference services and then make them efficient. That means finding where time actually goes, which is rarely where people assume: preprocessing and serialization frequently dominate model compute, and a batching strategy tuned for throughput can wreck the tail latency users experience. You will measure rather than guess, and apply dynamic batching, model optimization, and hardware selection deliberately.
The course also covers the operational surface that makes a deployment supportable: autoscaling policies that account for slow model load times, health checks that detect a model returning nonsense rather than merely a process that is alive, versioned endpoints, graceful rollout and rollback, and the cost arithmetic that decides between CPU, GPU, and serverless inference.
Duration: 2 days|Delivery: onsite, virtual, or hybrid
Prerequisites
- Experience with trained models in Python (scikit-learn, PyTorch, or TensorFlow)
- Working knowledge of Docker
- Basic understanding of HTTP APIs and REST
- Some cloud deployment experience is helpful
Who Should Attend
- ML engineers who need to expose a model as a production service
- Backend engineers asked to host and scale a model built by another team
- Platform engineers standardizing inference infrastructure across teams
- Technical leads sizing the infrastructure cost of an AI feature before commitment
Course Outline
- 1Deployment patterns compared: batch scoring, real-time APIs, streaming, and edge
- 2Building an inference service: FastAPI, request validation, and error handling
- 3Serialization and preprocessing: the hidden cost that often dominates latency
- 4Model servers compared: TorchServe, TensorFlow Serving, Triton, and rolling your own
- 5Dynamic batching: throughput gains and the tail latency cost
- 6Model optimization: quantization, pruning, distillation, and ONNX export
- 7Hardware selection: CPU, GPU, and inference accelerators with real cost comparison
- 8Autoscaling: cold starts, model load time, and why default settings misbehave
- 9Health checks and readiness: detecting a model that is alive but wrong
- 10Versioned endpoints, blue-green deploys, and safe rollback
- 11Load testing: finding the actual throughput ceiling and latency curve
- 12Cost per prediction: modeling and reducing it
Learning Outcomes
- Choose a deployment pattern that matches your latency and volume requirements
- Build and containerize a production-grade inference service
- Profile an inference path and identify where time is actually spent
- Apply quantization and export optimizations without unacceptable accuracy loss
- Configure autoscaling that accounts for model load times and cold starts
- Load test a service to establish its real throughput ceiling
- Calculate and reduce cost per prediction across hardware options
What You Will Build
- A containerized inference service with validation, health checks, and versioning
- A load test report showing the throughput and latency curve
- An optimization comparison measuring accuracy against latency and cost
- A cost-per-prediction model across at least two hardware options
Frequently Asked Questions
- Do we need GPUs for inference?
- Often not. Many models, including quantized transformers of moderate size, serve acceptably on CPU at far lower cost, especially at low request volumes where a GPU sits idle. The course covers the crossover point and how to measure it for your model rather than assuming a GPU is required.
- Should we use a managed endpoint or self-host?
- Managed endpoints such as SageMaker or Vertex AI cut operational work and are usually right at low to moderate volume. Self-hosting wins on cost at sustained high throughput and when you need control over batching and hardware. The course provides the cost arithmetic so the decision is a calculation rather than a preference.
- How does dynamic batching hurt latency?
- Batching waits to accumulate requests, which raises throughput but adds queueing delay to every request. A batch window tuned for GPU efficiency can push tail latency well past an interactive service level target. The course covers tuning the window against your actual traffic pattern and measuring the effect on the tail, not just the average.
- Is this course specific to a cloud provider?
- No. The concepts and labs are portable, using containers and open-source model servers. Managed equivalents on AWS, Azure, and GCP are covered so you can apply the material on your platform.
- How does this relate to the MLOps course?
- MLOps for Production AI covers the full lifecycle including training pipelines, registries, and retraining. This course focuses specifically and more deeply on serving: latency, throughput, hardware, and cost. They complement each other and can be taken in either order.
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Ready to Get Started?
Contact us to schedule training for your team or inquire about upcoming sessions.