Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Many enterprises rely on Flask for internal microservices and analytical tools, yet a model script launched with the built-in development server (app.run()) quickly stalls under real traffic. This course teaches how to turn scikit-learn, XGBoost or PyTorch models into reliable, production-grade WSGI services using Flask, Gunicorn and Docker.

Participants structure a modular prediction service with the Application Factory and Blueprint patterns, add input validation and standardised error responses, and tune Gunicorn --preload, worker and thread settings to control memory usage. They leave with a reusable Flask model-serving template and a release checklist.

Who Should Attend?

  • Data scientists who want to expose models as internal APIs or lightweight web services
  • Python developers integrating ML models into existing Flask/WSGI applications
  • ML engineers building synchronous model microservices
  • Prerequisite: basic Python and familiarity with scikit-learn/pandas

Key Takeaways

  • Structure a modular prediction API using Flask's Application Factory and Blueprints
  • Validate JSON and file inputs while returning consistent error codes and structured logs
  • Load models once at startup rather than per request to keep latency low
  • Configure Gunicorn WSGI worker and thread settings in place of the Flask dev server
  • Package the service as a Docker image complete with health checks and unit tests

Curriculum

Flask Architecture and Model Service Design
  • How WSGI works, the Application Factory (create_app) pattern and Blueprints
  • Designing single (/predict), batch (/predict-batch) and /healthz endpoints
Model Loading, Input Validation and Error Handling
  • Loading models and preprocessing pipelines safely within the application context
  • Schema validation with Marshmallow or Pydantic and custom HTTP error handlers
Production WSGI Serving with Gunicorn and Memory Tuning
  • sync, gthread and gevent workers and how the GIL affects CPU-bound inference
  • Using --preload for Copy-on-Write memory sharing and tuning worker timeouts
Logging, Security and Automated Testing
  • Structured JSON logging, request ID tracing and basic API key authentication
  • Testing endpoints and model predictions with pytest and the Flask test client
Packaging and Deployment with Docker and Nginx
  • Nginx reverse proxy + Gunicorn + Flask topology and payload size limits
  • Local simulation with Docker Compose and a pre-release checklist

Other Courses in This Category

  • Distributed Model and LLM Serving with Ray Serve

    Python-native distributed model and LLM serving with Ray Serve: DeploymentGraph composition, fractional GPU sharing, dynamic batching, replica autoscaling, vLLM integration and KubeRay. For ML and platform engineers.

    Model Serving & Deployment Corporate
    Live Course
    5 modules
    View
  • Model Versioning, Rollout Strategies and Safe Replacement

    Updating production models safely without downtime: MLflow Model Registry, semantic versioning, shadow deployments, canary rollouts, blue/green switches, A/B testing and automated rollback. For MLOps and ML engineers.

    Model Serving & Deployment Corporate
    Live Course
    5 modules
    View
  • Scaling Deployed Machine Learning and LLM Services

    Horizontal and vertical scaling for live model services: autoscaling on queue/GPU metrics with Kubernetes HPA, KEDA and Knative, reducing cold starts, MIG/time-slicing and load balancing. For MLOps, SRE and platform engineers.

    Model Serving & Deployment Corporate
    Live Course
    5 modules
    View
  • Introduction to Model Deployment

    The core steps for moving a trained model from notebook to production: serialization, serving via an API, container packaging, basic monitoring and rollback. For data scientists and developers new to model deployment.

    Model Serving & Deployment Corporate
    Live Course
    5 modules
    View
  • REST API and gRPC Fundamentals for Model Serving

    Fundamentals of REST and gRPC for model services: HTTP/JSON design, OpenAPI, Protocol Buffers, streaming RPCs and performance comparison. For developers who want to pick the right protocol and build their first service.

    Model Serving & Deployment Corporate
    Live Course
    5 modules
    View
  • Synchronous vs Asynchronous Inference

    Synchronous and asynchronous inference patterns: request-response, queue-based processing (Celery, Redis, Kafka), job polling, webhooks and async Python. For engineers designing long-running model calls that scale.

    Model Serving & Deployment Corporate
    Live Course
    5 modules
    View

Get in touch