Model Deployment Workflow Blueprint

Gather the trained model file (e.g., .h5, .pt, .onnx), along with any required dependencies, configuration files, and a serialization format. Set up the target deployment environment (cloud, on-prem, or edge) with the necessary runtime, libraries, and permissions. This step ensures the model is ready for packaging and the infrastructure is provisioned.

Why ONNX (Open Neural Network Exchange): ONNX is a direct model serialization and conversion tool, which is the primary need for preparing model artifacts. It supports multiple frameworks and edge deployment.

Build a Docker image that bundles the model file, inference code, and all runtime dependencies into a single, reproducible unit. Write a Dockerfile that copies the model, installs dependencies, and exposes a prediction endpoint (e.g., via Flask or FastAPI). Test the container locally to verify it starts and responds to requests.

Why MLEM: MLEM is designed for model packaging and saving, which directly supports containerizing the model and its dependencies.

Push the Docker image to a container registry (e.g., Docker Hub, AWS ECR, Google Container Registry). Then, define deployment manifests (Kubernetes YAML, Terraform, or serverless config) that specify replicas, resource limits, environment variables, and health checks. This step makes the image available and defines how it will be run in production.

Why Huddle01 Cloud: Huddle01 Cloud provides managed Kubernetes clusters and VM deployment, directly supporting container registry push and orchestration configuration.

Apply the orchestration configuration to the target cluster or platform (e.g., kubectl apply, Terraform apply, or serverless deploy). Monitor the rollout to ensure pods/services start successfully and pass health checks. This step transitions the model from staging to live serving.

Why DigitalOcean Gradient AI Inference Cloud: DigitalOcean Gradient AI Inference Cloud is specifically designed for AI model deployment and inference, directly matching the production deployment need.

Configure monitoring tools (e.g., Prometheus, Grafana, CloudWatch) to track inference latency, error rates, and resource utilization. Set up structured logging (e.g., JSON logs to stdout) and a log aggregation system (e.g., ELK, Loki). This step ensures you can detect issues and debug in production.

Why Polyaxon: Polyaxon includes experiment tracking and model deployment monitoring capabilities, which align with setting up monitoring and logging.

If desired, deploy a second version of the model (e.g., v2) alongside the current version and route a small percentage of traffic to it. Use a service mesh (Istio) or load balancer rules to split traffic. Compare performance metrics and user feedback before fully rolling out.

Why Polyaxon: Polyaxon supports experiment tracking and model deployment, which can facilitate A/B testing by managing different model versions and comparing performance.

Set up a CI/CD pipeline (e.g., GitHub Actions, Jenkins, GitLab CI) that triggers on new model versions or data updates. The pipeline should rebuild the Docker image, run validation tests, and redeploy automatically (or with manual approval). This step closes the loop for continuous improvement.

Why Polyaxon: Polyaxon supports experiment tracking, hyperparameter optimization, and model deployment, which are key for automating retraining and redeployment pipelines.