Who should use the Deploy AI models workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for deploy ai models with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A fully automated process to push model updates to production with validation and rollback safety.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A fully automated process to push model updates to production with validation and rollback safety.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use ONNX (Open Neural Network Exchange) to a self-contained model artifact ready for deployment, with all dependencies and preprocessing captured. Then, you pass the output to MLEM to a versioned, portable docker image containing the model and its inference api, stored in a registry. Then, you pass the output to Huddle01 Cloud to a deployment configuration tailored to the chosen platform, with resource constraints and networking defined. Then, you pass the output to Huddle01 Cloud to a live inference endpoint accessible via http, with the model serving predictions. Then, you pass the output to Braintrust (bt) to real-time visibility into inference performance and health, with automated alerts for anomalies. Then, you pass the output to Fireworks AI to an elastic inference service that automatically adjusts capacity to handle traffic spikes and cost-efficiently scales down. Finally, GitHub Copilot is used to a fully automated process to push model updates to production with validation and rollback safety.
Prepare Model Artifacts
A self-contained model artifact ready for deployment, with all dependencies and preprocessing captured.
Containerize the Inference Service
A versioned, portable Docker image containing the model and its inference API, stored in a registry.
Configure Deployment Environment
A deployment configuration tailored to the chosen platform, with resource constraints and networking defined.
Deploy and Expose the Endpoint
A live inference endpoint accessible via HTTP, with the model serving predictions.
Set Up Monitoring and Logging
Real-time visibility into inference performance and health, with automated alerts for anomalies.
Implement Scaling and Auto-scaling
An elastic inference service that automatically adjusts capacity to handle traffic spikes and cost-efficiently scales down.
Establish CI/CD Pipeline for Model Updates
A fully automated process to push model updates to production with validation and rollback safety.
Export your trained model into a portable format (e.g., ONNX, TensorFlow SavedModel, or PyTorch TorchScript). Include any preprocessing logic, tokenizers, or normalization parameters as separate files or bundled in a container. Validate that the artifact loads and runs inference correctly in a clean environment.
Why ONNX (Open Neural Network Exchange): ONNX directly supports model conversion and inference acceleration for ONNX, TensorFlow, and PyTorch formats, and is the most relevant tool for preparing model artifacts in these formats.
Write a Dockerfile that installs only the necessary runtime dependencies (e.g., tensorflow-serving, torchserve, or a custom Flask/FastAPI app). Copy the model artifact into the image, expose an HTTP endpoint for inference, and define health check and readiness probes. Build and tag the image, then push it to a container registry.
Why MLEM: MLEM handles model packaging, saving, and multi-platform deployment, which aligns with containerizing the inference service using Docker and registries.
Choose a target platform (Kubernetes, AWS SageMaker, GCP AI Platform, or a simple VM). Set up environment variables for model path, logging level, and resource limits (CPU/memory). If using Kubernetes, write a Deployment manifest with resource requests/limits and a Service manifest for load balancing. For serverless, configure the function memory and timeout.
Why Huddle01 Cloud: Huddle01 Cloud deploys managed Kubernetes clusters, directly supporting Kubernetes manifests and cloud infrastructure configuration.
Apply the deployment configuration to your cluster or platform. For Kubernetes: run kubectl apply -f deployment.yaml and service.yaml. For SageMaker: create the endpoint using the AWS CLI or SDK. Wait for the service to become healthy (all pods ready, endpoint InService). Obtain the public or internal endpoint URL.
Why Huddle01 Cloud: Huddle01 Cloud deploys managed Kubernetes clusters and VMs, directly supporting kubectl and cloud CLI tools for exposing endpoints.
Configure logging to capture request payloads (sanitized), response times, and error rates. Integrate with a monitoring tool (Prometheus + Grafana, CloudWatch, or Stackdriver) to track latency, throughput, and error metrics. Set up alerts for high latency or error spikes. Optionally add model-specific metrics (e.g., prediction drift).
Why Braintrust (bt): Braintrust provides production LLM logging and automated AI evaluation, directly addressing monitoring and logging needs for AI models.
Configure horizontal pod autoscaling (HPA) for Kubernetes based on CPU/memory or custom metrics (e.g., requests per second). For managed platforms, enable auto-scaling with min/max instances and target utilization. Test with a load generator to ensure scaling behaves as expected. Optionally set up a canary or blue-green deployment strategy for safe updates.
Why Fireworks AI: Fireworks AI scales inference workloads with auto-scaling, directly matching the need for scaling and auto-scaling implementations.
Create a pipeline (GitHub Actions, GitLab CI, Jenkins) that triggers on new model artifacts pushed to a registry. The pipeline should: run validation tests (e.g., accuracy on a holdout set), build a new Docker image, push it, and update the deployment (e.g., kubectl set image). Include a rollback step if health checks fail after deployment.
Why GitHub Copilot: GitHub Copilot assists with code generation and optimization, which supports CI/CD pipeline development for model updates.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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