Model Deployment
Serve ML models as REST APIs — containerised with Docker, deployed to the cloud.
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After this skill you can…
- Wrap a model in a FastAPI endpoint
- Containerise and deploy to a cloud service
- Implement batch and online inference pipelines
Prerequisites
Watch (10 videos)
Model Size and Hyperparameters Explained
→ Deploy models to production→ Monitor model performance→ Adjust hyperparameters for optimal results
Designing Machine Learning Systems | Chapter 7: Model Deployment & Prediction Service
→ Deploy models effectively→ Implement prediction services
Banking Model Validation: Ensuring Accuracy and Protection #shorts
→ Implement validated models→ Ensure model accuracy→ Protect against model risk
Updating ML Deployments: Human-Readable Predictions on AWS Lambda & ECR
→ Update ML deployments→ Provide human-readable predictions
Run AI Models Inference on Amazon SageMaker HyperPod EKS | Amazon Web Services
→ Deploy AI models on Amazon SageMaker HyperPod EKS→ Use the HyperPod Inference Operator for simplified deployment
Trace OpenRouter Calls to LangSmith — No Code Changes Needed
→ Deploy models to LangSmith→ Use OpenRouter's Broadcast feature
LLMOPS 06: CI/CD Deployment with AWS ECS & Fargate | End-to-End GenAI Project Deployment
→ Deploy models on cloud platforms→ Configure model serving for GenAI projects→ Monitor model performance in production
Tour De Force: LLM Inference Optimization From Simple To Sophisticated - Christin Pohl, Microsoft
→ Deploy Optimized LLM Models→ Integrate with MLOps Pipelines
Deploy Machine Learning Models with AWS Lambda & Docker: Step-by-Step Hands-on
→ Deploy machine learning models to cloud environments→ Use AWS Lambda for serverless deployment
How to Deploy Machine Learning Models to AWS Lambda using Docker & ECR
→ Deploy ML models to cloud platforms→ Use containerization for model deployment→ Troubleshoot common deployment errors
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