Introduction to MLOps
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Getting started with the Use Case and Environment Setup
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From Raw Data to Models
Understanding Data Science with Feature Engineering and Experimentation
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Packaging Model along with FastAPI Wrapper and Streamlit with Containers
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Setting up MLOps CI Workflow with GitHub Actions
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Building Scalable Prod Inference Infrastructure with Kubernetes
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Monitoring and Autoscaling a ML Model – Prometheus, Grafana, KEDA, HPA, VPA
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GitOps Based Deployments for ML/LLM Apps
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