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MLOpsAICloudKubernetescompleted
MovieSentiment
Production-oriented ML discovery platform with automated MLOps & EKS deployment.
01 · The Problem & Hypothesis
Why this needed to be built
Machine learning models often remain trapped in Jupyter notebooks without robust CI/CD, containerized packaging, automated retraining, and scalable Kubernetes orchestration.
The Core Idea
Build an end-to-end ML sentiment discovery platform demonstrating the full lifecycle: Model → Version → Package → Deploy → Monitor.
02 · Technical Implementation
How the system was architected
Engineered to bridge data science and DevOps. The system tracks experiment metrics and model artifacts via MLflow and DVC, automates container builds with GitHub Actions, deploys onto Amazon EKS clusters, and monitors inference health via Prometheus and Grafana.
Python
PyTorch / Scikit-learn
MLflow
DVC
Docker
Kubernetes
Amazon EKS
GitHub Actions
Prometheus
Grafana
03 · Key Takeaways Earned
Takeaway 01
“A machine learning model is only as dependable as the deployment pipeline and observability monitoring backing it.”
Takeaway 02
“Version-controlling datasets (DVC) and model weights (MLflow) is mandatory for reproducible production systems.”