MLOps 101
My Journey into Production Machine Learning
Hey there! 👋
I want to share what I've learned about taking machine learning models from Jupyter notebooks to production systems. If you've ever trained a model that worked perfectly on your laptop but struggled to deploy it reliably, or found yourself manually retraining models and wondering "there has to be a better way," this guide is for you.
MLOps isn't just DevOps for ML—it's a complete rethinking of how we build, deploy, and maintain machine learning systems at scale.
What You'll Learn
This guide covers everything I wish I knew when I started deploying ML models in production:
Core Concepts
MLOps Fundamentals: Understanding the ML lifecycle and why traditional DevOps falls short
Kubernetes for ML: Container orchestration tailored for ML workloads
Kubeflow Ecosystem: The complete ML platform built on Kubernetes
Practical Implementation
Development Environment: Setting up Kubeflow Notebooks with Python 3.12
Pipeline Orchestration: Building reproducible ML pipelines with Kubeflow Pipelines
Automated Training: Hyperparameter tuning and experiment tracking with Katib
Model Serving: Deploying models with KServe for production inference
Model Management: Versioning and tracking with Model Registry
Production Operations
Monitoring & Observability: Tracking model performance in production
CI/CD for ML: Automated testing, validation, and deployment
End-to-End Workflow: Complete example from data to production
Why Kubeflow?
After exploring various MLOps platforms, I chose Kubeflow because:
Kubernetes-Native: Leverages existing K8s infrastructure and skills
Modular Design: Use only the components you need
Open Source: No vendor lock-in, active community
Production-Ready: Battle-tested by organizations running ML at scale
Complete Platform: Covers the entire ML lifecycle
Prerequisites
To get the most out of this guide, you should have:
Basic Python knowledge (we'll use Python 3.12)
Familiarity with Docker and containers
Understanding of basic Kubernetes concepts (pods, services, deployments)
Experience training machine learning models
Don't worry if you're not an expert—I'll explain concepts as we go.
What Makes This Guide Different
This isn't a theoretical overview or marketing material. Everything here comes from:
Real implementations and debugging sessions
Actual production deployments and lessons learned
Personal projects where I've used these tools
Mistakes I've made (and how to avoid them)
You won't find "imagine you work at Company X" scenarios here—just practical knowledge from hands-on experience.
The Path Forward
Machine learning in production is different from research. Models drift, data changes, infrastructure fails, and business requirements evolve. MLOps gives us the tools and practices to handle these challenges systematically.
Let's dive in and build production-ready ML systems together.
Navigation:
Start with MLOps Fundamentals to understand core concepts
Jump to Kubeflow Overview & Setup if you want to get hands-on quickly
Check the End-to-End Example to see everything working together
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