This 30-day MLOps course is designed for beginners who want to move from basic ML knowledge to production-ready MLOps fundamentals.
Each day requires 1 hour and blends concepts + hands-on thinking so you build real-world intuition.
What to expect?
By Day 30, you’ll understand how ML systems are built, deployed, monitored, and maintained in production—not just trained in notebooks.
Training Video/s: https://youtu.be/ql5rmq-FTwM
Week 1 — Foundations
Day 1: What Is MLOps?
Goal: Understand why MLOps exists and why it is essential for real-world machine learning systems.
Introduction: Why MLOps Matters
Machine Learning looks powerful in demos and notebooks.
But in the real world, models don’t live in notebooks — they live in production.
Many beginners believe the job ends once a model reaches high accuracy. In reality, that’s only the beginning. Models must be deployed, monitored, maintained, and continuously improved. This gap between experimentation and production is exactly why MLOps exists.
The Reality of Machine Learning Without MLOps
ML Works in Notebooks…
In early stages, ML models are built in controlled environments:
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Clean datasets
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Static assumptions
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Manual experimentation
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One-off training runs
Everything works perfectly inside a notebook.
…But Production Is Chaotic
Once deployed, models face:
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Changing data patterns (data drift)
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System failures and latency issues
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Inconsistent environments
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Scaling problems
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Silent accuracy degradation
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Difficulty retraining or rolling back models
Without structure, production ML becomes fragile and unpredictable.
What Is MLOps?
MLOps (Machine Learning Operations) is the practice of managing the end-to-end lifecycle of machine learning models in production.
In Simple Terms:
MLOps = Machine Learning + DevOps + Data Engineering
It ensures that models are:
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Reproducible
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Scalable
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Reliable
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Can be monitored
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Continuously improving
MLOps brings engineering discipline to machine learning.
How MLOps Combines Multiple Disciplines
Machine Learning
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Model development
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Feature engineering
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Training and evaluation
DevOps
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CI/CD pipelines
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Version control
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Deployment automation
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Infrastructure management
Data Engineering
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Data pipelines
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Data validation
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Feature stores
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Data quality monitoring
MLOps connects all three into one reliable system.
The Machine Learning Model Lifecycle
Understanding the lifecycle is critical to understanding MLOps.
1. Data Collection & Validation
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Gather raw data
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Check quality, schema, and completeness
2. Feature Engineering
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Transform raw data into usable features
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Ensure consistency between training and production
3. Model Training
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Train models
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Track experiments and metrics
4. Model Evaluation
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Validate accuracy, bias, and performance
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Compare against baselines
5. Deployment
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Serve models via APIs or batch jobs
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Integrate into applications
6. Monitoring
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Track performance, drift, latency, and errors
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Detect when models degrade
7. Retraining & Iteration
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Update models with new data
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Redeploy safely
MLOps manages this entire loop.
Why MLOps Is Critical in the Real World
Without MLOps:
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Models break silently
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Teams struggle to reproduce results
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Scaling becomes expensive
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Business trust in ML erodes
With MLOps:
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ML systems behave like reliable software
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Teams collaborate efficiently
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Models improve continuously
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Businesses gain real value from AI
Who Should Learn MLOps?
MLOps is essential for:
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Aspiring ML Engineers
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Data Scientists moving to production
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DevOps Engineers entering AI
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AI Infrastructure Engineers
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Anyone building real-world ML systems
Key Takeaways
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ML models don’t fail in notebooks — they fail in production
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Production ML is complex and unpredictable
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MLOps exists to control this complexity
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MLOps unifies ML, DevOps, and Data Engineering
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Understanding the ML lifecycle is the foundation of MLOps
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