📘 MLOps for Beginners

1. What is MLOps?

• Machine Learning Operations (MLOps) is a set of practices, tools, and methods that help bring machine learning models from research into production and maintain them reliably in real-world applications.

• It combines:

  • Machine Learning (ML) → model building.

  • DevOps → automation, deployment, and monitoring.

👉 Simply put: MLOps ensures that ML models don’t just run in notebooks, but live stably in production systems.

2. Why do we need MLOps?

• A trained model saved as a .pkl or .h5 file isn’t directly usable for end users.

• MLOps solves these problems:

  • Automating the pipeline: data collection → training → deployment.

  • Versioning: managing data, code, and models.

  • Monitoring: detecting if the model suffers from “drift” (data distribution shift).

  • Retraining: updating models when new data comes in.

3. Key Components of MLOps

1. Data Engineering

   • Collecting, cleaning, and storing data.

   • Tools: Airflow, Spark, DBT.

2. Model Development

   • Training and evaluating models.

   • Tools: Scikit-learn, TensorFlow, PyTorch.

3. Model Versioning & Experiment Tracking

   • Managing multiple model versions.

   • Tools: MLflow, Weights & Biases.

4. CI/CD for ML (Continuous Integration/Deployment)

   • Automated testing and deployment.

   • Tools: GitHub Actions, Jenkins, GitLab CI.

5. Model Serving

   • Deploying models as APIs or batch jobs.

   • Tools: FastAPI, Flask, TorchServe, TensorFlow Serving.

6. Monitoring & Logging

   • Tracking performance, drift, and system health.

   • Tools: Prometheus, Grafana, Evidently AI.

7. Retraining & Feedback Loop

   • Collecting new data and retraining models.

4. Standard MLOps Lifecycle

1. Data collection.

2. Data preprocessing.

3. Model training.

4. Model versioning and registry.

5. Model deployment.

6. Monitoring.

7. Continuous retraining.

👉 This process is iterative (a loop).

5. Common Tools & Tech Stack

• Data & Workflow: Airflow, Prefect, Luigi.

• Experiment Tracking: MLflow, Weights & Biases.

• Deployment: Docker, Kubernetes, FastAPI.

• Monitoring: Prometheus, Grafana, Evidently AI.

• Cloud Platforms: AWS SageMaker, GCP Vertex AI, Azure ML.

6. Learning Path for Beginners

Step 1: Learn ML basics

• Regression, classification, training/evaluation.

• Tools: scikit-learn, pandas, matplotlib.

Step 2: Learn DevOps fundamentals

• Git/GitHub.

• Docker (containerization).

• CI/CD (GitHub Actions).

Step 3: Learn MLOps workflow

• MLflow for experiment tracking.

• FastAPI for model deployment.

• Docker + Kubernetes for scaling.

Step 4: Learn monitoring

• Prometheus + Grafana.

• Data drift detection with Evidently AI.

Step 5: Build a small end-to-end project

Example:

• Collect movie review data 🎬.

• Train a sentiment classification model.

• Deploy with FastAPI + Docker.

• Track with MLflow.

• Monitor with Evidently AI.

7. Resources for Learning

• Made With ML – structured roadmap for MLOps.

• Full Stack Deep Learning – detailed course.

• Book: Practical MLOps (Noah Gift).

• YouTube: “MLOps Community” channel.