PySpark.inData Engineering
High Demand

MLOps

Bridge the gap between building ML models and running them reliably in production at scale.

Beginner Friendly Self-Paced Prerequisites: Python and basic ML knowledge
Start Learning MLOps
Course Outline

What You'll Learn

  • What MLOps is and why models fail in production without it
  • Experiment tracking with MLflow — log metrics, parameters, and artefacts
  • Model versioning and the model registry (promoting models from staging to production)
  • Building ML pipelines with scikit-learn Pipelines and ZenML/Prefect
  • Serving models as REST APIs with FastAPI and MLflow model server
  • Monitoring for data drift and model degradation with Evidently
  • CI/CD for ML — automating testing and deployment of model updates
  • Feature stores — sharing and reusing features across multiple models
Overview

Introduction to MLOps

MLOps (Machine Learning Operations) is the set of practices that makes it possible to take ML models from an experimental notebook and reliably deploy, monitor, and maintain them in production. 87% of ML projects never make it to production — MLOps exists to fix that. It applies DevOps principles (CI/CD, version control, automation, monitoring) specifically to the machine learning workflow.

The core problem MLOps solves is that ML models are not just software — they depend on data and model weights that change over time. A fraud detection model that was 95% accurate when deployed may degrade to 80% accuracy six months later because real-world fraud patterns changed (this is called data drift). MLOps builds the infrastructure to detect this degradation and trigger retraining automatically.

In a mature MLOps setup, you have: experiment tracking (MLflow logs every model you train with its metrics and parameters), model registry (a central store of all approved model versions), automated retraining pipelines (triggered by data drift or on a schedule), and a serving layer (REST API or batch inference) with monitoring dashboards showing prediction accuracy, latency, and data distribution over time.

Watch & Learn

Video Tutorials

Handpicked free YouTube videos to accelerate your understanding

🎧 Playing in English

MLOps Course for Beginners

freeCodeCamp 2 hr 🇬🇧 English

Full MLOps course: experiment tracking, model packaging, deployment pipelines, and drift monitoring — all hands-on from scratch.

🎧 Playing in English

MLflow Tutorial — Track ML Experiments

AssemblyAI 30 min 🇬🇧 English

Learn MLflow from scratch — log parameters, metrics, and models. Set up the experiment UI and promote models to production.

Hands-On

Track ML experiments with MLflow

Copy the code below and paste it into your Python environment or our free online compiler.

python
# Install: pip install mlflow scikit-learn

import mlflow
import mlflow.sklearn
from sklearn.datasets import load_wine
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score

# 1. Load data
X, y = load_wine(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 2. Start an MLflow experiment run
mlflow.set_experiment("wine-quality-classifier")

with mlflow.start_run():

    # 3. Define hyperparameters
    n_estimators = 100
    max_depth = 5

    # 4. Train the model
    model = RandomForestClassifier(
        n_estimators=n_estimators,
        max_depth=max_depth,
        random_state=42
    )
    model.fit(X_train, y_train)

    # 5. Evaluate
    predictions = model.predict(X_test)
    acc = accuracy_score(y_test, predictions)
    f1  = f1_score(y_test, predictions, average='weighted')

    # 6. Log everything to MLflow
    mlflow.log_param("n_estimators", n_estimators)
    mlflow.log_param("max_depth", max_depth)
    mlflow.log_metric("accuracy", acc)
    mlflow.log_metric("f1_score", f1)

    # 7. Save the model to the registry
    mlflow.sklearn.log_model(model, "random_forest_model")

    print(f"Accuracy: {acc:.4f}, F1: {f1:.4f}")
    print(f"View run at: http://localhost:5000")
# Run 'mlflow ui' in terminal to see the dashboard
Want to run this code in your browser — no setup needed? Open Free Compiler →
Glossary

Key Concepts Explained

Master these terms and you'll understand 80% of the conversations in this field.

Experiment Tracking

Recording every model training run with its hyperparameters, metrics (accuracy, F1), data version, and saved model artefacts. MLflow, Weights & Biases, and Neptune are popular tools.

Model Registry

A central store of all model versions with metadata and lifecycle stages (Staging → Production → Archived). Enables reproducibility and safe rollbacks.

ML Pipeline

An automated sequence of steps: data ingestion → preprocessing → feature engineering → training → evaluation → deployment. Makes the entire ML workflow reproducible and automated.

Data Drift

When the statistical properties of the input data change after deployment (e.g., customer demographics shift). Causes model performance to degrade over time without retraining.

Feature Store

A centralised repository where computed features (like "customer average spend in last 30 days") are stored and shared across multiple ML models, ensuring consistency.

Model Serving

Making a trained model accessible to applications. Options: REST API (FastAPI + model), batch inference (Spark ML), or real-time serving (TensorFlow Serving, Triton).

CI/CD for ML

Continuous Integration/Deployment adapted for ML: automatically run tests on new data, retrain models, validate performance thresholds, and promote to production if metrics pass.

Evidently AI

An open-source Python library for monitoring ML models in production. Generates data quality, data drift, and model performance reports automatically.

Step-by-Step Plan

Your MLOps Learning Path

Follow these steps in order — each one builds on the last. Designed for complete beginners.

  1. 1

    ML Foundations

    Be comfortable training scikit-learn models, understanding train/test splits, and interpreting metrics like accuracy, precision, and recall.

  2. 2

    MLflow Basics

    Set up MLflow locally. Track experiments from your existing notebooks. Compare runs in the MLflow UI.

  3. 3

    Model Packaging

    Package models with Docker. Build a FastAPI endpoint that loads a model and serves predictions at /predict.

  4. 4

    ML Pipelines

    Build end-to-end pipelines with scikit-learn Pipeline objects. Add data preprocessing, feature scaling, and the model in one reusable object.

  5. 5

    Data & Model Monitoring

    Use Evidently to generate drift reports. Set up alerts for when accuracy drops below a threshold.

  6. 6

    Orchestration

    Schedule and automate pipelines with Prefect or Airflow. Trigger retraining when drift is detected.

  7. 7

    Cloud MLOps

    Explore cloud MLOps platforms: AWS SageMaker, Azure ML, or Databricks MLflow. Run your pipeline in a managed environment.

Ready to master MLOps?

Explore our free tutorials, hands-on code examples, and interview questions. No sign-up. No paywalls. Forever free.

Browse MLOps Tutorials Practice Interview Q&A Run Code Free