MLOps (Machine Learning Operations) is a set of practices that helps teams build, deploy, monitor and maintain machine learning models in production systems. In simple words, MLOps connects model development, infrastructure and real-world usage into a continuous workflow so that ML systems remain reliable and scalable.

MLOps combines concepts from machine learning, software engineering, DevOps and data engineering to create scalable AI systems.

Introduction to Machine Learning and MLOps

This section explains the ML lifecycle and why MLOps is needed.

• Introduction to MLOps
• Machine Learning Lifecycle
• What MLOps solves
• Difference between notebook ML and production ML

Classic Machine Learning Overview

This module introduces core ML algorithms and evaluation methods.

Data Preparation Basics

• Handling missing values and duplicates
• Feature selection and PCA
• Dataset splitting strategies

Core ML Algorithms Overview

• Regression models
• Classification models
• Tree-based models
• Clustering methods

Evaluation Concepts

• Train–test split and cross-validation
• Accuracy, F1, ROC basics
• Confusion matrix
• Overfitting vs generalization

Environments and System Basics

It covers setting up development environments, managing dependencies and understanding basic system tools so software and ML workflows run smoothly and consistently.

Environment Setup

• Python virtual environments
• Dependency management
• Reproducibility practices

Linux and Shell Basics

• CLI navigation
• File handling
• Permissions and processes
• Bash scripting to automate ML tasks

Version Control for ML

• Git branching and commits
• Pull requests workflow
• Dataset versioning

Data Engineering for MLOps

This section explains how production ML handles large-scale data.

Data Lakes and Storage

• Data lake vs data warehouse
• S3-style storage systems
• Delta Lake basics

Batch Processing

• Introduction to Apache Spark
• PySpark batch workflows

Streaming Data Pipelines

• Apache Kafka architecture
• Spark structured streaming
• Real-time feature ingestion

Experiment Tracking and Model Lifecycle

It focuses on recording model experiments, parameters and results while managing stages like training, validation, deployment and updates so models can be improved and maintained systematically.

Experiment Tracking

• Introduction to MLflow
• Logging metrics
• Comparing model runs
• Tracking experiments

Model Registry

• Versioning models
• Staging vs production transitions
• Managing lifecycle with MLflow

Explainability, Documentation and Model Serving

It covers making model decisions understandable, properly recording model details and usage and deploying models so they can handle requests reliably in real applications

Explainable AI

• Why XAI matters in production
• Explainable AI using LIME

Documentation for ML Systems

• README files
• Experiment logs
• Dataset cards
• Model cards
• API documentation

Building Inference APIs

• Creating REST APIs for models
• Serving models with Flask
• Serving models with FastAPI

Containerization and Orchestration

It involves packaging applications with their dependencies into portable containers and managing them at scale using orchestration tools.

Containerization

• Docker architecture
• Building model containers
• Running containers locally

Kubernetes for ML Systems

• Introduction to Kubernetes
• Pods and deployments
• Services and scaling
• Horizontal Pod Autoscaling

Cloud Deployment and CI/CD

It refers to deploying applications on cloud platforms and using automated pipelines to test, integrate and update models.

Cloud Model Deployment

• Deploying models on Amazon SageMaker
• Real-time vs serverless inference
• Connecting deployment with data lake storage

CI/CD for Machine Learning

• Automated testing of models
• GitHub Actions pipelines
• Scheduled retraining
• Pipeline orchestration concepts

Monitoring, Drift Detection and Production Systems

It focuses on tracking model performance after deployment, identifying data or behavior changes over time and maintaining reliable systems so AI applications keep working correctly in real-world use.

Monitoring ML Systems

• Logging predictions
• Latency monitoring
• Usage metrics tracking
• Monitoring with Prometheus
• Visualizing metrics with Grafana

Drift Detection

• Data drift
• Concept drift
• Retraining triggers

Production Architecture

• End-to-end MLOps pipeline
• Debugging production ML systems

Edge AI

It refers to running AI models directly on local devices like phones or sensors so that predictions can be made faster, with lower latency.

• Running models on mobile devices
• IoT deployments
• Latency constraints
• Lightweight inference models

Resources:

• For project ideas refer to 100+ Machine Learning Projects with Source Code [2025] for hands-on implementation on projects
• For interview preparation, refer to Machine Learning Interview Questions and Answers to revise key concepts and commonly asked questions.