Introducing MLOps: From Model Development to Deployment (AI)

Name: Introducing MLOps: From Model Development to Deployment (AI)
Rating: 4.5 (597 reviews)

A Practical Guide to Building, Automating, and Scaling Machine Learning Pipelines with Modern Tools and Best Practices

Created bySchool of AI

Last updated 2/2026

English

English [Auto],Spanish [Auto],

What you'll learn

Understand the core concepts, benefits, and evolution of MLOps.
Learn the differences between MLOps and DevOps practices.
Set up a version-controlled MLOps project using Git and Docker.
Build end-to-end ML pipelines from data preprocessing to deployment.
Transition ML models from experimentation to production environments.
Deploy and monitor ML models for performance and data drift.
Gain hands-on experience with Docker for ML model containerization.
Learn Kubernetes basics and orchestrate ML workloads effectively.
Set up local and cloud-based MLOps infrastructure (AWS, GCP, Azure).Troubleshoot common challenges in scalability, reproducibility, and reliability.

Course content

3 sections • 18 lectures • 1h 48m total length

Certificate of Completion0:29
Finish the course on Udemy, download your Udemy certificate, and email it to schoolofaillc at gmail.com to have your completion verified and receive the official School of AI certificate.
Introduction to Section0:37
Understand the overview, importance, and evolution of MLOps, compare it with DevOps, and cover versioning, automation, monitoring, plus a hands-on setup with Git, Docker, and a simple model pipeline.
Overview of MLOps and its Importance1:29
MLOps combines machine learning with DevOps to automate and streamline the model lifecycle. It enables collaboration among data scientists, engineers, and IT operations for reproducible, scalable, and maintainable production.
Evolution of Machine Learning Operations1:06
Explore how ml operations evolved from manual model development to automated, scalable processes, emphasizing continuous integration, continuous delivery, automated workflows, and monitoring from development to production.
Key Concepts in MLOps: Versioning, Automation, and Monitoring1:22
Explore key MLOps concepts including data versioning, model versioning, code versioning, automation with automatic training and deployment via CI/CD pipelines, and monitoring with logging for drift and performance.
MLOps vs. DevOps: Similarities and Differences1:12
Explore how MLOps and DevOps share automation, collaboration, and CI/CD pipelines while highlighting the differences in artifacts, experimentation, and monitoring for data-driven models.
Hands-on: Set up a basic MLOps Project Structure (Git, Docker, Model Pipeline)20:37
Set up a foundational MLOps project with Git version control, Docker containerization, MLflow experiment tracking, and basic monitoring to streamline model development and deployment.

Introduction to Section0:24
Discover the ml workflow from data preparation to deployment, compare experimentation and production, address deployment challenges, and build an end-to-end ml model pipeline hands-on.
Overview of the ML Workflow: Data Preparation to Deployment3:11
Explain the ml workflow from data preparation to deployment, including data pre-processing, model training, evaluation, hyperparameter tuning, and containerized api deployment.
Experimentation vs. Production1:51
Examine the differences between experimentation and production in mlops, from notebooks and small data sets to scalable pipelines. Use Kubeflow, MLflow, and CI/CD to automate retraining, deployment, and monitoring.
Challenges in Deploying ML Models0:58
Explore challenges in deploying ml models, including scalability, real-time serving infrastructure, and reproducibility across environments to ensure reliability under varying loads and data, while guarding against concept drift.
Hands-on: Build an end-to-end pipeline for an ML model21:06
Build an end-to-end machine learning pipeline—from data pre-processing through model training and evaluation to saving and loading the model in production—using Python and Jupyter notebooks with California housing data.

Introduction to Section0:36
Explore infrastructure for MLOps, including cloud platforms such as AWS, GCP, and Azure, Docker containerization, Kubernetes orchestration, and setting up a local MLOps environment through hands-on practice.
Introduction to Cloud Platforms (AWS, GCP, Azure)4:13
Compare AWS, GCP, and Azure cloud platforms for MLOps, highlighting SageMaker, S3, EKS, AI platform, BigQuery ML, GKE, and Azure ML to guide cloud infrastructure setup.
Containerization with Docker1:21
Use Docker containerization to ensure reproducibility and consistency across development, testing, and production. Build and run ML models with a Dockerfile to deploy on AWS, GCP, and Azure.
Kubernetes for Orchestrating ML Workloads1:20
Orchestrate ml workloads with Kubernetes by deploying, scaling, and managing containerized apps using pods, nodes, deployments, and services, with local setup via Minikube or Docker Desktop.
Setting up Local MLOps Environments1:25
Learn to set up local mlops environments with Python virtual environments, SQLite databases, and Docker Compose; implement unit and integration tests, and manage code with Git and automated builds.
Hands-on: Containerize a simple ML model and deploy it locally using Kubernetes45:41
learn to containerize a simple ml model with docker, build and run a flask app, and deploy it locally on kubernetes using minikube.

Requirements

Basic Python Programming Skills: Familiarity with Python syntax and scripting.
Fundamentals of Machine Learning: Understanding of ML concepts like training, testing, and evaluation.
Basic Knowledge of Data Science Tools: Exposure to Jupyter Notebooks or similar tools.
Understanding of Version Control: Familiarity with Git for tracking code changes.
Willingness to Learn Docker and Kubernetes: No prior experience needed, but a readiness to learn these tools is essential.
Basic Command-Line Skills: Ability to navigate and execute commands in a terminal.
Access to a Computer with Internet Connection: Suitable for running Docker and cloud services.
Curiosity and Problem-Solving Mindset: Enthusiasm to troubleshoot and optimize workflows.

Description

In today’s AI-driven world, the demand for efficient, reliable, and scalable Machine Learning (ML) systems has never been higher. MLOps (Machine Learning Operations) bridges the critical gap between ML model development and real-world deployment, ensuring seamless workflows, reproducibility, and robust monitoring. This comprehensive course, Mastering MLOps: From Model Development to Deployment, is designed to equip learners with hands-on expertise in building, automating, and scaling ML pipelines using industry-standard tools and best practices.

Throughout this course, you will dive deep into the key principles of MLOps, learning how to manage the entire ML lifecycle — from data preprocessing, model training, and evaluation to deployment, monitoring, and scaling in production environments. You’ll explore the core differences between MLOps and traditional DevOps, gaining clarity on how ML workflows require specialized tools and techniques to handle model experimentation, versioning, and performance monitoring effectively.

You’ll gain hands-on experience with essential tools such as Docker for containerization, Kubernetes for orchestrating ML workloads, and Git for version control. You’ll also learn to integrate cloud platforms like AWS, GCP, and Azure into your MLOps pipelines, enabling scalable deployments in production environments. These skills are indispensable for anyone aiming to bridge the gap between AI experimentation and real-world scalability.

One of the key highlights of this course is the practical, hands-on projects included in every chapter. From building end-to-end ML pipelines in Python to setting up cloud infrastructure and deploying models locally using Kubernetes, you’ll gain actionable skills that can be directly applied in real-world AI and ML projects.

In addition to mastering MLOps tools and workflows, you'll learn how to address common challenges in ML deployment, including scalability issues, model drift, and monitoring performance in dynamic environments. By the end of this course, you’ll be able to confidently transition ML models from Jupyter notebooks to robust production systems, ensuring they deliver consistent and reliable results.

Whether you are a Data Scientist, Machine Learning Engineer, DevOps Professional, or an AI enthusiast, this course will provide you with the skills and knowledge necessary to excel in the evolving field of MLOps.

Don’t just build Machine Learning models — learn how to deploy, monitor, and scale them with confidence. Join us in this transformative journey to Master MLOps: From Model Development to Deployment, and position yourself at the forefront of AI innovation.

This course is your gateway to mastering the intersection of AI, ML, and operational excellence, empowering you to deliver impactful and scalable AI solutions in real-world production environments.

Who this course is for:

Data Scientists looking to transition their models from experimentation to production.
Machine Learning Engineers aiming to master end-to-end ML workflows.
DevOps Professionals interested in integrating ML workflows into CI/CD pipelines.
AI Enthusiasts eager to understand how to scale and monitor ML models effectively.
Software Engineers who want to add MLOps skills to their toolkit.
Technical Project Managers overseeing AI/ML projects and workflows.
Students and Beginners curious about building real-world ML systems.
IT Professionals aiming to specialize in AI infrastructure and deployment.
Entrepreneurs planning to deploy AI products efficiently at scale.
Anyone Passionate About AI & ML Operations looking to gain practical, hands-on experience in MLOps tools and practices.

Introducing MLOps: From Model Development to Deployment (AI)

What you'll learn

Explore related topics

Course content

Introduction to MLOps7 lectures • 27min

Data Science to Production Pipeline5 lectures • 28min

Infrastructure for MLOps6 lectures • 55min

Requirements

Description

Who this course is for: