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64 students
MLOps Masters
Mastering MLOps: Build, Deploy, and Monitor Scalable Machine Learning Pipelines
Created byBoktiar Ahmed Bappy
Last updated 1/2025
English
What you'll learn
- Gain a strong understanding of MLOps concepts and their importance in bridging the gap between machine learning and production systems.
- Master the use of tools like Git, DVC, Docker, MLflow, and Grafana for efficient ML pipeline management and monitoring.
- Learn to set up and use Linux commands and environments for streamlined MLOps workflows.
- Explore CI/CD deployment for machine learning projects using tools like GitHub Actions, Jenkins, and CircleCI.
- Develop expertise in containerizing ML applications with Docker and creating custom Docker images.
- Build end-to-end machine learning pipelines for data ingestion, validation, transformation, model training, and evaluation.
- Integrate AWS SageMaker to train, deploy, and serve ML models on the cloud.
- Work with BentoML to deploy and manage machine learning models at scale.
- Learn how to set up monitoring dashboards with Grafana for real-time application performance tracking.
- Implement DVC for version control of data and pipelines, ensuring reproducibility in ML projects.
Explore related topics
Course content
10 sections • 63 lectures • 11h 40m total length
Requirements
- Basic Python Programming Skills – Familiarity with Python syntax and scripting is essential.
- Foundational Knowledge of Machine Learning – Understanding basic ML concepts like training, evaluation, and algorithms.
- Basic Understanding of Git – Experience with version control systems is helpful but not mandatory.
- Command Line Basics – Comfort with navigating and executing commands in the terminal.
- Access to a Computer – A system capable of running Docker and handling machine learning workloads.
- AWS Free Tier Account – Required for hands-on cloud exercises and deployment practices.
- Internet Connection – Reliable internet for cloud integration and software installations.
- Eagerness to Learn – A curious mindset and enthusiasm to explore MLOps tools and concepts.
Description
In today’s rapidly evolving AI landscape, deploying machine learning models to production and maintaining them at scale requires a blend of cutting-edge tools, streamlined workflows, and robust operational practices. This course on MLOps (Machine Learning Operations) is your ultimate guide to mastering the art of integrating machine learning into real-world production systems seamlessly and efficiently.
Designed for data scientists, ML engineers, and developers, this course walks you through the end-to-end lifecycle of machine learning, from model development to deployment and monitoring. You’ll learn how to bridge the gap between data science and DevOps, implementing reliable, scalable, and efficient pipelines for continuous integration and delivery of ML models.
This course covers essential MLOps concepts such as:
Model versioning, tracking, and reproducibility.
Continuous integration/continuous delivery (CI/CD) for ML.
Tools like MLflow, Kubeflow, and TensorFlow Extended (TFX).
Automating data pipelines and feature engineering.
Monitoring models in production and detecting drift.
Ensuring compliance, security, and governance in ML workflows.
With practical examples and hands-on labs, you’ll gain real-world skills to optimize your ML pipelines, reduce downtime, and enhance collaboration between teams. By the end of this course, you’ll be equipped to deliver scalable, reliable, and production-ready machine learning solutions for any industry.
Transform your passion for machine learning into real-world impact by mastering the tools and skills to deploy and scale with confidence!
Who this course is for:
- Aspiring Machine Learning Engineers – Looking to enhance their skills in deploying and managing ML models.
- Data Scientists – Interested in learning how to take ML models from experimentation to production.
- Software Engineers – Seeking to transition into the field of MLOps and gain hands-on experience with tools like Docker, CI/CD, and cloud platforms.
- DevOps Professionals – Wanting to integrate ML workflows into existing DevOps pipelines.
- AI Enthusiasts – Who want to explore the operational side of AI and ML systems.
- Cloud Engineers – Focused on utilizing cloud platforms like AWS for machine learning workflows.
- Students and Freshers – With basic ML and Python knowledge, aiming to build a career in MLOps.
- Professionals Transitioning to AI/ML Roles – Seeking a structured and practical approach to learning MLOps tools and frameworks.