MLOps
Master the end-to-end lifecycle of machine learning models with our cover-to-cover MLOps course. This course delves into the integration of machine learning and DevOps principles. Here, you'll learn how to ensure your models are not only deployed but maintained efficiently in production.
DIFFICULTY
Intermediate to Advanced
COURSE TYPE
SCHEDULE
Self-paced
PRE-REQUISITES
Basic knowledge of Python and machine learning recommended
TAGS
MLOps, Machine Learning Lifecycle, CI/CD, Data Engineering, Model Deployment, Monitoring
What you'll learn
Introduction to MLOps
Understand the basics of MLOps, including its importance and key aspects.
Machine Learning Lifecycle
Learn the stages of the ML lifecycle, from data collection and processing to model training, deployment, and monitoring.
Tools and Technologies
Get familiar with essential tools like Apache Airflow, Kubeflow, MLflow, and Docker.
Continuous Integration and Continuous Deployment (CI/CD)
Learn how to implement CI/CD pipelines using tools like Jenkins, GitHub Actions, and GitLab CI.
Data
Engineering
Build data pipelines and understand the role of data engineering in MLOps.
Model
Deployment
Explore various deployment strategies and platforms, including AWS, Google Cloud, and Azure.
Course Introduction Video
What you will build in this course
Data Pipelines
Develop data ingestion, cleaning, and transformation pipelines using Apache Airflow.
Model Training Workflows
Get a high-level understanding of automated model training workflows with MLflow and Weights & Biases.
Deployment Pipelines
Implement CI/CD pipelines to deploy models to production using Docker and Kubernetes.
Real-World Applications
Deploy models to cloud platforms like AWS SageMaker, Azure ML, and Google AI Platform.
Course Outline
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Frequently Asked Questions
What is MLOps, and why is it important?
MLOps, or Machine Learning Operations, is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. It combines machine learning (ML) and DevOps (development and operations) principles to ensure models are scalable, reproducible, and maintainable.
What types of tools will I learn to use in this course?
You will learn about different tools essential for MLOps, including Apache Airflow for data pipelines, MLflow for experiment tracking, Docker for containerization, Kubernetes for orchestration, and Prometheus and Grafana for monitoring.
How will this course help me in my machine learning projects?
This course provides a comprehensive understanding of MLOps, covering the entire machine learning lifecycle from data collection to model deployment and monitoring. You will gain practical skills to implement MLOps practices, ensuring your models are robust and scalable in production.
What kind of projects will I work on in this course?
You will work on projects such as developing data pipelines, creating model training workflows, implementing CI/CD pipelines for model deployment, and setting up monitoring dashboards.
How do I monitor the performance of deployed models?
The course briefly covers tools and techniques for monitoring deployed models, including using Prometheus for real-time monitoring and Grafana for creating interactive dashboards. These tools help track key metrics like prediction accuracy, latency, and resource usage.
MLops Course Description PDF
Download a copy of this course's description PDF
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Hands-On Learning
Learn by doing! Our AI school equips you with practical, real-world skills to apply AI concepts effectively. Success is measured by your achievements and your ability to solve real-life challenges.
Engaging Learning Materials
Enjoy a variety of interactive content, including video lessons, coding walkthroughs, eBooks, audiobooks, explainer videos, animated videos, and SCORM materials. These high-quality resources are designed to make learning both engaging and efficient.
Your Success, Our Priority
Your success drives us. Our programs give you the tools and strategies to thrive in the fast-changing world of AI. Learn to create AI solutions that deliver real value.
Crafted by Experts
AI Degree is forged by a team of visionaries—experts in computer science, cybersecurity, machine learning, and education. Driven by innovation, they blend real-world expertise with academic rigor. Together, the team's aim is to provide a transformative learning experience—your gateway to mastering AI, a skill that will shape the future and set you apart in a tech-driven world.
