MLOps (Machine Learning Operations) Fundamentals
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Approx. 16 hours to complete
Course Summary
This course covers the fundamentals of MLOps, a practice that combines machine learning and operations to create a streamlined process for developing and deploying machine learning models at scale. You will learn about the tools and techniques used in MLOps and gain hands-on experience through practical exercises.Key Learning Points
- Understand the principles of MLOps and its benefits
- Learn how to use tools like Docker, Kubernetes, and Git for MLOps
- Explore techniques for monitoring and debugging machine learning models
- Gain experience with developing and deploying machine learning models at scale
Job Positions & Salaries of people who have taken this course might have
- Machine Learning Engineer
- USA: $120,000 - $160,000
- India: ₹1,000,000 - ₹2,000,000
- Spain: €45,000 - €60,000
- Data Engineer
- USA: $100,000 - $140,000
- India: ₹700,000 - ₹1,500,000
- Spain: €30,000 - €50,000
- DevOps Engineer
- USA: $100,000 - $140,000
- India: ₹700,000 - ₹1,500,000
- Spain: €30,000 - €50,000
Related Topics for further study
Learning Outcomes
- Understand the principles and benefits of MLOps
- Learn how to use tools like Docker, Kubernetes, and Git for MLOps
- Gain hands-on experience with developing and deploying machine learning models at scale
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of machine learning concepts
- Familiarity with Python programming language
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Practical
- Hands-on
- Project-based
Similar Courses
- Data Science Essentials
- Applied Machine Learning
- Introduction to Deep Learning
Related Education Paths
Notable People in This Field
- Founder of deeplearning.ai
- Founder of Fast Forward Labs
Related Books
Description
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
Knowledge
- Identify and use core technologies required to support effective MLOps.
- Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
- Adopt the best CI/CD practices in the context of ML systems.
- Implement reliable and repeatable training and inference workflows.
Outline
- Welcome to MLOps Fundamentals
- Course Introduction
- How to download course resources
- How to Send Feedback
- Why and When do we need MLOps
- Data Scientists’ Pain Points
- Machine Learning Lifecycle
- MLOps Architecture and TensorFlow Extended Components
- Why and When to Employ MLOps
- Understanding the Main Kubernetes Components (Optional)
- Introduction
- Introduction to Containers
- Containers and Container Images
- Lab Intro
- Lab solution
- Introduction to Kubernetes
- Introduction to Google Kubernetes Engine
- Compute Options Detail
- Kubernetes Concepts
- The Kubernetes Control Plane
- Google Kubernetes Engine Concepts
- Lab Intro
- Lab solution
- Deployments
- Ways to Create Deployments
- Services and Scaling
- Updating Deployments
- Rolling Updates
- Blue-Green Deployments
- Canary Deployments
- Managing Deployments
- Lab Intro
- Jobs and CronJobs
- Parallel Jobs
- CronJobs
- Introduction to Containers
- Containers and Container Images
- Introduction to Kubernetes
- Introduction to Google Kubernetes Engine
- Containers and Kubernetes in Google Cloud
- Kubernetes Concepts
- The Kubernetes Control Plane
- Google Kubernetes Engine Concepts
- Deployments
- Updating Deployments
- Jobs
- Introduction to AI Platform Pipelines
- Overview
- Introduction to AI Platform Pipelines
- Concepts
- When to use
- Ecosystem
- Getting Started with Google Cloud and Qwiklabs
- Lab Solution
- AI Platform Pipelines
- Training, Tuning and Serving on AI Platform
- System and concepts overview
- Create a reproducible dataset
- Implement a tunable model
- Build and push a training container
- Train and tune the model
- Serve and query the model
- Lab Intro
- Lab Solution
- Training, Tuning and Serving on AI Platform
- Kubeflow Pipelines on AI Platform
- System and concept overview
- Describing a Kubeflow Pipeline with KF DSL
- Pre-built components
- Lightweight Python Components
- Custom components
- Compile, upload and Run
- Lab Intro
- Lab Solution
- Kubeflow Pipelines on AI Platform
- CI/CD for Kubeflow Pipelines on AI Platform
- Concept Overview
- Cloud Build Builders
- Cloud Build Configuration
- Cloud Build Triggers
- Lab Intro
- CI/CD for a Kubeflow Pipeline
- Summary
- Summary
Summary of User Reviews
Discover the fundamentals of MLOps with this course from Coursera. Students have praised the course for its comprehensive coverage of the subject matter and practical approach to learning. Overall, the course has received positive reviews from users.Key Aspect Users Liked About This Course
practical approach to learningPros from User Reviews
- Comprehensive coverage of MLOps
- Hands-on assignments and projects
- Expert instructors with industry experience
Cons from User Reviews
- Some users found the course to be challenging
- Limited interaction with course instructors
- Course content may be too basic for advanced users
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