Course Summary
Learn how to deploy machine learning models using various tools and platforms, including Docker, Kubernetes, and AWS SageMaker.Key Learning Points
- Understand the different ways to deploy machine learning models in real-world scenarios
- Learn how to use Docker and Kubernetes to deploy models in a scalable and efficient way
- Get hands-on experience with AWS SageMaker and other cloud platforms
Job Positions & Salaries of people who have taken this course might have
- USA: $112,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $112,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $96,000
- India: ₹900,000
- Spain: €35,000
- USA: $112,000
- India: ₹1,000,000
- Spain: €45,000
- USA: $96,000
- India: ₹900,000
- Spain: €35,000
- USA: $120,000
- India: ₹1,200,000
- Spain: €50,000
Related Topics for further study
- Deploying machine learning models
- Docker and Kubernetes
- AWS SageMaker
- Cloud platforms
- Scalable and efficient deployment
Learning Outcomes
- Understand the different ways to deploy machine learning models in the real world
- Learn how to use Docker and Kubernetes to deploy models in a scalable and efficient way
- Gain hands-on experience with AWS SageMaker and other cloud platforms
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of machine learning concepts and programming languages such as Python
- Access to a computer with internet connection
Course Difficulty Level
IntermediateCourse Format
- Online Self-paced Course
- Video Lectures
- Hands-on Exercises
Similar Courses
- Practical Machine Learning with TensorFlow
- Applied Data Science with Python
Notable People in This Field
- Andrew Ng
- Yann LeCun
Related Books
Description
In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets.
Knowledge
- Project structure of interactive Python data applications
- Python web server frameworks: (e.g.) Flask, Django, Dash
- Best practices around deploying ML models and monitoring performance
- Deployment scripts, serializing models, APIs
Outline
- Introduction
- Introduction to Recommender Systems
- Recommender Systems versus Other Forms of Supervised Learning
- Collaborative Filtering-Based Recommendation
- Latent Factor Models (Part 1)
- Latent Factor Models (Part 2)
- Syllabus
- Course Materials
- Setting Up Your System
- Review: Recommender Systems
- Review: Introduction to Latent Factor Models
- Recommender Systems and Latent Factor Models
- Implementing Recommender Systems
- Implementing a Similarity-Based Recommender
- Similarity-Based Recommender for Rating Prediction
- Implementing a Latent Factor Model (Part 1)
- Implementing a Latent Factor Model (Part 2)
- Review: Similarity-Based Recommenders
- Review: Implementing Latent Factor Models
- Implementing Recommender Systems
- Deploying Recommender Systems
- Intro to Web Server Frameworks (in Python)
- Intro to Django
- Flask
- Setting up Your Workspace with Docker: Django
- Review: Flask and Django
- Deploying Recommender Systems
- Project 4: Recommender System
- Project Description
- How to Find a Dataset
- Capstone
- Description of Capstone Tasks
- Capstone Overview
Summary of User Reviews
Discover how to deploy machine learning models with this comprehensive Coursera course. Users have praised the practicality of the lessons, which have helped them apply the knowledge directly to their work. Overall, the course has received high ratings from students.Key Aspect Users Liked About This Course
The practicality of the lessons.Pros from User Reviews
- Great practical examples
- Real-world applications
- Engaging instructors
- Excellent resources
- Good pace
Cons from User Reviews
- Some sections could be more detailed
- Limited focus on certain technologies
- Not suitable for beginners
- Some lectures are too short
- Can be repetitive
Ilkay Altintas
3.5 Raiting
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