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Deploying Machine Learning Models

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.

This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

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What's inside

Syllabus

Introduction

Welcome to the first week of Deploying Machine Learning Models! We will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of recommender systems and differentiate it from other types of machine learning

Implementing Recommender Systems

This week, we will learn how to implement a similarity-based recommender, returning predictions similar to an user's given item. We will cover how to optimize these models based on gradient descent and Jaccard similarity.

Deploying Recommender Systems

This week, we will learn about Python web server frameworks and the overall structure of interactive Python data applications. We will also cover some tips for best practices on deploying and monitoring your applications.

Project 4: Recommender System

For this final project, you will build a recommender system of your own. Find a dataset, clean it, and create a predictive system from the dataset. This will help prepare you for the upcoming capstone, where you will harness your skills from all courses of this specialization into one single project!

Capstone

Time to put all your hard work to the test! This capstone project consists of four components, each drawing from a separate course in this specialization. It's time to show off everything you've learned from this specialization.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches standard industry technology

Develops skills that are directly applicable in professional settings and projects

Provides practice in implementing and deploying your models in the real world

Builds on concepts and skills learned in the previous courses in the specialization

Course content is relevant to both academic and professional contexts

Suitable for learners of all levels, thanks to its real-world hands-on approach

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Reviews summary

Deployment of machine learning models

Learners say this course can be summarized as a good course with a focus on building recommender systems. The grading process is reliant on others, which can be a negative experience. Students notice a lack of participants which can make peer grading a slow process. Other complaints surround a cursory approach to the more complex topics in the course. It is unclear if this course delivers what the title suggests, as some state that what is offered is not reflected in the title.

Grading relies on peers

"The evaluation process is terrible, it depends on someone that can evaluate your project"

"Some of the questions in the quizzes were not so useful in my opinion"

Projects in this course can be too complex

"Recommender system is complex and need more in depth teaching"

"Topics are hastily rushed"

"You need to find a dataset to answer final capstone project which can be hard to find to fulfill the grading criteria"

"Deployment was not even taught. misleading title, underwhelming content."

Content is misleading

"Deployment was not even taught. misleading title, underwhelming content"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deploying Machine Learning Models with these activities:

Review Python basics and data structures

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Ensure a strong foundation by refreshing your knowledge of Python programming fundamentals and commonly used data structures.

Browse courses on Python Basics

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Go through an online tutorial or review Python documentation to refresh your understanding of data types, operators, and control flow.
Practice working with lists, dictionaries, and sets to reinforce your understanding of data structures.

Review the Primer on Machine Learning by Alpaydin

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Understand the fundamental concepts of machine learning, such as supervised and unsupervised learning, feature selection, model evaluation, and overfitting.

View Machine Learning, revised and updated edition... on Amazon

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Read Chapters 1-3 to establish a strong foundation in the basics of machine learning.
Work through the practice problems at the end of each chapter to reinforce your understanding.
Consider creating a simple machine learning model using a programming language of your choice.

Follow the Scikit-learn tutorials

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Gain hands-on experience with a popular Python library for machine learning, covering topics such as data preprocessing, model training, and evaluation.

Browse courses on scikit-learn

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Complete the Scikit-learn tutorial on data preprocessing.
Walk through the tutorial on model training and evaluation for a specific machine learning task.

Three other activities

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Solve practice problems on LeetCode or Kaggle

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Develop your problem-solving and coding skills by working through practice problems related to recommender systems and data analysis.

Browse courses on Coding Challenges

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Choose a problem set focused on recommender systems or data manipulation.
Attempt to solve the problems on your own, referring to documentation or online resources as needed.
Review your solutions and identify areas for improvement.

Write a blog post or article on best practices for recommender system evaluation

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Enhance your critical thinking and communication skills by writing a blog post or article that shares your insights on best practices for evaluating the effectiveness of recommender systems.

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Research and gather information on evaluation metrics and techniques.
Develop a clear and concise outline for your blog post or article.
Write and edit your content, ensuring clarity and flow.

Attend a workshop on recommender system deployment

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Gain insights from experts and engage in hands-on exercises to learn best practices for deploying and monitoring recommender systems in real-world environments.

Browse courses on Machine Learning Deployment

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Research and identify a relevant workshop.
Register and attend the workshop.
Actively participate in the sessions and ask questions.

Career center

Learners who complete Deploying Machine Learning Models will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, you will use your knowledge of machine learning models to help businesses make better decisions. This course will teach you how to deploy machine learning models, which is a critical skill for Data Scientists. By taking this course, you will be able to build and deploy machine learning models that can help businesses improve their operations and make more informed decisions.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

As a Machine Learning Engineer, you will use your knowledge of machine learning models to design and develop machine learning systems. This course will teach you how to deploy machine learning models, which is a critical skill for Machine Learning Engineers. By taking this course, you will be able to build and deploy machine learning models that can solve real-world problems.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

As a Software Engineer, you will use your knowledge of machine learning models to develop software applications. This course will teach you how to deploy machine learning models, which is a critical skill for Software Engineers. By taking this course, you will be able to build and deploy machine learning models that can improve the functionality of software applications.

See salaries and explore the career path for Software Engineer

Data Analyst

As a Data Analyst, you will use your knowledge of machine learning models to analyze data and extract insights. This course will teach you how to deploy machine learning models, which is a critical skill for Data Analysts. By taking this course, you will be able to build and deploy machine learning models that can help businesses make better decisions.

See salaries and explore the career path for Data Analyst

Business Analyst

As a Business Analyst, you will use your knowledge of machine learning models to help businesses improve their operations. This course will teach you how to deploy machine learning models, which is a critical skill for Business Analysts. By taking this course, you will be able to build and deploy machine learning models that can help businesses make better decisions.

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Product Manager

As a Product Manager, you will use your knowledge of machine learning models to develop and manage products. This course will teach you how to deploy machine learning models, which is a critical skill for Product Managers. By taking this course, you will be able to build and deploy machine learning models that can improve the functionality of products.

See salaries and explore the career path for Product Manager

Project Manager

As a Project Manager, you will use your knowledge of machine learning models to manage projects. This course will teach you how to deploy machine learning models, which is a critical skill for Project Managers. By taking this course, you will be able to build and deploy machine learning models that can help projects succeed.

See salaries and explore the career path for Project Manager

Data Engineer

As a Data Engineer, you will use your knowledge of machine learning models to build and manage data pipelines. This course will teach you how to deploy machine learning models, which is a critical skill for Data Engineers. By taking this course, you will be able to build and deploy machine learning models that can improve the efficiency of data pipelines.

See salaries and explore the career path for Data Engineer

Database Administrator

As a Database Administrator, you will use your knowledge of machine learning models to manage databases. This course will teach you how to deploy machine learning models, which is a critical skill for Database Administrators. By taking this course, you will be able to build and deploy machine learning models that can improve the performance of databases.

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System Administrator

As a System Administrator, you will use your knowledge of machine learning models to manage systems. This course will teach you how to deploy machine learning models, which is a critical skill for System Administrators. By taking this course, you will be able to build and deploy machine learning models that can improve the performance of systems.

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Network Administrator

As a Network Administrator, you will use your knowledge of machine learning models to manage networks. This course will teach you how to deploy machine learning models, which is a critical skill for Network Administrators. By taking this course, you will be able to build and deploy machine learning models that can improve the performance of networks.

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Security Analyst

As a Security Analyst, you will use your knowledge of machine learning models to analyze security data and identify threats. This course will teach you how to deploy machine learning models, which is a critical skill for Security Analysts. By taking this course, you will be able to build and deploy machine learning models that can help protect organizations from cyberattacks.

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Risk Analyst

As a Risk Analyst, you will use your knowledge of machine learning models to identify and assess risks. This course will teach you how to deploy machine learning models, which is a critical skill for Risk Analysts. By taking this course, you will be able to build and deploy machine learning models that can help organizations make better decisions.

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Compliance Analyst

As a Compliance Analyst, you will use your knowledge of machine learning models to ensure that organizations comply with regulations. This course will teach you how to deploy machine learning models, which is a critical skill for Compliance Analysts. By taking this course, you will be able to build and deploy machine learning models that can help organizations meet regulatory requirements.

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Auditor

As an Auditor, you will use your knowledge of machine learning models to audit organizations' financial statements. This course will teach you how to deploy machine learning models, which is a critical skill for Auditors. By taking this course, you will be able to build and deploy machine learning models that can help auditors detect fraud and errors.

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Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Deploying Machine Learning Models.

Hands-On Machine Learning with Scikit-Learn, Keras,...

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Provides a practical guide to machine learning using Python and the scikit-learn, Keras, and TensorFlow libraries. It covers a wide range of topics, from data preparation and feature engineering to model selection and evaluation.

Hands-On Machine Learning with Scikit-Learn, Keras,...

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Deploying Machine Learning Models

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Deployment of machine learning models

Activities

Career center

Reading list

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