Managing Machine Learning Projects from Coursera

This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.

At the conclusion of this course, you should be able to:

1) Identify opportunities to apply ML to solve problems for users

2) Apply the data science process to organize ML projects

3) Evaluate the key technology decisions to make in ML system design

4) Lead ML projects from ideation through production using best practices

What's inside

Syllabus

Identifying Opportunities for Machine Learning

In this module we will discuss how to identify problems worth solving, how to determine whether ML is a good fit as part of the solution, and how to validate solution concepts. We will also learn why heuristics are useful in modeling projects and the advantages and disadvantages of ML relative to heuristics.

Organizing ML Projects

In this module we will focus on the CRISP-DM data science process and how it can be used to organize ML projects. We will begin by understanding what is unique about ML project relative to normal software projects, and then discuss approaches to manage the inherent risks of ML projects. We will also walk through the key roles on a ML project team and how to organize work.

Data Considerations

In this module we will explore the key data-related issues that arise in ML projects. Data is the foundation of successful machine learning, and gathering data of sufficient quantity and quality with the right set of attributes is the key to a successful project. We will discuss the key considerations in sourcing data, cleaning data, and developing and selecting a feature set to use in modeling. The module will conclude with a discussion on best practices to ensure reproducibility of your data pipeline.

ML System Design & Technology Selection

In this module we will discuss the key decisions to make in designing ML systems, such as cloud vs. edge and online vs. batch, and compare the benefits of each type of system. We will then discuss the primary technology decisions to make in a ML project and introduce the common tools and technologies used to build ML models.

Model Lifecycle Management

The final module in the course focuses on identifying and mitigating the key issues which ML models experience once they are in production. We will discuss how to set up a robust ML system monitoring capability and define a model maintenance plan to maintain high performance of a production model. We will conclude with a discussion on the importance of versioning in ML systems to facilitate continued rapid iteration even after deployment.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces tools and methods that are at the forefront of industry practices

Taught by a recognized expert from a prominent educational institution

Covers essential concepts and techniques for managing ML projects

Provides hands-on exercises and case studies for practical application

May require familiarity with data analysis and programming concepts

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 Managing Machine Learning Projects with these activities:

Review data science and machine learning concepts

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Start the course with a refreshed understanding of fundamental data science and machine learning concepts.

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Review introductory textbooks or online resources on data science and machine learning.
Complete practice problems or exercises to reinforce your understanding.

Refresh Probability Theory

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Understanding probability theory will improve your ability to understand the theory behind the machine learning models you will learn in the course.

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Review the basics of probability distributions: binomial, normal, Poisson
Review concepts of Bayesian statistics: Bayes' theorem, conjugate priors
Practice solving probability problems involving conditional probability and Bayes' theorem

Connect with experienced machine learning practitioners

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Seek guidance and advice from experts in the field to accelerate your learning.

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Attend industry events and meetups to network with machine learning professionals.
Reach out to individuals whose work or experience aligns with your interests.
Clearly articulate your goals and how their mentorship can support your growth.

Seven other activities

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Practice Machine Learning Algorithms

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You will need to write code throughout this course. This practice will get you ready.

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Implement linear regression from scratch in Python
Implement logistic regression from scratch
Implement a decision tree from scratch
Implement a k-means clustering algorithm from scratch
Implement a neural network from scratch

Solve machine learning coding challenges

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Sharpen your machine learning coding skills and problem-solving abilities.

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Identify reputable online platforms or resources for machine learning coding challenges.
Select problems that align with course topics and your skill level.
Allocate dedicated time for practicing and solving these challenges.

Attend machine learning workshops or conferences

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Expand your knowledge and network by attending industry events focused on machine learning.

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Identify reputable machine learning workshops or conferences that align with your interests and learning goals.
Register and actively participate in the sessions, taking notes and engaging in discussions.
Network with other attendees and speakers to exchange ideas and insights.

Follow online tutorials on machine learning best practices

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Enhance your machine learning development skills by learning industry best practices.

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Explore reputable online platforms or resources for machine learning tutorials.
Select tutorials that cover specific best practices or techniques relevant to the course.
Follow the tutorials diligently, taking notes and practicing the concepts.

Develop a machine learning deployment plan

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Gain practical experience in planning and executing the deployment of machine learning models.

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Choose a specific machine learning model and use case for your deployment plan.
Research and select appropriate tools and technologies for deployment.
Design and document a detailed deployment plan, including infrastructure, monitoring, and maintenance.

Develop a portfolio of machine learning projects

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Showcase your machine learning skills and understanding by creating a portfolio of practical projects.

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Identify real-world problems or datasets that can be addressed using machine learning.
Design and implement machine learning models to solve these problems.
Write detailed documentation and present your results effectively.

Participate in machine learning competitions

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Apply your machine learning knowledge and skills in a competitive environment.

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Identify relevant machine learning competitions that align with your interests and skill level.
Carefully read and understand the competition rules and guidelines.
Form a team or work individually to develop and submit your solutions.
Analyze your results and learn from the feedback and insights gained.

Career center

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

Machine Learning Engineer

Machine Learning Engineers are responsible for building and maintaining machine learning models. They work closely with Data Scientists to develop and implement solutions to business problems. This course provides a deep understanding of the machine learning system design process, which is essential for success in this role.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning to solve a variety of business problems. They may work on projects such as developing new products, improving customer service, or reducing costs. This course provides a strong foundation in the machine learning process, which will be essential for success in this role.

See salaries and explore the career path for Data Scientist

Machine Learning Consultant

Machine Learning Consultants provide machine learning services to clients. They work with clients to identify and solve business problems using machine learning techniques. This course provides a strong foundation in the machine learning consulting process, which is essential for success in this role.

See salaries and explore the career path for Machine Learning Consultant

Data Analyst

Data Analysts use data to identify trends and patterns. They work with stakeholders to develop and implement solutions to business problems. This course provides a strong foundation in the data science process, which is essential for success in this role.

See salaries and explore the career path for Data Analyst

Data Engineer

Data Engineers are responsible for designing, building, and maintaining data pipelines. They work with stakeholders to develop and implement solutions to data-related problems. This course provides a strong foundation in the data engineering process, which is essential for success in this role.

See salaries and explore the career path for Data Engineer

Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and techniques. They work with stakeholders to develop and implement solutions to business problems. This course provides a strong foundation in the machine learning research process, which is essential for success in this role.

See salaries and explore the career path for Machine Learning Researcher

AI Architect

AI Architects are responsible for designing and implementing AI solutions. They work with stakeholders to develop and implement solutions to business problems. This course provides a strong foundation in the AI architecture process, which is essential for success in this role.

See salaries and explore the career path for AI Architect

Data Science Consultant

Data Science Consultants provide data science services to clients. They work with clients to identify and solve business problems using data science techniques. This course provides a strong foundation in the data science consulting process, which is essential for success in this role.

See salaries and explore the career path for Data Science Consultant

Agile Coach

Agile Coaches help organizations adopt and implement agile practices. They work with teams to define and manage agile processes, and remove roadblocks. This course provides a strong foundation in agile principles and practices, which is essential for success in this role.

See salaries and explore the career path for Agile Coach

Product Owner

Product Owners are responsible for defining and managing the development of new products. They work with stakeholders to define product requirements, develop marketing strategies, and manage product development. This course provides a strong foundation in the product development process, which is essential for success in this role.

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Business Analyst

Business Analysts work with stakeholders to identify and solve business problems. They may develop and implement solutions such as new products, processes, or systems. This course provides a strong foundation in the business analysis process, which is essential for success in this role.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They may work on projects such as developing new products, improving customer service, or reducing costs. This course provides a solid understanding of the software development process, which is essential for success in this role.

See salaries and explore the career path for Software Engineer

Scrum Master

Scrum Masters are responsible for facilitating agile development teams. They work with teams to define and manage sprints, and remove roadblocks. This course provides a strong foundation in the scrum process, which is essential for success in this role.

See salaries and explore the career path for Scrum Master

Product Manager

Product Managers are responsible for the development and launch of new products. They work with stakeholders to define product requirements, develop marketing strategies, and manage product development. This course provides a strong foundation in the product development process, which is essential for success in this role.

See salaries and explore the career path for Product Manager

Project Manager

Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to define project scope, develop project plans, and manage project resources. This course provides a strong foundation in the project management process, which is essential for success in this role.

See salaries and explore the career path for Project Manager