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Noah Gift
and
Alfredo Deza
Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML.
Read more
Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML.
Read more
Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML.
By the end of the course, you will be able to use web frameworks (e.g., Gradio and Hugging Face) for ML solutions, build a command-line tool using the Click framework, and leverage Rust for GPU-accelerated ML tasks.
Week 1: Explore MLOps technologies and pre-trained models to solve problems for customers.
Week 2: Apply ML and AI in practice through optimization, heuristics, and simulations.
Week 3: Develop operations pipelines, including DevOps, DataOps, and MLOps, with Github.
Week 4: Build containers for ML and package solutions in a uniformed manner to enable deployment in Cloud systems that accept containers.
Week 5: Switch from Python to Rust to build solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and MLOps.
Register for this course and see more details by visiting:
OpenCourser.com/course/ywzncx/devops
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What's inside
Syllabus
Week 1: Introduction to MLOps
This week you will learn how to apply foundational skills in MLOps to build machine learning solutions and apply it by building microservices in Python.
Read more
Syllabus
Week 1: Introduction to MLOps
This week you will learn how to apply foundational skills in MLOps to build machine learning solutions and apply it by building microservices in Python.
Read more
Week 2: Essential Math and Data Science
This week you will learn how to apply essential skills in math and data science for MLOps and apply it by building simulations.
Week 3: Operations Pipelines: DevOps, DataOps, MLOps
This week you will learn how to build operations pipelines and then apply these skills by building solutions for pre-trained Hugging Face models.
End to End MLOps and AIOps
This week you will learn how to build end to end MLOps and AIOps solutions and apply it by building solutions with pre-trained models from OpenAI while benefiting from using AI Pair Programming tools like GitHub Copilot.
Rust for MLOps: The Practical Transition from Python to Rust
This week, you will learn how to switch from Python to Rust, a powerful and efficient systems programming language. This week will cover various practical applications of Rust, such as CLI, Web, and MLOps solutions, as well as cloud computing solutions for AWS, GCP, and Azure. You'll also learn how to build Rust solutions for Kubernetes, Docker, Serverless, Data Engineering, Data Science, and Machine Learning Operations (MLOps). By the end of this week, you will have a strong understanding of Rust's key syntax and features, and be able to leverage Rust for GPU-accelerated machine learning tasks.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores MLOps technologies and strategies, which is standard industry practice
Provides foundational skills in Python, math, and data science, which are core skills for many data-centric careers
Focuses on practical applications of MLOps, DevOps, DataOps, and AIOps
Integrates with GitHub, Hugging Face, and OpenAI, which are widely used tools in the MLOps industry
Covers Rust, a language gaining traction in MLOps for its efficiency in systems programming
Teaches end-to-end MLOps solutions, preparing learners for real-world MLOps pipelines
Save this course
Save DevOps, DataOps, MLOps to your list so you can find it easily later:
Save
Save
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 DevOps, DataOps, MLOps with these
activities:
Review essential math and data science concepts
Show steps
A quick review solidifies essential knowledge and prepares you for the course's quantitative aspects.
Browse courses on
Math
Show steps
-
Review online resources or textbooks on math and data science
-
Focus on concepts such as statistics, probability, and linear algebra
-
Practice solving problems and applying these concepts
Install Python with PyPI
Show steps
This activity will help prepare you to run the code and follow along with the course.
Show steps
-
Follow the official Python installation guide
Brush up on Python syntax
Show steps
A quick Python syntax review ensures that you're ready to hit the ground running when the course begins.
Browse courses on
Python
Show steps
-
Review online tutorials
-
Refer to Python documentation
-
Practice writing simple Python programs
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Exercises on Jupyter Notebooks
Show steps
Regular practice on Jupyter Notebooks will help you build a strong foundation in Python and machine learning.
Browse courses on
Python
Show steps
-
Follow the tutorials on Jupyter Notebooks
-
Attempt solving beginner-level exercises
-
Participate in online forums for Q&A
Gather resources on MLOps best practices
Show steps
A curated collection of MLOps resources serves as a valuable reference throughout the course.
Browse courses on
MLOps
Show steps
-
Identify and collect articles, tutorials, and documentation on MLOps best practices
-
Organize the resources into a central repository or online collection
-
Share the compilation with peers or contribute it to an online community
Build a Simple Machine Learning Model in Python
Show steps
These tutorials will give you hands-on experience in building basic machine learning models, which is essential for this course.
Browse courses on
Python
Show steps
-
Follow a tutorial on building a simple machine learning model in Python
-
Experiment with different models and algorithms
-
Share your results and insights online
Collaborate on MLOps projects
Show steps
Peer collaboration enhances problem-solving and deepens understanding of MLOps concepts.
Browse courses on
MLOps
Show steps
-
Form study groups with peers
-
Identify and collaborate on MLOps projects
-
Share insights and provide constructive feedback
Discussion on Metrics for Machine Learning Models
Show steps
Engage in discussions with peers to develop a deep understanding of different metrics used to evaluate machine learning models.
Browse courses on
Machine Learning
Show steps
-
Join a study group or online forum
-
Participate in discussions on best practices
-
Share your knowledge and insights
Practice MLOps pipeline optimization
Show steps
Additional MLOps practice enhances problem-solving skills and reinforces concepts.
Browse courses on
MLOps
Show steps
-
Identify areas for optimization in MLOps pipelines
-
Implement optimization techniques using Python or Rust
-
Test and evaluate the optimized pipelines
Explore Rust programming for MLOps
Show steps
Targeted Rust tutorials complement the course's transition from Python to Rust.
Browse courses on
Rust
Show steps
-
Identify suitable Rust tutorials for MLOps
-
Follow the tutorials and implement Rust solutions for MLOps tasks
-
Experiment with Rust features for GPU-accelerated ML
Contribute to Open-Source MLOps Projects
Show steps
Contribute to open-source MLOps projects to gain practical experience and enhance your portfolio.
Browse courses on
MLOps
Show steps
-
Identify open-source MLOps projects that align with your interests
-
Start contributing by fixing bugs or adding features
-
Collaborate with other developers and maintainers
Build a containerized ML solution
Show steps
Hands-on containerization experience solidifies understanding of deployment and packaging.
Browse courses on
Containerization
Show steps
-
Design the ML solution architecture
-
Implement the solution using appropriate frameworks and tools
-
Containerize the solution using Docker or Kubernetes
-
Deploy and test the containerized solution
Develop an MLOps solution for a real-world problem
Show steps
A comprehensive MLOps project provides a practical application of skills and reinforces learning.
Browse courses on
MLOps
Show steps
-
Define the real-world problem and identify the appropriate solution
-
Design and implement the MLOps solution using best practices
-
Evaluate the solution's performance and make necessary adjustments
-
Document the project and present your findings
Review essential math and data science concepts
Show steps
A quick review solidifies essential knowledge and prepares you for the course's quantitative aspects.
Browse courses on
Math
Show steps
Show steps
Show steps
- Review online resources or textbooks on math and data science
- Focus on concepts such as statistics, probability, and linear algebra
- Practice solving problems and applying these concepts
Install Python with PyPI
Show steps
This activity will help prepare you to run the code and follow along with the course.
Show steps
Show steps
Show steps
- Follow the official Python installation guide
Brush up on Python syntax
Show steps
A quick Python syntax review ensures that you're ready to hit the ground running when the course begins.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review online tutorials
- Refer to Python documentation
- Practice writing simple Python programs
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Exercises on Jupyter Notebooks
Show steps
Regular practice on Jupyter Notebooks will help you build a strong foundation in Python and machine learning.
Browse courses on
Python
Show steps
Show steps
Show steps
- Follow the tutorials on Jupyter Notebooks
- Attempt solving beginner-level exercises
- Participate in online forums for Q&A
Gather resources on MLOps best practices
Show steps
A curated collection of MLOps resources serves as a valuable reference throughout the course.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Identify and collect articles, tutorials, and documentation on MLOps best practices
- Organize the resources into a central repository or online collection
- Share the compilation with peers or contribute it to an online community
Build a Simple Machine Learning Model in Python
Show steps
These tutorials will give you hands-on experience in building basic machine learning models, which is essential for this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Follow a tutorial on building a simple machine learning model in Python
- Experiment with different models and algorithms
- Share your results and insights online
Collaborate on MLOps projects
Show steps
Peer collaboration enhances problem-solving and deepens understanding of MLOps concepts.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Form study groups with peers
- Identify and collaborate on MLOps projects
- Share insights and provide constructive feedback
Discussion on Metrics for Machine Learning Models
Show steps
Engage in discussions with peers to develop a deep understanding of different metrics used to evaluate machine learning models.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join a study group or online forum
- Participate in discussions on best practices
- Share your knowledge and insights
Practice MLOps pipeline optimization
Show steps
Additional MLOps practice enhances problem-solving skills and reinforces concepts.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Identify areas for optimization in MLOps pipelines
- Implement optimization techniques using Python or Rust
- Test and evaluate the optimized pipelines
Explore Rust programming for MLOps
Show steps
Targeted Rust tutorials complement the course's transition from Python to Rust.
Browse courses on
Rust
Show steps
Show steps
Show steps
- Identify suitable Rust tutorials for MLOps
- Follow the tutorials and implement Rust solutions for MLOps tasks
- Experiment with Rust features for GPU-accelerated ML
Contribute to Open-Source MLOps Projects
Show steps
Contribute to open-source MLOps projects to gain practical experience and enhance your portfolio.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Identify open-source MLOps projects that align with your interests
- Start contributing by fixing bugs or adding features
- Collaborate with other developers and maintainers
Build a containerized ML solution
Show steps
Hands-on containerization experience solidifies understanding of deployment and packaging.
Browse courses on
Containerization
Show steps
Show steps
Show steps
- Design the ML solution architecture
- Implement the solution using appropriate frameworks and tools
- Containerize the solution using Docker or Kubernetes
- Deploy and test the containerized solution
Develop an MLOps solution for a real-world problem
Show steps
A comprehensive MLOps project provides a practical application of skills and reinforces learning.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- Define the real-world problem and identify the appropriate solution
- Design and implement the MLOps solution using best practices
- Evaluate the solution's performance and make necessary adjustments
- Document the project and present your findings
Career center
Learners who complete DevOps, DataOps, MLOps will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning and other techniques to extract insights from data. This course will help you develop the skills needed to succeed in this role, including data analysis, machine learning, and cloud computing.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and maintaining machine learning models. This course will help you build a foundation in the skills and knowledge needed to succeed in this role, including model building, data analysis, and cloud computing.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. This course will help you develop the skills needed to succeed in this role, including continuous integration, continuous delivery, and cloud computing.
Data Analyst
Data Analyst
Data Analysts use data to make informed decisions. This course will help you develop the skills needed to succeed in this role, including data analysis, data visualization, and cloud computing.
Cloud Architect
Cloud Architect
Cloud Architects design, develop, and maintain cloud computing systems. This course will help you develop the skills needed to succeed in this role, including cloud computing, networking, and security.
AI Engineer
AI Engineer
AI Engineers design, develop, and maintain artificial intelligence systems. This course will help you develop the skills needed to succeed in this role, including machine learning, deep learning, and natural language processing.
Big Data Engineer
Big Data Engineer
Big Data Engineers work with large datasets to extract insights. This course will help you develop the skills needed to succeed in this role, including data analysis, data mining, and cloud computing.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course will help you build a foundation in the skills and knowledge needed to succeed in this role, including object-oriented programming, data structures, and algorithms.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course will help you develop the skills needed to succeed in this role, including data engineering, cloud computing, and machine learning.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify and solve problems. This course will help you develop the skills needed to succeed in this role, including data analysis, problem solving, and cloud computing.
Product Manager
Product Manager
Product Managers work with teams to develop and launch new products. This course will help you develop the skills needed to succeed in this role, including data analysis, product management, and cloud computing.
Project Manager
Project Manager
Project Managers work with teams to plan and execute projects. This course will help you develop the skills needed to succeed in this role, including project management, communication, and cloud computing.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to make investment decisions. This course will help you develop the skills needed to succeed in this role, including machine learning, data analysis, and cloud computing.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including machine learning, artificial intelligence, and cloud computing. This course will help you develop the skills needed to succeed in this role, including research methods, data analysis, and cloud computing.
Systems Engineer
Systems Engineer
Systems Engineers design, build, and maintain computer systems. This course will help you develop the skills needed to succeed in this role, including systems engineering, cloud computing, and machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/ywzncx/devops
Reading list
We've selected 11 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
DevOps, DataOps, MLOps.
Is the official Rust programming language documentation, providing a comprehensive and authoritative reference for Rust syntax, semantics, and libraries. It is an essential resource for anyone who wants to learn Rust or use it in their projects.
Save
Provides a comprehensive overview of deep learning, covering the fundamental concepts and algorithms of deep learning. It valuable resource for anyone looking to gain a deep understanding of deep learning.
Provides a comprehensive overview of statistical learning, covering the fundamental concepts and algorithms of statistical learning. It valuable resource for anyone looking to gain a deep understanding of statistical learning.
Save
Provides a comprehensive overview of machine learning, covering the fundamental concepts and algorithms of machine learning. It valuable resource for anyone looking to gain a deep understanding of machine learning.
Provides a practical introduction to data analytics, covering the fundamental concepts and techniques of data analytics. It valuable resource for anyone looking to gain a deep understanding of data analytics.
Provides a comprehensive overview of Python for data analysis, covering the fundamental concepts and techniques of data analysis using Python. It valuable resource for anyone looking to gain a deep understanding of data analysis using Python.
Save
Provides a comprehensive overview of R for data science, covering the fundamental concepts and techniques of data science using R. It valuable resource for anyone looking to gain a deep understanding of data science using R.
Provides a practical introduction to data science, covering the fundamental concepts and techniques of data science. It valuable resource for anyone looking to gain a deep understanding of data science.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone looking to gain hands-on experience with machine learning.
Provides a comprehensive overview of machine learning using Python, covering the fundamental concepts and algorithms of machine learning. It valuable resource for anyone looking to gain a deep understanding of machine learning.
Save
Provides a practical introduction to the Rust programming language, covering the basics of Rust syntax and semantics as well as more advanced topics such as concurrency and memory management. It valuable resource for anyone looking to learn Rust or improve their Rust skills.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ywzncx/devops
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Effort
5 weeks of study, 3-5 hours/week
Level
Advanced
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
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پښتو
آذربایجان دیلی
This course belongs to a
collection
called
MLOps | Machine Learning Operations. It's comprised of four courses:
- MLOps Tools: MLflow and Hugging Face
- Python Essentials for MLOps
- DevOps, DataOps, MLOps (viewing)
- MLOps Platforms: Amazon SageMaker and Azure ML
Good to know
Explores MLOps technologies and strategies, which is standard industry practice
Provides foundational skills in Python, math, and data science, which are core skills for many data-centric careers
Focuses on practical applications of MLOps, DevOps, DataOps, and AIOps
Integrates with GitHub, Hugging Face, and OpenAI, which are widely used tools in the MLOps industry
Covers Rust, a language gaining traction in MLOps for its efficiency in systems programming
Teaches end-to-end MLOps solutions, preparing learners for real-world MLOps pipelines
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