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Carey Phelps
Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.
This course will introduce you to Machine Learning Operations tools that manage this workload. You will learn to use the Weights & Biases platform which makes it easy to track your experiments, run and version your data, and collaborate with your team.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides an easy way to track experiments and version data
Introduces tools to manage machine learning and AI project workload
Helps learners track experiment results and log run metrics
Learners can collect artifacts for dataset and model versioning
Provides a systematic workflow to enhance productivity
Requires learners to have prior knowledge in machine learning and AI
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Reviews summary
Practical mlops for generative ai
According to students, this course offers a largely positive experience, particularly for those seeking to apply MLOps principles to generative AI projects using the Weights & Biases platform. Learners consistently highlight the hands-on labs and practical application of concepts such as experiment tracking, artifact versioning, and LLM prompt and response tracing. While it provides a systematic workflow and clear explanations from instructors, some learners note it is highly focused on W&B, which might be a consideration for those expecting broader, tool-agnostic debugging strategies. It provides a solid foundation but assumes some prior ML background.
Learners benefit from prior machine learning background.
"I felt it assumed a bit too much prior knowledge in advanced ML debugging."
"It might be challenging for absolute beginners, as it assumes some ML background."
"It’s helpful to have some existing understanding of machine learning concepts before starting, especially around model evaluation."
Offers valuable insights into tracing prompts and responses for LLMs.
"The content on prompt tracing was particularly insightful. I immediately applied these techniques at my job."
"I appreciated the focus on LLM specific logging and felt it was a significant value-add."
"I learned how to trace prompts and responses to LLMs over time in complex interactions, which is highly relevant to my work."
Provides hands-on experience with MLOps tools for generative AI.
"The hands-on labs were incredibly practical, and the explanations for logging and tracing LLM interactions were super clear."
"Excellent introduction to MLOps for generative AI, focusing heavily on Weights & Biases. The Jupyter notebook exercises were very helpful."
"The content on experiment tracking and artifact versioning with W&B is gold. It streamlined my team's workflow immensely."
Some sections could benefit from more detailed coverage.
"My only minor critique is that some parts felt a bit rushed, especially the more advanced debugging techniques."
"I wished for a bit more depth in certain areas, though the overall pacing was generally good."
"The course could benefit from expanding on some of the debugging principles beyond just W&B usage."
Course is specific to W&B, not a broad debugging guide.
"I enrolled hoping for deep insights into debugging *all* generative AI issues. It largely felt like a product tutorial rather than a comprehensive debugging guide."
"I was hoping for more general debugging strategies for generative AI, not just platform-specific ones. It's good for W&B users, maybe less for broad debugging."
"It's quite specific to W&B, so if you're looking for a tool-agnostic approach to debugging, this might not be it."
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 Evaluating and Debugging Generative AI with these
activities:
Compile Course Notes and Materials
Show steps
Organize your course materials to facilitate effective review and recall.
Show steps
-
Review lecture notes, assignments, and other course materials
-
Organize and summarize key concepts in a structured format
-
Create a digital or physical notebook for easy access
Read 'Machine Learning in Action'
Show steps
This book provides a comprehensive overview of machine learning concepts, algorithms, and practical applications.
View
Machine Learning in Action
on Amazon
Show steps
-
Read the book thoroughly and take notes
-
Complete the exercises and projects provided in the book
-
Discuss the book with your peers or mentor
Join a Study Group
Show steps
Collaborating with peers will provide different perspectives and enhance your understanding.
Show steps
-
Find a study group or form one with classmates
-
Meet regularly to discuss course concepts and assignments
-
Share notes, resources, and support
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Data Preprocessing and Feature Engineering
Show steps
Building proficiency in data preprocessing and feature engineering will improve the quality of your ML projects.
Browse courses on
Data Preprocessing
Show steps
-
Find real-world datasets and perform data cleaning and transformation
-
Apply feature engineering techniques to enhance data quality
-
Experiment with different data preprocessing and feature engineering methods
Experiment with Simple Projects
Show steps
Getting hands-on with simple projects will build confidence and solidify your understanding of the tools and techniques taught in this course.
Show steps
-
Choose a small-scale project from the course materials or other online resources
-
Define the project scope and gather necessary data
-
Implement the project using the Weights & Biases platform
-
Run experiments and track results
Explore Advanced MLOps Techniques
Show steps
Stay updated on the latest MLOps techniques to enhance your project management skills.
Show steps
-
Follow online tutorials and articles on MLOps best practices
-
Experiment with different MLOps tools and platforms
-
Attend webinars and conferences on MLOps
Develop a Personal Machine Learning Project
Show steps
Working on a personal project will allow you to apply your skills and creativity.
Show steps
-
Identify a problem or opportunity that interests you
-
Gather data and explore different machine learning algorithms
-
Build and train a model, and evaluate its performance
-
Deploy your model and monitor its performance
Compile Course Notes and Materials
Show steps
Organize your course materials to facilitate effective review and recall.
Show steps
Show steps
Show steps
- Review lecture notes, assignments, and other course materials
- Organize and summarize key concepts in a structured format
- Create a digital or physical notebook for easy access
Read 'Machine Learning in Action'
Show steps
This book provides a comprehensive overview of machine learning concepts, algorithms, and practical applications.
View
Machine Learning in Action
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly and take notes
- Complete the exercises and projects provided in the book
- Discuss the book with your peers or mentor
Join a Study Group
Show steps
Collaborating with peers will provide different perspectives and enhance your understanding.
Show steps
Show steps
Show steps
- Find a study group or form one with classmates
- Meet regularly to discuss course concepts and assignments
- Share notes, resources, and support
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Data Preprocessing and Feature Engineering
Show steps
Building proficiency in data preprocessing and feature engineering will improve the quality of your ML projects.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Find real-world datasets and perform data cleaning and transformation
- Apply feature engineering techniques to enhance data quality
- Experiment with different data preprocessing and feature engineering methods
Experiment with Simple Projects
Show steps
Getting hands-on with simple projects will build confidence and solidify your understanding of the tools and techniques taught in this course.
Show steps
Show steps
Show steps
- Choose a small-scale project from the course materials or other online resources
- Define the project scope and gather necessary data
- Implement the project using the Weights & Biases platform
- Run experiments and track results
Explore Advanced MLOps Techniques
Show steps
Stay updated on the latest MLOps techniques to enhance your project management skills.
Show steps
Show steps
Show steps
- Follow online tutorials and articles on MLOps best practices
- Experiment with different MLOps tools and platforms
- Attend webinars and conferences on MLOps
Develop a Personal Machine Learning Project
Show steps
Working on a personal project will allow you to apply your skills and creativity.
Show steps
Show steps
Show steps
- Identify a problem or opportunity that interests you
- Gather data and explore different machine learning algorithms
- Build and train a model, and evaluate its performance
- Deploy your model and monitor its performance
Career center
Learners who complete Evaluating and Debugging Generative AI will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist works with big datasets in order to extract trends and other useful information from them. They may perform statistical analysis or run data models. This course provides skills that can help a Data Scientist manage complex data sources and become more efficient.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and maintains machine learning models. They develop, test, and deploy models that handle large datasets. This course can help a Machine Learning Engineer improve their workflow and maximize their productivity.
Software Engineer
Software Engineer
A Software Engineer works primarily in the context of coding and application development. They analyze user needs and create software solutions. This course provides Software Engineers with tools to increase efficiency in managing and developing high volumes of data.
Data Analyst
Data Analyst
Working with large and complex datasets, a Data Analyst finds and interprets patterns. This role often requires managing diverse data sources and running various tests and evaluations. This course provides skills that can help Data Analysts become more organized and efficient in their work.
Business Analyst
Business Analyst
Business Analysts analyze an organization or business to improve its performance. An understanding of data and data management is essential for this role so that sound recommendations can be made. This course can help equip Business Analysts with the knowledge they need to perform their jobs to a high standard.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. They develop statistical models and perform various tests. This course can be helpful to Statisticians by providing them with tools they can use to improve efficiency in data handling and analysis.
Data Architect
Data Architect
A Data Architect is a specialized IT professional who designs, builds, and manages data systems. They work to ensure that data is stored, organized, and accessed in a way that meets the needs of an organization and is in line with business goals. This course provides tools and skills to make Data Architects more efficient and productive in their roles.
Database Administrator
Database Administrator
A Database Administrator is responsible for managing and maintaining an organization's database system. They ensure that data is stored, organized, and accessed in a secure and efficient manner. This course provides skills that can help Database Administrators improve their workflow and maximize their productivity.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment recommendations. They play a vital role in the financial industry. This course can be helpful to a Quantitative Analyst by improving their ability to handle data and perform evaluations.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. They work to ensure that data is collected, processed, and stored in a way that meets the needs of an organization. This course may be useful to a Data Engineer as it provides skills and tools to improve efficiency in managing and developing data pipelines.
Product Manager
Product Manager
A Product Manager is responsible for managing the development and launch of a product. They work with engineers, designers, and marketers to ensure that a product is successful. This course may be useful to a Product Manager as it provides skills and tools that can help improve efficiency and collaboration in product development.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. They work with stakeholders to ensure that projects are completed on time, within budget, and to scope. This course may be useful to a Project Manager as it provides skills and tools to improve efficiency and collaboration in project management.
Systems Analyst
Systems Analyst
A Systems Analyst studies and designs computer systems to meet the needs of an organization. They analyze existing systems and develop solutions to improve efficiency and effectiveness. This course may be useful to a Systems Analyst as it provides skills and tools to improve efficiency and collaboration in systems analysis.
Technical Writer
Technical Writer
A Technical Writer creates and maintains documentation for a variety of technical products and services. They work with engineers and other stakeholders to ensure that documentation is clear, accurate, and meets the needs of users. This course may be useful to a Technical Writer as it provides skills and tools to improve efficiency and collaboration in documentation.
Information Security Analyst
Information Security Analyst
An Information Security Analyst plans and implements security measures to protect an organization's data and systems from unauthorized access, use, disclosure, disruption, modification, or destruction. This course may be useful to an Information Security Analyst as it provides skills and tools to improve efficiency and collaboration in information security.
For more career information including salaries, visit:
OpenCourser.com/course/tlhh9r/evaluating
Reading list
We've selected ten 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
Evaluating and Debugging Generative AI.
Comprehensive reference on reinforcement learning, covering topics such as Markov decision processes, value functions, and policy gradients. It valuable resource for anyone looking to gain a deeper understanding of the theory and practice of reinforcement learning.
Save
Comprehensive reference on deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone looking to gain a deeper understanding of the theory and practice of deep learning.
Provides a hands-on introduction to deep learning using the Fastai and PyTorch libraries. It great resource for anyone looking to gain practical experience with deep learning.
Provides a hands-on introduction to deep learning using Python. It great resource for anyone looking to gain practical experience with deep learning.
Provides a hands-on introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It great resource for anyone looking to gain practical experience with machine learning.
Provides a comprehensive overview of Python for data analysis, covering topics such as data manipulation, data visualization, and machine learning. It valuable resource for anyone looking to gain a deeper understanding of the Python ecosystem for data analysis.
Provides a comprehensive overview of machine learning using R, covering topics such as data preprocessing, model selection, model training, and model evaluation. It valuable resource for anyone looking to gain a deeper understanding of the machine learning development lifecycle.
Provides a hands-on introduction to machine learning using Python. It great resource for anyone looking to gain practical experience with machine learning.
Provides a gentle introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to gain a basic understanding of the field of machine learning.
Provides a business-oriented introduction to data science, covering topics such as data collection, data analysis, and data visualization. It great resource for anyone looking to gain a basic understanding of how data science can be used to solve business problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tlhh9r/evaluating
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