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John Elder, IV,
Peter Bruce,
Shree Taylor,
Bryce Pilcher,
Allison Marrs,
Ramzi Ziade,
Greg Carmean,
LeAnna Kent,
Henry Mead,
Kuber Deokar,
and
Janet Dobbins
This is the second of three courses in the Machine Learning Operations Program using Amazon Web Services (AWS).
Read more
This is the second of three courses in the Machine Learning Operations Program using Amazon Web Services (AWS).
Read more
This is the second of three courses in the Machine Learning Operations Program using Amazon Web Services (AWS).
Data Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business and human-nature reasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: MLOps1(AWS): Deploying AI & ML Models in Production.
You will get hands-on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.
Most importantly, by the end of this course, you will know...
-
What data engineers need to know to work effectively with data scientists
-
How to embed a predictive model in a pipeline that takes in data and outputs predictions automatically
-
How to monitor the model’s performance and follow best practices
What you'll learn
-
What data engineers need to know in order to work effectively with data scientists
-
How to use a machine learning model to make predictions
-
How to embed that model in a pipeline that takes in data and outputs predictions automatically
-
How to measure the performance of the model and the pipeline, and how to log those metrics
-
How to follow best practices for “versioning” the model and the data
-
How to track and store model and data artifacts
Register for this course and see more details by visiting:
OpenCourser.com/course/t1301b/mlops1
What's inside
Syllabus
Week 1: The Machine Learning Pipeline
AI Engineering Role
ML pipeline lifecycle
Week 2: The Model in the Pipeline
Read more
Syllabus
Week 1: The Machine Learning Pipeline
AI Engineering Role
ML pipeline lifecycle
Week 2: The Model in the Pipeline
Read more
Case Study for the Course
Model Understanding
Week 3: Monitoring Model Performance
Logging and Metric Selection
Model and Data Versioning
Week 4: Training Artifacts and Model Store
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches learners how to monitor model performance, which is a key part of ensuring that the model is working as expected and is not biased
Develops skills in data engineering, which is increasingly important in many industries
Taught by instructors who are recognized for their work in the field and who work at the forefront of the tech industry
Covers topics that are highly relevant to industry
Requires students to have some prior knowledge of data science and machine learning
Focuses on deploying models on AWS, which may not be relevant for all learners
Save this course
Save MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services 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 MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services with these
activities:
Organize Course Materials for Efficient Learning
Show steps
Maximize your learning by organizing course materials in a systematic and accessible manner.
Browse courses on
Organization
Show steps
-
Gather all course materials, including notes, assignments, and recordings.
-
Create a digital or physical filing system to organize your materials.
-
Categorize and label materials for easy retrieval.
-
Review your materials regularly to reinforce understanding.
Connect with Mentors in the Field of Machine Learning Deployment
Show steps
Seek guidance and support by connecting with industry experts who have experience in deploying machine learning models.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors in your field.
-
Reach out to them and introduce yourself.
-
Schedule informational interviews or meetings to learn from their experiences.
-
Stay in touch and seek ongoing mentorship.
ML Pipeline Basics
Show steps
Review some of the core concepts used in ML Pipelines to ensure you're ready for this course.
Browse courses on
Machine Learning Pipelines
Show steps
-
Read AWS docs on constructing a data pipeline
-
Create a simple data pipeline in Python
-
Complete a basic tutorial on ML pipelines
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Explore AWS Data Pipeline Services
Show steps
Enhance your understanding of AWS services by exploring tutorials related to data pipelines.
Browse courses on
AWS Services
Show steps
-
Identify AWS services relevant to data pipelines.
-
Follow tutorials on using these services for data ingestion, processing, and transformation.
-
Experiment with sample data to gain hands-on experience.
Interactive Data Pipeline Exercises
Show steps
Reinforce your understanding of constructing and managing data pipelines by actively engaging in interactive exercises.
Browse courses on
Data Pipelines
Show steps
-
Follow guided tutorials on data pipeline construction.
-
Practice creating pipelines that ingest data from various sources.
-
Experiment with data transformations and feature engineering within pipelines.
Attend an AWS ML Deployment Workshop
Show steps
Expand your practical knowledge by attending an AWS workshop dedicated to machine learning deployment.
Browse courses on
Machine Learning Deployment
Show steps
-
Register for an AWS ML Deployment workshop.
-
Attend the workshop and actively participate in hands-on exercises.
-
Network with industry experts and fellow attendees.
Personalize a Model Deployment Project
Show steps
Solidify your knowledge by initiating a project that involves deploying a machine learning model on AWS.
Browse courses on
Model Deployment
Show steps
-
Identify a real-world problem that can be addressed with a machine learning model.
-
Develop a machine learning model to solve the problem.
-
Deploy the model on AWS using appropriate services.
-
Monitor and evaluate the deployed model's performance.
Develop a Model Monitoring and Evaluation Plan
Show steps
Enhance your practical skills by creating a comprehensive plan for monitoring and evaluating deployed machine learning models.
Browse courses on
Model Monitoring
Show steps
-
Define key performance indicators (KPIs) for monitoring the model.
-
Identify appropriate monitoring tools and techniques.
-
Develop a data collection and analysis strategy for model evaluation.
-
Establish a feedback loop to continuously improve the model's performance.
Contribute to Open-Source Projects Related to Model Deployment
Show steps
Deepen your understanding by actively contributing to open-source projects in the field of model deployment.
Show steps
-
Identify open-source projects related to model deployment.
-
Review the project documentation and codebase.
-
Identify areas where you can contribute.
-
Propose and implement your contributions.
Record a Video Tutorial on a Specific Topic
Show steps
Solidify your understanding by teaching others through the creation of a video tutorial on a specific topic related to the course.
Show steps
-
Identify a topic that you have a strong understanding of.
-
Create an outline for your video tutorial.
-
Record and edit your video tutorial.
-
Share your tutorial on a platform like YouTube or Vimeo.
Organize Course Materials for Efficient Learning
Show steps
Maximize your learning by organizing course materials in a systematic and accessible manner.
Browse courses on
Organization
Show steps
Show steps
Show steps
- Gather all course materials, including notes, assignments, and recordings.
- Create a digital or physical filing system to organize your materials.
- Categorize and label materials for easy retrieval.
- Review your materials regularly to reinforce understanding.
Connect with Mentors in the Field of Machine Learning Deployment
Show steps
Seek guidance and support by connecting with industry experts who have experience in deploying machine learning models.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors in your field.
- Reach out to them and introduce yourself.
- Schedule informational interviews or meetings to learn from their experiences.
- Stay in touch and seek ongoing mentorship.
ML Pipeline Basics
Show steps
Review some of the core concepts used in ML Pipelines to ensure you're ready for this course.
Browse courses on
Machine Learning Pipelines
Show steps
Show steps
Show steps
- Read AWS docs on constructing a data pipeline
- Create a simple data pipeline in Python
- Complete a basic tutorial on ML pipelines
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Explore AWS Data Pipeline Services
Show steps
Enhance your understanding of AWS services by exploring tutorials related to data pipelines.
Browse courses on
AWS Services
Show steps
Show steps
Show steps
- Identify AWS services relevant to data pipelines.
- Follow tutorials on using these services for data ingestion, processing, and transformation.
- Experiment with sample data to gain hands-on experience.
Interactive Data Pipeline Exercises
Show steps
Reinforce your understanding of constructing and managing data pipelines by actively engaging in interactive exercises.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Follow guided tutorials on data pipeline construction.
- Practice creating pipelines that ingest data from various sources.
- Experiment with data transformations and feature engineering within pipelines.
Attend an AWS ML Deployment Workshop
Show steps
Expand your practical knowledge by attending an AWS workshop dedicated to machine learning deployment.
Browse courses on
Machine Learning Deployment
Show steps
Show steps
Show steps
- Register for an AWS ML Deployment workshop.
- Attend the workshop and actively participate in hands-on exercises.
- Network with industry experts and fellow attendees.
Personalize a Model Deployment Project
Show steps
Solidify your knowledge by initiating a project that involves deploying a machine learning model on AWS.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed with a machine learning model.
- Develop a machine learning model to solve the problem.
- Deploy the model on AWS using appropriate services.
- Monitor and evaluate the deployed model's performance.
Develop a Model Monitoring and Evaluation Plan
Show steps
Enhance your practical skills by creating a comprehensive plan for monitoring and evaluating deployed machine learning models.
Browse courses on
Model Monitoring
Show steps
Show steps
Show steps
- Define key performance indicators (KPIs) for monitoring the model.
- Identify appropriate monitoring tools and techniques.
- Develop a data collection and analysis strategy for model evaluation.
- Establish a feedback loop to continuously improve the model's performance.
Contribute to Open-Source Projects Related to Model Deployment
Show steps
Deepen your understanding by actively contributing to open-source projects in the field of model deployment.
Show steps
Show steps
Show steps
- Identify open-source projects related to model deployment.
- Review the project documentation and codebase.
- Identify areas where you can contribute.
- Propose and implement your contributions.
Record a Video Tutorial on a Specific Topic
Show steps
Solidify your understanding by teaching others through the creation of a video tutorial on a specific topic related to the course.
Show steps
Show steps
Show steps
- Identify a topic that you have a strong understanding of.
- Create an outline for your video tutorial.
- Record and edit your video tutorial.
- Share your tutorial on a platform like YouTube or Vimeo.
Career center
Learners who complete MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use many of the same skills as ML Engineers, Machine Learning Engineers, and Machine Learning Scientists. This course covers basic machine learning principles and provides an in-depth examination of the creation and monitoring of machine learning pipelines. Those who want to maximize their potential to become Data Scientists should consider taking this course.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are professionals who work with data scientists to apply machine learning to solve business problems. As a Machine Learning Engineer, you would likely use and manage the machine learning pipelines that this course concerns itself with creating. Additionally, you would be responsible for monitoring the performance of the models within the pipeline. Therefore, this course is highly relevant to Machine Learning Engineers.
Data Engineer
Data Engineer
This course focuses on how to prepare data for machine learning models. It also discusses how to measure and maintain the performance of a machine learning pipeline. Therefore, this course is highly relevant to Data Engineers.
Machine Learning Scientist
Machine Learning Scientist
As a Machine Learning Scientist, you may find yourself creating and monitoring the machine learning pipelines that this course covers. You would also be responsible for monitoring those pipelines, training models, and applying those models to data. This course provides many tools and demonstrates many best practices that Machine Learning Scientists should be familiar with.
Software Engineer
Software Engineer
This course teaches the skills needed to create, deploy, and manage machine learning pipelines. As a Software Engineer, you may be involved in some of these tasks. Therefore, this course may be of some use to Software Engineers.
Business Analyst
Business Analyst
This course may be useful for Business Analysts who want to improve their data analysis skills.
Project Manager
Project Manager
Project Managers may benefit from the organizational and planning elements of this course.
Data Analyst
Data Analyst
This course covers many foundational elements of data analysis and could be useful to someone looking to work in the field.
Systems Analyst
Systems Analyst
This course may be useful to a Systems Analyst who wants to gain a higher level understanding of machine learning.
Database Administrator
Database Administrator
This course may be useful to a Database Administrator who wants to gain a higher level understanding of machine learning.
Statistician
Statistician
This course covers many foundational elements of statistics and could be useful to someone looking to work in the field.
Financial Analyst
Financial Analyst
This course may be useful to a Financial Analyst who wants to gain a higher level understanding of machine learning.
Operations Research Analyst
Operations Research Analyst
This course may be useful to an Operations Research Analyst who wants to gain a higher level understanding of machine learning.
Marketing Analyst
Marketing Analyst
This course may be useful to a Marketing Analyst who wants to gain a higher level understanding of machine learning.
Management Consultant
Management Consultant
This course may be useful to a Management Consultant who wants to gain a higher level understanding of machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/t1301b/mlops1
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
MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services.
The book emphasizes the practical aspects of implementing ML projects. Readers will benefit from its focus on best practices and real-world case studies, which provide valuable insights into the challenges and solutions in deploying ML systems.
Introduces deep learning using Fastai and PyTorch, making it accessible to those with limited ML background. It provides a practical approach to building and deploying deep learning models for real-world applications.
Covers a range of machine learning topics, with a focus on using AWS services for data engineering, model training, and deployment.
Covers the fundamentals of big data analytics using Java, Apache Spark, and Hadoop, providing a solid foundation for implementing data engineering pipelines.
Provides an introduction to deep learning, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks.
Provides insights into software engineering practices at Google, including how they approach data engineering and ML infrastructure.
Classic reference on data warehousing and dimensional modeling, providing a solid foundation for understanding data structures and organization.
Save
Provides a broad overview of data science, including data visualization, statistical modeling, and machine learning.
Provides a practical introduction to data science using Python, covering data cleaning, analysis, and visualization.
Provides a comprehensive guide to using the Pandas library in Python for data manipulation and analysis.
Provides a comprehensive guide to using Python for data science, covering data analysis, visualization, and machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/t1301b/mlops1
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Effort
5 to 7 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Statistics.com
Instructors
John Elder, IV
Peter Bruce
Shree Taylor
Bryce Pilcher
Allison Marrs
Ramzi Ziade
Greg Carmean
LeAnna Kent
Henry Mead
Kuber Deokar
Janet Dobbins
Language
English
Transcript
English
Good to know
Teaches learners how to monitor model performance, which is a key part of ensuring that the model is working as expected and is not biased
Develops skills in data engineering, which is increasingly important in many industries
Taught by instructors who are recognized for their work in the field and who work at the forefront of the tech industry
Covers topics that are highly relevant to industry
Requires students to have some prior knowledge of data science and machine learning
Focuses on deploying models on AWS, which may not be relevant for all learners
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