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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 Azure Machine Learning.
Read more
This is the second of three courses in the Machine Learning Operations Program using Azure Machine Learning.
Read more
This is the second of three courses in the Machine Learning Operations Program using Azure Machine Learning.
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 andhuman-naturereasons. 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 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning.
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 moniter 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/tra08d/mlops1
What's inside
Syllabus
Week 1: The Machine Learning Pipeline
AI Engineering Role
ML pipelin lifecycle
Week 2: The Model in the Pipeline
Read more
Syllabus
Week 1: The Machine Learning Pipeline
AI Engineering Role
ML pipelin lifecycle
Week 2: The Model in the Pipeline
Read more
Case Study for the Course
Model Undeerstanding
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
Ideal for data engineers and data scientists who want to build models that can be useful in real-world scenarios
Taught by 10 industry experts who have worked with machine learning and data engineering at leading tech companies
Students are given hands-on experience with key concepts in machine learning operations, such as data pipelines and model versioning
Provides a solid foundation for professionals who want to transition into the field of machine learning operations
The course focuses on practical applications of machine learning, making it relevant for those who want to use machine learning to solve real-world problems
Save this course
Save MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning to your list so you can find it easily later:
Save
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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 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning with these
activities:
Review Model Training Concepts
Show steps
Review the concepts surrounding training machine learning models to prepare for the course content.
Browse courses on
Model Training
Show steps
-
Review the documentation on model training in Azure Machine Learning
-
Complete a tutorial on model training using Azure Machine Learning
Practice Model Deployment
Show steps
Practice deploying machine learning models to improve understanding and retention of the course content.
Browse courses on
Model Deployment
Show steps
-
Use Azure Machine Learning to deploy a simple model to Azure Kubernetes Service (AKS)
-
Deploy a model to Azure Container Instances (ACI) using Azure Machine Learning
-
Explore the Azure Machine Learning deployment documentation
Coding Exercises
Show steps
Build your mastery of the basic principles of MLOps through focused practice.
Show steps
-
Complete the coding drills provided in the course materials
Three other activities
Expand to see all activities and additional details
Show all six activities
Model Deployment Plan
Show steps
Enhance your ability to plan and execute ML model deployments by creating a comprehensive plan.
Show steps
-
Write a document outlining your ML model deployment strategy
-
Identify necessary resources and infrastructure
-
Create a timeline for implementation
Build a Model Pipeline
Show steps
Gain hands-on experience designing and building machine learning pipelines for deploying models.
Show steps
-
Create a data pipeline in Azure Machine Learning
-
Train a model using Azure Machine Learning
-
Deploy the model to Azure Kubernetes Service (AKS)
-
Monitor the model's performance
Build a data pipeline to automate the deployment of a machine learning model
Show steps
Building a data pipeline to automate the deployment of a machine learning model will give you valuable experience in a key part of the ML process.
Browse courses on
Data Pipelines
Show steps
-
Design the data pipeline.
-
Implement the data pipeline using Azure Data Factory.
-
Deploy the data pipeline.
-
Monitor the data pipeline.
Review Model Training Concepts
Show steps
Review the concepts surrounding training machine learning models to prepare for the course content.
Browse courses on
Model Training
Show steps
Show steps
Show steps
- Review the documentation on model training in Azure Machine Learning
- Complete a tutorial on model training using Azure Machine Learning
Practice Model Deployment
Show steps
Practice deploying machine learning models to improve understanding and retention of the course content.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Use Azure Machine Learning to deploy a simple model to Azure Kubernetes Service (AKS)
- Deploy a model to Azure Container Instances (ACI) using Azure Machine Learning
- Explore the Azure Machine Learning deployment documentation
Coding Exercises
Show steps
Build your mastery of the basic principles of MLOps through focused practice.
Show steps
Show steps
Show steps
- Complete the coding drills provided in the course materials
Three other activities
Expand to see all activities and additional details
Show all six activities
Model Deployment Plan
Show steps
Enhance your ability to plan and execute ML model deployments by creating a comprehensive plan.
Show steps
Show steps
Show steps
- Write a document outlining your ML model deployment strategy
- Identify necessary resources and infrastructure
- Create a timeline for implementation
Build a Model Pipeline
Show steps
Gain hands-on experience designing and building machine learning pipelines for deploying models.
Show steps
Show steps
Show steps
- Create a data pipeline in Azure Machine Learning
- Train a model using Azure Machine Learning
- Deploy the model to Azure Kubernetes Service (AKS)
- Monitor the model's performance
Build a data pipeline to automate the deployment of a machine learning model
Show steps
Building a data pipeline to automate the deployment of a machine learning model will give you valuable experience in a key part of the ML process.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Design the data pipeline.
- Implement the data pipeline using Azure Data Factory.
- Deploy the data pipeline.
- Monitor the data pipeline.
Career center
Learners who complete MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
If machine learning interests you and you are interested in crafting software, then a Software Engineer may be a good career for you. Software Engineers play an important role in developing, maintaining, and improving software. This role is considered a good fit with the course because you will learn how to deploy AI and ML models in production using Microsoft Azure Machine Learning. This is an important part of being a successful Software Engineer.
Data Analyst
Data Analyst
A Data Analyst is a role that uses data to solve complex problems. It is very closely related to Data Science. You will use statistical modeling, data visualization, and other techniques to understand data and create solutions. This course is a good fit with this role since you will learn about the entire process of getting a machine learning model into production. This includes data pipelines, data and model versioning, model storage, and data artifacts.
Data Engineer
Data Engineer
A Data Engineer is responsible for building and maintaining the infrastructure that handles data for an organization. This is a great role for those who are interested in both data and technology. You will need to have a strong understanding of data management and engineering principles. This course provides a good overview of some of the key topics in data engineering, including data pipelines, data and model versioning, and model storage. This will give you a strong foundation for a career as a Data Engineer.
Machine Learning Specialist
Machine Learning Specialist
A Machine Learning Specialist is responsible for developing and implementing machine learning solutions. This is a highly specialized role and you will need to have a strong understanding of machine learning algorithms and techniques. You will also need to be able to work with data and understand how to build and deploy machine learning models. As such, this course is a great option because it will teach you a lot of the fundamentals of Machine Learning Operations.
Data Scientist
Data Scientist
Data Scientists are responsible for using data to solve business problems. This is a highly analytical and technical role and you will need to have a strong understanding of statistics, machine learning, and data mining. You will also need to be able to communicate your findings to non-technical audiences. This course is a good fit for this role as it will help you understand the entire process of getting a machine learning model into production. This is an important part of being a successful Data Scientist.
For more career information including salaries, visit:
OpenCourser.com/course/tra08d/mlops1
Reading list
We've selected 17 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 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning.
Andriy Burkov's book on Machine Learning Engineering will provide a solid foundation and a useful reference for readers when it comes to dealing with complex ML models and pipelines in the real-world.
By Christof Molnar valuable resource for understanding the inner workings of ML models and how to interpret their predictions, which is important for effectively deploying and managing ML models in production.
Covers the end-to-end process of data science and machine learning using Azure AI services.
Provides a comprehensive introduction to data-driven science and engineering, including topics such as machine learning, dynamical systems, and control. It would be a valuable resource for anyone looking to learn more about the theoretical foundations of machine learning.
Save
Will give a valuable perspective on designing data-intensive applications and using pipelines to solve real-world problems.
May be more focused on the business applications of machine learning, it may still be a useful reference for those who are interested in how machine learning pipelines are used to solve real-world business problems.
Provides a practical introduction to machine learning using Python. It would be a valuable resource for anyone looking to learn more about how to use machine learning in practice.
Save
Provides a comprehensive introduction to deep learning, including topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It would be a valuable resource for anyone looking to learn more about the latest advances in machine learning.
Provides a practical introduction to machine learning for non-technical readers. It would be a valuable resource for anyone looking to learn more about machine learning without getting bogged down in the technical details.
Provides a high-level overview of artificial intelligence, including topics such as machine learning, natural language processing, and computer vision. It would be a valuable resource for anyone looking to learn more about the basics of artificial intelligence.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It would be a valuable resource for anyone looking to learn more about the theoretical foundations of machine learning.
Provides a comprehensive introduction to Bayesian reasoning and machine learning. It would be a valuable resource for anyone looking to learn more about the theoretical foundations of machine learning.
Provides a comprehensive introduction to pattern recognition and machine learning. It would be a valuable resource for anyone looking to learn more about the theoretical foundations of machine learning.
Provides a high-level overview of machine learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning. It would be a valuable resource for anyone looking to learn more about the basics of machine learning.
Provides a practical introduction to deep learning using Python. It would be a valuable resource for anyone looking to learn more about how to use deep learning in practice.
Provides a comprehensive introduction to reinforcement learning. It would be a valuable resource for anyone looking to learn more about the theoretical foundations of reinforcement learning.
Provides a practical introduction to natural language processing using Python. It would be a valuable resource for anyone looking to learn more about how to use natural language processing in practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tra08d/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
Ideal for data engineers and data scientists who want to build models that can be useful in real-world scenarios
Taught by 10 industry experts who have worked with machine learning and data engineering at leading tech companies
Students are given hands-on experience with key concepts in machine learning operations, such as data pipelines and model versioning
Provides a solid foundation for professionals who want to transition into the field of machine learning operations
The course focuses on practical applications of machine learning, making it relevant for those who want to use machine learning to solve real-world problems
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