We may earn an affiliate commission when you visit our partners.
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 Google Cloud Platform (GCP).
Read more
This is the second of three courses in the Machine Learning Operations Program using Google Cloud Platform (GCP).
Read more
This is the second of three courses in the Machine Learning Operations Program using Google Cloud Platform (GCP).
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 (GCP): 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/5rg501/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
Develops skills in data engineering and data pipelines, which are essential for implementing machine learning models in production
Offers hands-on experience with real-world tools and technologies used in data engineering and ML operations
Taught by a team of experts with decades of experience in deploying machine learning models
Part of a series of three courses that provide a comprehensive understanding of Machine Learning Operations using Google Cloud Platform
Requires explicit prerequisites, so students may need to take other courses first
May require access to specific software or tools not readily available in a typical household or library
Save this course
Save MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform 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 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform with these
activities:
Review Data Pipeline Concepts
Show steps
Reviewing data pipeline concepts will help you better understand the role of data engineers and how to embed a predictive model in a pipeline.
Browse courses on
Data Pipelines
Show steps
-
Study online tutorials and articles about data pipelines.
-
Practice building simple data pipelines using open-source tools.
-
Review examples of real-world data pipelines.
Review SQL
Show steps
Refreshes your knowledge of SQL before the course begins.
Browse courses on
SQL
Show steps
-
Read through SQL tutorial
-
Complete practice exercises
Review Model Training
Show steps
Review and refresh your understanding of model training. This will strengthen your knowledge and prepare you for the upcoming course.
Browse courses on
Model Training
Show steps
-
Revisit materials on model training techniques.
-
Practice implementing model training algorithms in a programming language.
-
Analyze the results of model training experiments to understand the impact of different parameters.
15 other activities
Expand to see all activities and additional details
Show all 18 activities
Cloud AI Workshop
Show steps
Provides practical experience with deploying machine learning models on Google Cloud.
Browse courses on
Cloud AI Platform
Show steps
-
Attend the workshop in person or online
-
Follow the hands-on exercises
-
Ask questions and interact with experts
Data Pipeline Tutorial
Show steps
Engage with guided tutorials on data pipelines to build a practical understanding of data handling and preparation.
Browse courses on
Data Pipeline
Show steps
-
Follow step-by-step tutorials to create data pipelines using industry-standard tools.
-
Learn best practices for data cleaning, transformation, and feature engineering.
-
Experiment with different data pipeline architectures to understand their advantages and limitations.
Practice Model Evaluation Metrics
Show steps
Practicing model evaluation metrics will help you understand how to measure the performance of a model and follow best practices.
Browse courses on
Model Evaluation
Show steps
-
Calculate different model evaluation metrics, such as accuracy, precision, recall, and F1 score.
-
Compare the performance of different models using different metrics.
-
Analyze the results of model evaluation to identify areas for improvement.
Study Group
Show steps
Encourages collaboration, knowledge sharing, and reinforces concepts covered in the course.
Show steps
-
Form a study group with classmates
-
Meet regularly to discuss course materials
-
Work together on assignments and projects
Peer-Review Model Training Experiments
Show steps
Engage in peer-review of model training experiments to enhance your critical thinking and communication skills while learning from others.
Browse courses on
Model Training
Show steps
-
Form a peer-review group with fellow learners.
-
Share model training experiments and provide constructive feedback on methodology, results, and conclusions.
-
Incorporate insights from peer reviews to refine your own approach to model training.
Machine Learning Blog Post
Show steps
Provides an opportunity to synthesize and share your knowledge of machine learning concepts and deployment best practices.
Browse courses on
Machine Learning
Show steps
-
Choose a topic
-
Research and gather information
-
Write and edit the blog post
-
Publish and promote the blog post
Model Performance Monitoring
Show steps
Practice drills on model performance monitoring will enhance your ability to assess and improve model effectiveness.
Browse courses on
Model Monitoring
Show steps
-
Solve problems involving the selection of appropriate metrics for model evaluation.
-
Develop a monitoring strategy to track model performance over time.
-
Perform root cause analysis to identify and address performance issues.
Pipeline Debugging Drills
Show steps
Engage in debugging drills for data pipelines to strengthen your ability to identify and resolve issues, ensuring data integrity.
Browse courses on
Data Engineering
Show steps
-
Analyze error messages and logs to identify the root cause of data pipeline failures.
-
Troubleshoot data quality issues and implement data validation techniques.
-
Develop strategies to handle missing or corrupted data.
Data Engineering Workshop
Show steps
Attend a data engineering workshop to gain hands-on experience with industry-standard tools and techniques, deepening your understanding of data handling and preparation.
Browse courses on
Data Management
Show steps
-
Participate in interactive exercises and discussions on data engineering best practices.
-
Work on real-world data engineering projects under the guidance of experienced practitioners.
-
Network with experts and peers in the field of data engineering.
Kaggle Data Science Bowl
Show steps
Develops hands-on skills in applying machine learning models to real-world problems.
Browse courses on
Data Science
Show steps
-
Review the data and competition guidelines
-
Develop a machine learning model
-
Evaluate and improve your model
-
Submit your predictions to the competition
Data Artifacts Management Plan
Show steps
Create a data artifacts management plan to demonstrate your understanding of data storage and versioning best practices.
Browse courses on
Data Artifacts
Show steps
-
Define a strategy for storing and managing different types of data artifacts (e.g., models, training data, evaluation results).
-
Implement version control mechanisms to track changes and ensure reproducibility.
-
Establish guidelines for data access and security.
Follow Tutorials on Model Storage
Show steps
Following tutorials on model storage will help you understand how to store and track models and data artifacts.
Browse courses on
Model Storage
Show steps
-
Find online tutorials or courses on model storage using Google Cloud Platform (GCP).
-
Follow the tutorials to learn how to store and retrieve models and data artifacts using GCP services.
-
Practice storing and retrieving models and data artifacts using GCP services.
Data Pipeline Project
Show steps
Builds a complete data pipeline for a machine learning project, including data ingestion, cleaning, and model training.
Browse courses on
Data Pipelines
Show steps
-
Define the data pipeline architecture
-
Implement the data pipeline using a cloud platform
-
Test and evaluate the data pipeline
-
Document and present the data pipeline
Model Deployment Guide
Show steps
Create a comprehensive guide on model deployment to showcase your knowledge and provide a valuable resource to the community.
Browse courses on
Model Deployment
Show steps
-
Document the steps involved in deploying a machine learning model into production.
-
Provide insights on choosing the right deployment architecture and infrastructure.
-
Discuss best practices for monitoring and maintaining deployed models.
Contribute to Open Source Model Repository
Show steps
Contribute to an open source model repository to share your expertise, collaborate with others, and enhance your understanding of model development and deployment.
Browse courses on
Open Source
Show steps
-
Identify an open source model repository that aligns with your interests.
-
Review the existing models and identify areas where you can contribute.
-
Develop a model or improve an existing one, following the repository's guidelines.
-
Submit a pull request with your contributions and engage in code reviews.
Review Data Pipeline Concepts
Show steps
Reviewing data pipeline concepts will help you better understand the role of data engineers and how to embed a predictive model in a pipeline.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Study online tutorials and articles about data pipelines.
- Practice building simple data pipelines using open-source tools.
- Review examples of real-world data pipelines.
Review SQL
Show steps
Refreshes your knowledge of SQL before the course begins.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Read through SQL tutorial
- Complete practice exercises
Review Model Training
Show steps
Review and refresh your understanding of model training. This will strengthen your knowledge and prepare you for the upcoming course.
Browse courses on
Model Training
Show steps
Show steps
Show steps
- Revisit materials on model training techniques.
- Practice implementing model training algorithms in a programming language.
- Analyze the results of model training experiments to understand the impact of different parameters.
15 other activities
Expand to see all activities and additional details
Show all 18 activities
Cloud AI Workshop
Show steps
Provides practical experience with deploying machine learning models on Google Cloud.
Browse courses on
Cloud AI Platform
Show steps
Show steps
Show steps
- Attend the workshop in person or online
- Follow the hands-on exercises
- Ask questions and interact with experts
Data Pipeline Tutorial
Show steps
Engage with guided tutorials on data pipelines to build a practical understanding of data handling and preparation.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Follow step-by-step tutorials to create data pipelines using industry-standard tools.
- Learn best practices for data cleaning, transformation, and feature engineering.
- Experiment with different data pipeline architectures to understand their advantages and limitations.
Practice Model Evaluation Metrics
Show steps
Practicing model evaluation metrics will help you understand how to measure the performance of a model and follow best practices.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Calculate different model evaluation metrics, such as accuracy, precision, recall, and F1 score.
- Compare the performance of different models using different metrics.
- Analyze the results of model evaluation to identify areas for improvement.
Study Group
Show steps
Encourages collaboration, knowledge sharing, and reinforces concepts covered in the course.
Show steps
Show steps
Show steps
- Form a study group with classmates
- Meet regularly to discuss course materials
- Work together on assignments and projects
Peer-Review Model Training Experiments
Show steps
Engage in peer-review of model training experiments to enhance your critical thinking and communication skills while learning from others.
Browse courses on
Model Training
Show steps
Show steps
Show steps
- Form a peer-review group with fellow learners.
- Share model training experiments and provide constructive feedback on methodology, results, and conclusions.
- Incorporate insights from peer reviews to refine your own approach to model training.
Machine Learning Blog Post
Show steps
Provides an opportunity to synthesize and share your knowledge of machine learning concepts and deployment best practices.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a topic
- Research and gather information
- Write and edit the blog post
- Publish and promote the blog post
Model Performance Monitoring
Show steps
Practice drills on model performance monitoring will enhance your ability to assess and improve model effectiveness.
Browse courses on
Model Monitoring
Show steps
Show steps
Show steps
- Solve problems involving the selection of appropriate metrics for model evaluation.
- Develop a monitoring strategy to track model performance over time.
- Perform root cause analysis to identify and address performance issues.
Pipeline Debugging Drills
Show steps
Engage in debugging drills for data pipelines to strengthen your ability to identify and resolve issues, ensuring data integrity.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Analyze error messages and logs to identify the root cause of data pipeline failures.
- Troubleshoot data quality issues and implement data validation techniques.
- Develop strategies to handle missing or corrupted data.
Data Engineering Workshop
Show steps
Attend a data engineering workshop to gain hands-on experience with industry-standard tools and techniques, deepening your understanding of data handling and preparation.
Browse courses on
Data Management
Show steps
Show steps
Show steps
- Participate in interactive exercises and discussions on data engineering best practices.
- Work on real-world data engineering projects under the guidance of experienced practitioners.
- Network with experts and peers in the field of data engineering.
Kaggle Data Science Bowl
Show steps
Develops hands-on skills in applying machine learning models to real-world problems.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Review the data and competition guidelines
- Develop a machine learning model
- Evaluate and improve your model
- Submit your predictions to the competition
Data Artifacts Management Plan
Show steps
Create a data artifacts management plan to demonstrate your understanding of data storage and versioning best practices.
Browse courses on
Data Artifacts
Show steps
Show steps
Show steps
- Define a strategy for storing and managing different types of data artifacts (e.g., models, training data, evaluation results).
- Implement version control mechanisms to track changes and ensure reproducibility.
- Establish guidelines for data access and security.
Follow Tutorials on Model Storage
Show steps
Following tutorials on model storage will help you understand how to store and track models and data artifacts.
Browse courses on
Model Storage
Show steps
Show steps
Show steps
- Find online tutorials or courses on model storage using Google Cloud Platform (GCP).
- Follow the tutorials to learn how to store and retrieve models and data artifacts using GCP services.
- Practice storing and retrieving models and data artifacts using GCP services.
Data Pipeline Project
Show steps
Builds a complete data pipeline for a machine learning project, including data ingestion, cleaning, and model training.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Define the data pipeline architecture
- Implement the data pipeline using a cloud platform
- Test and evaluate the data pipeline
- Document and present the data pipeline
Model Deployment Guide
Show steps
Create a comprehensive guide on model deployment to showcase your knowledge and provide a valuable resource to the community.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Document the steps involved in deploying a machine learning model into production.
- Provide insights on choosing the right deployment architecture and infrastructure.
- Discuss best practices for monitoring and maintaining deployed models.
Contribute to Open Source Model Repository
Show steps
Contribute to an open source model repository to share your expertise, collaborate with others, and enhance your understanding of model development and deployment.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open source model repository that aligns with your interests.
- Review the existing models and identify areas where you can contribute.
- Develop a model or improve an existing one, following the repository's guidelines.
- Submit a pull request with your contributions and engage in code reviews.
Career center
Learners who complete MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer is a data professional who is responsible for the development and maintenance of data pipelines. They work with data scientists to ensure that the data used to train machine learning models is clean, accurate, and consistent. This course will teach you the skills you need to become a successful Data Engineer, including how to design and build data pipelines, how to work with data scientists, and how to monitor the performance of machine learning models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for the development and deployment of machine learning models. They work with data scientists to identify the best machine learning algorithms for a given problem, and then they build and deploy the models. This course will teach you the skills you need to become a successful Machine Learning Engineer, including how to build and deploy machine learning models, how to work with data scientists, and how to monitor the performance of machine learning models.
Data Scientist
Data Scientist
A Data Scientist is responsible for the analysis and interpretation of data. They use statistical methods and machine learning algorithms to extract insights from data, and then they communicate these insights to stakeholders. This course will teach you the skills you need to become a successful Data Scientist, including how to analyze and interpret data, how to build and deploy machine learning models, and how to communicate your findings to stakeholders.
Data Analyst
Data Analyst
A Data Analyst is responsible for the collection, cleaning, and analysis of data. They use statistical methods and machine learning algorithms to find patterns and trends in data, and then they communicate these findings to stakeholders. This course will teach you the skills you need to become a successful Data Analyst, including how to collect and clean data, how to analyze and interpret data, and how to communicate your findings to stakeholders.
Business Analyst
Business Analyst
A Business Analyst is responsible for the analysis of business processes and the identification of opportunities for improvement. They use statistical methods and machine learning algorithms to analyze data and identify trends, and then they make recommendations to stakeholders on how to improve business processes. This course will teach you the skills you need to become a successful Business Analyst, including how to analyze data, how to identify trends, and how to make recommendations to stakeholders.
Software Engineer
Software Engineer
A Software Engineer is responsible for the design, development, and maintenance of software systems. They use programming languages and software development tools to create software that meets the needs of users. This course will teach you the skills you need to become a successful Software Engineer, including how to design and develop software systems, how to work with data, and how to deploy software.
Statistician
Statistician
A Statistician is responsible for the collection, analysis, and interpretation of data. They use statistical methods to find patterns and trends in data, and then they communicate these findings to stakeholders. This course will teach you the skills you need to become a successful Statistician, including how to collect and analyze data, how to interpret statistical results, and how to communicate your findings to stakeholders.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for the analysis of business processes and the identification of opportunities for improvement. They use mathematical models and optimization techniques to find the best way to allocate resources and make decisions. This course will teach you the skills you need to become a successful Operations Research Analyst, including how to analyze business processes, how to build mathematical models, and how to use optimization techniques.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for the analysis of financial data and the identification of investment opportunities. They use financial models and statistical techniques to find patterns and trends in financial data, and then they make recommendations to stakeholders on how to invest their money. This course will teach you the skills you need to become a successful Financial Analyst, including how to analyze financial data, how to build financial models, and how to make investment recommendations.
Market Research Analyst
Market Research Analyst
A Market Research Analyst is responsible for the collection, analysis, and interpretation of data about markets and consumers. They use statistical methods and market research techniques to find patterns and trends in data, and then they communicate these findings to stakeholders. This course will teach you the skills you need to become a successful Market Research Analyst, including how to collect and analyze data, how to interpret market research results, and how to communicate your findings to stakeholders.
Product Manager
Product Manager
A Product Manager is responsible for the development and management of products. They work with engineers, designers, and marketers to create products that meet the needs of users. This course will teach you the skills you need to become a successful Product Manager, including how to develop and manage products, how to work with engineers, designers, and marketers, and how to bring products to market.
Project Manager
Project Manager
A Project Manager is responsible for the planning, execution, and closure of projects. They work with stakeholders to define project goals, develop project plans, and track project progress. This course will teach you the skills you need to become a successful Project Manager, including how to plan and execute projects, how to work with stakeholders, and how to track project progress.
Technical Writer
Technical Writer
A Technical Writer is responsible for the creation and maintenance of technical documentation. They work with engineers and other technical experts to create documentation that is clear, concise, and accurate. This course will teach you the skills you need to become a successful Technical Writer, including how to create and maintain technical documentation, how to work with engineers and other technical experts, and how to communicate technical information to non-technical audiences.
Data Architect
Data Architect
A Data Architect is responsible for the design and management of data systems. They work with data engineers and other technical experts to create data systems that are scalable, reliable, and secure. This course will teach you the skills you need to become a successful Data Architect, including how to design and manage data systems, how to work with data engineers and other technical experts, and how to ensure that data systems meet the needs of users.
Database Administrator
Database Administrator
A Database Administrator is responsible for the maintenance and performance of databases. They work with database engineers and other technical experts to ensure that databases are available, reliable, and secure. This course will teach you the skills you need to become a successful Database Administrator, including how to maintain and performance databases, how to work with database engineers and other technical experts, and how to ensure that databases meet the needs of users.
For more career information including salaries, visit:
OpenCourser.com/course/5rg501/mlops1
Reading list
We've selected seven 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 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform.
Provides a hands-on guide to MLOps, covering topics such as data engineering, model training, and deployment. It also includes code examples and exercises.
Provides a catalog of design patterns for ML systems. It covers patterns for data preprocessing, feature engineering, model training, and deployment.
Provides a quick reference to data pipelines, covering topics such as data ingestion, data cleaning, and data transformation. It also includes code examples and exercises.
Provides a comprehensive overview of ML in Python, covering topics such as data preprocessing, feature engineering, model training, and evaluation. It also includes code examples and exercises.
Provides a guide to deep learning using Python libraries such as Fastai and PyTorch. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a guide to natural language processing using Python libraries such as PyTorch. It covers topics such as text preprocessing, tokenization, and machine translation.
Provides a guide to computer vision using Python libraries such as OpenCV. It covers topics such as image processing, object detection, and facial recognition.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5rg501/mlops1
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Deployment of Machine Learning Models
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
Develops skills in data engineering and data pipelines, which are essential for implementing machine learning models in production
Offers hands-on experience with real-world tools and technologies used in data engineering and ML operations
Taught by a team of experts with decades of experience in deploying machine learning models
Part of a series of three courses that provide a comprehensive understanding of Machine Learning Operations using Google Cloud Platform
Requires explicit prerequisites, so students may need to take other courses first
May require access to specific software or tools not readily available in a typical household or library
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