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MLOps1 (GCP)
Deploying AI & ML Models in Production using Google Cloud Platform
John Elder, IV,
Peter Bruce,
Shree Taylor,
Bryce Pilcher,
Allison Marrs,
Ramzi Ziade,
Greg Carmean,
LeAnna Kent,
Henry Mead,
Kuber Deokar,
and
Janet Dobbins
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:
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Activities
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We're preparing activities for
MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform.
These are activities you can do either before, during, or after a course.
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
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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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