This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataflow and Dataprep to using BigQuery for preprocessing tasks.
Machine Learning in the Enterprise
About this Course
What you will learn
Describe data management, governance, and preprocessing options
Identify when to use Vertex AutoML, BigQuery ML, and custom training
Implement Vertex Vizier Hyperparameter Tuning
Explain how to create batch and online predictions, setup model monitoring, and create pipelines using Vertex AI
Skills you will gain
- Machine Learning
- Cloud Computing
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
This module provides an overview of the course and its objectives.
Understanding the ML Enterprise Workflow
This module discusses the ML enterprise workflow and the purpose of each step.
Data in the Enterprise
This module reviews Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub.
Science of Machine Learning and Custom Training
This module reviews the art and science of machine learning and neural networks. We'll also discuss how to train custom ML models using Vertex AI.
Vertex Vizier Hyperparameter Tuning
In this module we discuss how to do hyperparameter tuning using Vertex AI Vizier.
Prediction and Model Monitoring Using Vertex AI
This module covers Vertex AI prediction and model monitoring. We'll first discuss batch and online predictions using pre-built and custom containers, then we'll review model monitoring, which is a service that helps manage the performance of your ML models.
Vertex AI Pipelines
This module discusses Vertex AI pipelines and how to build them to orchestrate your ML workflow.
Best Practices for ML Development
This module reviews best practices for a number of different machine learning processes in Vertex AI.
This module is a summary of the Machine Learning in the Enterprise course.
This module is a summary of the Machine Learning on Google Cloud course series.
- 5 stars70.67%
- 4 stars22.88%
- 3 stars4.47%
- 2 stars0.90%
- 1 star1.04%
TOP REVIEWS FROM MACHINE LEARNING IN THE ENTERPRISE
Best course in this specialization. Lak is such a wonderful instructor.
It would be nice to have more complex datasets where predictions would be more meaningful.
The course is quite good and have balance of theory and labs. It is useful course for beginners.
This was a quite dense specialization. The instructors were very clear and the assignments helpful.
Frequently Asked Questions
Can I preview a course before enrolling?
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
When will I have access to the lectures and assignments?
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
What will I get when I enroll?
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
When will I receive my Course Certificate?
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
Why can’t I audit this course?
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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Last Updated: September 1, 2015
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