Machine Learning in the Enterprise

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.The team is presented with three options to build machine learning models for two specific use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to exporting a trained model.

You will build a custom training machine learning model, which allows you to build a container image with little knowledge of Docker.

The case study team examines hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Vertex AI can be used to manage ML models.

• 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

Syllabus

Syllabus - What you will learn from this course
Week 1
Introduction
Understanding the ML Enterprise Workflow
Data in the Enterprise
Science of Machine Learning and Custom Training
Week 2
Vertex Vizier Hyperparameter Tuning
Prediction and Model Monitoring Using Vertex AI
Week 3
Vertex AI Pipelines
Best Practices for ML Development
Course Summary
Series Summary

FAQ

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.

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.

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Last Updated: September 1, 2015

Reviews

This course tells about the different aspect of usage of art and science in machine learning.

This final course is also very good. Embedding is my favorite part and Lak is my favorite instructor.

Thanks Googlers! Looking forward to the next five courses.

Nice course however I think it suits folks who have good exposure of ML to take complete advantage of the techniques

It would be nice to have more complex datasets where predictions would be more meaningful.