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Machine Learning in the Enterprise

This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.

The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

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What's inside

Syllabus

Introduction

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.

Course Summary

This module is a summary of the Machine Learning in the Enterprise course.

Series Summary

This module is a summary of the Machine Learning on Google Cloud course series.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills and knowledge in enterprise-scale machine learning for data engineers, data scientists, and machine learning engineers

Relevant to industry

Offers hands-on labs and interactive materials

Introduces Google enterprise products for data management and governance

Builds a strong foundation for beginners while also strengthening an existing foundation for intermediate learners

Emphasizes data management best practices

Focuses on custom training, which is often the best approach for achieving specific objectives in ML projects

Covers model monitoring, which is an essential aspect of ML project lifecycle

May require prior knowledge of ML concepts for some learners

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Reviews summary

Hands-on machine learning for the enterprise

Learners say that Machine Learning in the Enterprise, a course from Google Cloud, provides a great hands-on learning experience about the principles of machine learning in a business environment. The course is engaging, well-paced, and includes a variety of interactive labs and self-assessments making it a great choice for anyone looking to build practical skills in this field.

Includes a variety of labs to develop practical skills

"Very well designed and defined..with great hands-on exercise."

"The course is difficult. You may need to review some sections because off the amount of information."

"The labs are done in a way where you can get familiar with TensorFlow and Python programming, without having to know Python programming. Awesome intro if you want to be familiar with GCP ML."

Provides valuable knowledge and skills

"I have learned a lot of things about Machine Learning from this course."

"Iam very satisface, the contents, methodology, tools were exceletents, and the translations were very good im not was problems with the platform is very easy and intuitive"

"This course is so really good to learn about the general knowledge and skill of Data Science like optimization batch or regularization and so on with Google Cloud Platform."

"Excellent conclusion to a really enjoyable specialization. "

Can be challenging for beginners

"A lot of inaccurate data, please check deep learning ai specialization for more accurate info. this is good for introducing you to GCP not the concepts of AI"

"Very bad english subtitles. For non-english speakers, the subtitles doesn't help, but it confuse what the teacher is explaining. It takes me a lot of time to understand some parts of the course"

"All eight labs had defects/ bugs. In four (4) labs the defects prevented me from completing the labs."

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 Machine Learning in the Enterprise with these activities:

Find a mentor who can provide guidance on your machine learning journey

Show steps

Finding a mentor who can provide guidance on your machine learning journey will help you learn more quickly and avoid common pitfalls.

Browse courses on Machine Learning

Show steps

Identify your goals and what you hope to gain from a mentor.
Network with people in your field.
Attend industry events.
Reach out to potential mentors and ask for their advice.

Review basic machine learning concepts

Show steps

Reviewing basic machine learning concepts will help you understand the material covered in this course more easily.

Browse courses on scikit-learn

Show steps

Read through the scikit-learn user guide.
Complete a few tutorials on pandas.
Review your notes from a previous machine learning course.

Attend a machine learning conference or meetup

Show steps

Attending a machine learning conference or meetup will help you connect with other people who are interested in machine learning and learn about the latest trends in the field.

Browse courses on Machine Learning

Show steps

Find a conference or meetup that is relevant to your interests.
Register for the event.
Attend the event and participate in the discussions.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Follow a tutorial on how to build a machine learning model

Show steps

Following a tutorial on how to build a machine learning model will help you apply the concepts you learn in this course to a practical problem.

Browse courses on Machine Learning Model

Show steps

Find a tutorial that is appropriate for your skill level.
Follow the tutorial step-by-step.
Experiment with different parameters and settings.

Solve practice problems on machine learning

Show steps

Solving practice problems on machine learning will help you improve your understanding of the material covered in this course and develop your problem-solving skills.

Browse courses on Machine Learning

Show steps

Find a set of practice problems that are appropriate for your skill level.
Solve the problems on your own.
Check your answers against the solutions provided.

Write a blog post about a machine learning project you worked on

Show steps

Writing a blog post about a machine learning project you worked on will help you reflect on what you learned and share your knowledge with others.

Browse courses on Machine Learning

Show steps

Choose a project to write about.
Write a draft of your blog post.
Get feedback on your draft from a friend or colleague.
Publish your blog post.

Contribute to an open-source machine learning project

Show steps

Contributing to an open-source machine learning project will help you learn about the latest developments in the field and develop your coding skills.

Browse courses on Machine Learning

Show steps

Find an open-source machine learning project that you are interested in.
Read the project's documentation.
Make a contribution to the project.

Start a project to build a machine learning model to solve a real-world problem

Show steps

Starting a project to build a machine learning model to solve a real-world problem will help you apply the concepts you learn in this course to a practical problem and develop your project management skills.

Browse courses on Machine Learning

Show steps

Define the problem you want to solve.
Gather data to train your model.
Choose a machine learning algorithm to use.
Train and evaluate your model.
Deploy your model.

Career center

Learners who complete Machine Learning in the Enterprise will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

The course Machine Learning in the Enterprise is a superb choice for someone looking to become a Machine Learning Engineer. A Machine Learning Engineer is responsible for overseeing ML projects. They take a business need and translate it into a solution that can be deployed in a production environment. This course goes through the ML enterprise workflow. It discusses the purpose of each step and which tools you can use. The course also covers how to train custom ML models and do hyperparameter tuning. These are skills all Machine Learning Engineers should have.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists are responsible for understanding patterns in data and making predictions. For this reason, Machine Learning in the Enterprise would be a useful course for Data Scientists to take. This course discusses the art and science of machine learning and neural networks, helping Data Scientists build a strong foundation in the field. The course also covers how to train custom ML models and do hyperparameter tuning. This is an important skill for a Data Scientist.

See salaries and explore the career path for Data Scientist

Data Engineer

Data Engineers are responsible for building and maintaining data pipelines. The course Machine Learning in the Enterprise may be useful for someone looking to become a Data Engineer. This course goes over the ML enterprise workflow and the purpose of each step. It also discusses Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub. A Data Engineer is responsible for setting up and managing this type of software.

See salaries and explore the career path for Data Engineer

AI Engineer

AI Engineers are responsible for designing and maintaining AI systems. Machine Learning in the Enterprise would be a useful course for someone interested in this career path. This course goes over the ML enterprise workflow and the purpose of each step. It also discusses the art and science of machine learning and neural networks. This course can help build a foundation in AI.

See salaries and explore the career path for AI Engineer

AI Researcher

AI Researchers are responsible for developing new AI algorithms. Machine Learning in the Enterprise may help someone become an AI Researcher. This course discusses the art and science of machine learning and neural networks. Artificial Intelligence Researchers should have a solid understanding of this topic and this course can help them build a foundation.

See salaries and explore the career path for AI Researcher

Machine Learning Architect

Machine Learning Architects are responsible for designing and building the machine learning infrastructure. Machine Learning in the Enterprise can help teach someone how to become a Machine Learning Architect. This course goes over the ML enterprise workflow and the purpose of each step. It also discusses Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub. Machine Learning Architects need an understanding of this kind of software.

See salaries and explore the career path for Machine Learning Architect

Software Engineer

Software Engineers are responsible for developing and maintaining software applications. The course Machine Learning in the Enterprise is a useful course for Software Engineers to take. This course covers how to train custom ML models. It also discusses hyperparameter tuning and Vertex AI pipelines. This course can help Software Engineers increase their skillset and become more relevant in the modern tech landscape.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts are responsible for cleaning, preparing, and analyzing data. Machine Learning in the Enterprise could be a useful course for someone looking to become a Data Analyst. This course discusses Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub. Data Analysts are responsible for using this type of software.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts are responsible for using mathematical and statistical models to analyze data. Machine Learning in the Enterprise may be useful for someone who is looking to become a Quantitative Analyst. This course discusses the art and science of machine learning and neural networks. Quantitative Analysts need a strong understanding of these topics.

See salaries and explore the career path for Quantitative Analyst

Business Analyst

Business Analysts are responsible for understanding the business needs of an organization. The course Machine Learning in the Enterprise may be helpful for someone looking to become a Business Analyst. This course goes over the ML enterprise workflow and the purpose of each step. This course can help Business Analysts appreciate the technical side of making these models.

See salaries and explore the career path for Business Analyst

Technical Writer

Technical Writers are responsible for writing documentation for technical products. Machine Learning in the Enterprise may be useful for anyone looking to become a Technical Writer. This course goes over the ML enterprise workflow and the purpose of each step. It also discusses the art and science of machine learning and neural networks. This knowledge can help Technical Writers write more precise documentation.

See salaries and explore the career path for Technical Writer

Product Manager

Product Managers are responsible for managing the development and launch of new products. Machine Learning in the Enterprise may be useful for someone looking to become a Product Manager. This course goes over the ML enterprise workflow and the purpose of each step. Product Managers need this knowledge to understand how products are built.

See salaries and explore the career path for Product Manager

Consultant

Consultants are responsible for providing advice to businesses. Machine Learning in the Enterprise may be helpful for someone looking to become a Consultant. This course goes over the ML enterprise workflow and the purpose of each step. This can help Consultants understand their clients better.

See salaries and explore the career path for Consultant

Teacher

Teachers are responsible for educating students. Machine Learning in the Enterprise may be useful for someone looking to become a Teacher. This course goes over the art and science of machine learning and neural networks. This knowledge can help Teachers better prepare students for their future careers.

See salaries and explore the career path for Teacher

Student

Students are enrolled in an educational program. Machine Learning in the Enterprise may be helpful for a student. This course goes over the ML enterprise workflow and the purpose of each step. It also discusses the art and science of machine learning and neural networks. This knowledge can help Students learn more about the field.

See salaries and explore the career path for Student

Reading list

We've selected 12 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 Machine Learning in the Enterprise.

Worship in an Age of Anxiety

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Provides a comprehensive overview of machine learning, with a focus on practical applications.

Machine Learning in the Enterprise

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Hands-on machine learning for the enterprise

Activities

Career center

Reading list

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