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Machine Learning in the Enterprise

Google Cloud Training

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.

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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

Applying machine learning in the enterprise

According to learners, this course provides a strong foundation for implementing machine learning workflows within a business context using Google Cloud tools. Many highlight the practical, real-world case study approach and the focus on the enterprise ML workflow as particularly valuable. Students found the coverage of specific services like Vertex AI, BigQuery ML, and AutoML to be insightful for understanding enterprise-level solutions. While the course is generally well-received, some reviewers noted that it serves as a high-level overview and may require supplemental learning or prior experience, especially regarding deeper theoretical concepts or hands-on lab details, which some found occasionally challenging or inconsistent.

Assumes some prior knowledge of ML fundamentals.

"Recommended for those who already have a basic understanding of machine learning concepts."

"As a beginner, I found some parts moved quite fast, assuming prior ML exposure."

"If you're new to ML entirely, you might need to supplement this with foundational courses first."

Hands-on labs are provided, though some found them inconsistent.

"The labs provided practical experience with the tools discussed in the lectures."

"Some labs seemed a bit disconnected from the lectures or had minor issues that needed troubleshooting."

"I wish the labs were a bit more challenging and guided less, but they were helpful for seeing the tools."

Provides breadth but sometimes lacks depth on specific topics.

"It's a great introduction to the enterprise ML landscape, but doesn't go deep into the algorithms."

"Felt more like a strategic overview of using Google Cloud for ML rather than a deep technical dive."

"Could use more in-depth coverage on complex topics or optimization techniques."

Highlights key Google Cloud ML tools for enterprise use.

"Detailed explanations of Vertex AI, BigQuery ML, and AutoML are a major strength."

"Learning how to use Vertex AI for model training and deployment was extremely useful."

"The course gave me a good overview of the different ML tools available on Google Cloud for businesses."

Course effectively covers the end-to-end ML workflow.

"This course effectively walks through the entire Machine Learning workflow in an enterprise setting."

"I really appreciated the focus on the *workflow* from data prep to monitoring, not just model building."

"The real-world case study helped me understand how ML projects fit into a business context."

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

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

Browse courses on scikit-learn

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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

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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

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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

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Follow a tutorial on how to build a machine learning model

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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

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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

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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

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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

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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

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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

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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

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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.

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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

Here's a deal for you

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Applying machine learning in the enterprise

Activities

Career center

Reading list

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