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Machine Learning in the Enterprise

Google Cloud

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.

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.

This course is no longer available. Find something similar by browsing:
Machine Learning ML Workflow Real-World Approach AutoML BigQuery ML Custom Training Vertex Vizier Hyperparameter Tuning

What's inside

Syllabus

Introduction
Understanding the ML Enterprise Workflow
Data in the Enterprise
Science of Machine Learning and Custom Training
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides new and unique perspectives on the machine learning lifecycle, offering a rare viewpoint in the industry
Designed for professionals in the machine learning field, particularly those seeking to enhance their expertise in managing the machine learning workflow

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Reviews summary

Practical machine learning in the enterprise

According to students, this course offers a strong practical approach to Machine Learning within an enterprise context. Many find the real-world case study invaluable, especially its focus on Vertex AI, AutoML, and BigQuery ML for deployment and adherence to best practices. While widely lauded for its clarity and direct applicability to work, some advanced learners desired more in-depth technical dives or advanced deployment strategies. It's largely seen as excellent for professionals bridging ML theory to industry, providing actionable insights, though a few noted minor UI differences due to platform updates.
Best suited for intermediate learners or those new to enterprise ML.
"It's definitely for professionals, not beginners."
"Maybe it's good for intermediate learners, but not for advanced practitioners looking for deep dives."
"It gave me a good framework to think about ML projects in a structured way. Definitely recommended for anyone looking to formalize their understanding."
Provides invaluable insights into Vertex AI, AutoML, and BigQuery ML.
"The modules on Vertex AI Pipelines and model monitoring are particularly useful."
"Absolutely essential for MLOps. The hands-on labs with Vertex AI are invaluable."
"I appreciated the emphasis on best practices. The sections on AutoML and custom training were excellent."
"I liked the comparison between AutoML, BigQuery ML, and custom training. It helped clarify when to use which."
Directly applicable for real-world enterprise ML projects.
"This course is incredibly practical and directly applicable to my work."
"The case study approach made complex concepts easy to grasp. I finally understand how to deploy ML models in a real enterprise setting."
"I loved the practical assignments that simulated real-world scenarios. This course truly empowers you to implement ML solutions effectively."
"It covers a lot of ground in a practical way. The emphasis on real-world use cases and the ML workflow makes it highly relevant."
Minor discrepancies due to evolving cloud platform UIs.
"The recent updates to Vertex AI mean some UIs might be slightly different than in the videos, but it's manageable."
"Some parts felt a bit outdated or could use more recent examples, especially regarding specific platform UIs."
May lack depth for highly experienced practitioners.
"I found the practical examples somewhat lacking in depth for a truly 'enterprise' level. I expected more advanced deployment strategies."
"I found the course quite basic for what it promises. It didn't provide enough advanced strategies for large-scale deployments as I hoped."
"It's more of a strategic overview. Good for managers or architects, less for hands-on developers unless they're new to the enterprise ML ecosystem."
"The content is okay for someone new to the *idea* of enterprise ML, but if you have some experience, you might find it too superficial."

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:
Review the fundamentals of ML
Refreshes fundamental concepts in ML to help bridge any foundational gaps before taking the course.
Browse courses on Machine Learning
Show steps
  • Review linear regression and logistic regression
  • Understand the concept of overfitting and underfitting
Explore ML case studies on Kaggle
Provides hands-on experience with ML problem-solving.
Browse courses on Kaggle Competitions
Show steps
  • Find a relevant ML case study on Kaggle
  • Follow the tutorial to understand the ML workflow
  • Experiment with different ML models and parameters
Participate in online discussion forums
Engages students with peers, fostering collaboration and knowledge exchange.
Show steps
  • Join online discussion forums related to ML
  • Ask questions and share insights
One other activity
Expand to see all activities and additional details
Show all four activities
Build an ML model for a real-world problem
Applies ML concepts to solve a specific problem and reinforces the ML workflow.
Browse courses on Capstone Project
Show steps
  • Define the problem and gather data
  • Explore and preprocess the data
  • Train and evaluate different ML models
  • Deploy the best model and monitor its performance

Career center

Learners who complete Machine Learning in the Enterprise will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts collect, organize, interpret, and present data to help businesses understand their market, customers, and operations. Machine Learning in the Enterprise may be useful for learning how to prepare and manage data for use in machine learning, as well as best practices for developing and deploying machine learning applications.
See salaries and explore the career path for Data Analyst
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain machine learning applications. The Machine Learning in the Enterprise course may be useful for learning about the workflow and processes enterprises use regarding machine learning.
See salaries and explore the career path for Machine Learning Engineer
Business Analyst
Business Analysts solve business problems by applying analytical and problem-solving skills to understand requirements, processes, and technology. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to solve business problems.
See salaries and explore the career path for Business Analyst
Software Engineer
Software Engineers design, develop, test, and maintain software applications. The Machine Learning in the Enterprise course may be useful for learning how to apply machine learning to software applications.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers are responsible for the development and management of products. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to improve products and services.
See salaries and explore the career path for Product Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing plans to promote products and services. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to target marketing campaigns and improve customer engagement.
See salaries and explore the career path for Marketing Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to improve operational efficiency and productivity.
See salaries and explore the career path for Operations Manager
Financial Analyst
Financial Analysts make recommendations on investments and financial planning. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to analyze financial data and make more informed decisions.
See salaries and explore the career path for Financial Analyst
Consultant
Consultants provide advice and expertise to organizations on a variety of topics. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to solve business problems and improve decision-making.
See salaries and explore the career path for Consultant
Researcher
Researchers conduct scientific research to develop new knowledge and technologies. The Machine Learning in the Enterprise course may be useful for learning about the latest advances in machine learning and how they can be applied to a variety of fields.
See salaries and explore the career path for Researcher
Teacher
Teachers educate students at all levels, from elementary school to college. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to improve teaching and learning.
See salaries and explore the career path for Teacher
Technical Writer
Technical Writers create documentation and other materials to explain technical information to a non-technical audience. The Machine Learning in the Enterprise course may be useful for learning how machine learning works and how to explain it to others.
See salaries and explore the career path for Technical Writer
Sales Engineer
Sales Engineers help customers implement and use technology solutions. The Machine Learning in the Enterprise course may be useful for learning how machine learning can be used to improve business outcomes and solve customer problems.
See salaries and explore the career path for Sales Engineer
Data Scientist
A Data Scientist combines machine learning, statistics, data analysis, and algorithms to solve business problems. The Machine Learning in the Enterprise course may be useful for learning about the workflow and processes enterprises use regarding machine learning.
See salaries and explore the career path for Data Scientist
Customer Success Manager
Customer Success Managers help customers achieve their desired outcomes with a product or service. The Machine Learning in the Enterprise course may be useful for learning about the benefits of machine learning and how it can be used to improve customer satisfaction and retention.
See salaries and explore the career path for Customer Success Manager

Reading list

We've selected ten 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.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a comprehensive overview of machine learning algorithms and techniques. It uses popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as statistical pattern recognition, neural networks, and support vector machines.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Machine Learning for Business Analytics
Save
Provides a comprehensive overview of data mining for business intelligence. It covers topics such as data preparation, data mining algorithms, and data visualization.
Machine Learning for Business Analytics: Concepts,...
Hardcover
$$$$
Machine Learning for Business Analytics: Concepts,...
Kindle Edition
$$$$
Cover image
Deep Learning with Python, Second Edition
Save
Provides a practical introduction to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
Probabilistic Machine Learning
Save
Provides a probabilistic perspective on machine learning. It covers topics such as Bayesian inference, Gaussian processes, and Markov chain Monte Carlo.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Machine Learning with R
Save
Provides a practical introduction to machine learning using R. It covers topics such as data preparation, model selection, and model evaluation.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Deep Learning for Coders with fastai and PyTorch
Save
Introduces deep learning concepts and techniques. It uses the Fastai library, which makes deep learning accessible to coders without a PhD.
Deep Learning for Coders with Fastai and PyTorch:...
Paperback
$$
Deep Learning for Coders with fastai and PyTorch:...
Kindle Edition
$$
Cover image
Reinforcement Learning, second edition
Save
Provides a comprehensive introduction to reinforcement learning. It covers topics such as Markov decision processes, value functions, and policy gradients.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Machine Learning South Asia Edition
Save
Provides a practical introduction to machine learning. It covers topics such as data preparation, model selection, and model evaluation.
Machine Learning
Paperback
$$$
Cover image
Data Science for Business
Save
Provides a practical introduction to data science for business managers. It covers topics such as data collection, data analysis, and data visualization.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$

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Course image
Time
128 hours
Level
Introductory
Via
Pluralsight
Instructor
Google Cloud
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides new and unique perspectives on the machine learning lifecycle, offering a rare viewpoint in the industry
Designed for professionals in the machine learning field, particularly those seeking to enhance their expertise in managing the machine learning workflow
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