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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
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What's inside
Syllabus
Introduction
Understanding the ML Enterprise Workflow
Data in the Enterprise
Science of Machine Learning and Custom Training
Read more
Syllabus
Introduction
Understanding the ML Enterprise Workflow
Data in the Enterprise
Science of Machine Learning and Custom Training
Read more
Traffic lights
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review the fundamentals of ML
Show steps
Refreshes fundamental concepts in ML to help bridge any foundational gaps before taking the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review linear regression and logistic regression
- Understand the concept of overfitting and underfitting
Explore ML case studies on Kaggle
Show steps
Provides hands-on experience with ML problem-solving.
Browse courses on
Kaggle Competitions
Show steps
Show steps
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
Show steps
Engages students with peers, fostering collaboration and knowledge exchange.
Show steps
Show steps
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
Show steps
Applies ML concepts to solve a specific problem and reinforces the ML workflow.
Browse courses on
Capstone Project
Show steps
Show steps
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 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.
Machine Learning Engineer
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.
Business Analyst
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.
Software Engineer
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.
Product Manager
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.
Marketing 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.
Operations 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.
Financial Analyst
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.
Consultant
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.
Researcher
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.
Teacher
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.
Technical Writer
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.
Sales Engineer
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.
Data Scientist
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.
Customer Success Manager
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.
For more career information including salaries, visit:
OpenCourser.com/course/xeqn3a/machine
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.
Provides a comprehensive overview of machine learning algorithms and techniques. It uses popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as statistical pattern recognition, neural networks, and support vector machines.
Provides a comprehensive overview of data mining for business intelligence. It covers topics such as data preparation, data mining algorithms, and data visualization.
Provides a practical introduction to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a probabilistic perspective on machine learning. It covers topics such as Bayesian inference, Gaussian processes, and Markov chain Monte Carlo.
Provides a practical introduction to machine learning using R. It covers topics such as data preparation, model selection, and model evaluation.
Introduces deep learning concepts and techniques. It uses the Fastai library, which makes deep learning accessible to coders without a PhD.
Provides a comprehensive introduction to reinforcement learning. It covers topics such as Markov decision processes, value functions, and policy gradients.
Provides a practical introduction to machine learning. It covers topics such as data preparation, model selection, and model evaluation.
Provides a practical introduction to data science for business managers. It covers topics such as data collection, data analysis, and data visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xeqn3a/machine
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