- Joined
- May 24, 2024
- Messages
- 264
- Reaction score
- 451
- Points
- 43
- Thread Author
-
- #1
Managing Machine Learning Projects. From design to deployment
The go-to guide in machine learning projects from design to production. No ML skills required!
In Managing Machine Learning Projects, you will learn essential machine learning project management techniques, including:
The go-to guide in machine learning projects from design to production. No ML skills required!
In Managing Machine Learning Projects, you will learn essential machine learning project management techniques, including:
- Understanding an ML project's requirements
- Setting up the infrastructure for the project and resourcing a team
- Working with clients and other stakeholders
- Dealing with data resources and bringing them into the project for use
- Handling the lifecycle of models in the project
- Managing the application of ML algorithms
- Evaluating the performance of algorithms and models
- Making decisions about which models to adopt for delivery
- Taking models through development and testing
- Integrating models with production systems to create effective applications
- Steps and behaviours for managing the ethical implications of ML technology
About the technology
Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, around 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you would take with standard software projects.
You need to account for large and diverse data resources, evaluate and track multiple separate models, and handle the unforeseeable risk of poor performance. Never fear -- this book lays out the unique practices you will need to ensure your projects succeed!
Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, around 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you would take with standard software projects.
You need to account for large and diverse data resources, evaluate and track multiple separate models, and handle the unforeseeable risk of poor performance. Never fear -- this book lays out the unique practices you will need to ensure your projects succeed!