
Managing Machine Learning Projects
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- 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 behaviors for managing the ethical implications of ML technology
- Set up infrastructure and resource a team
- Bring data resources into a project
- Accurately estimate time and effort
- Evaluate which models to adopt for delivery
- Integrate models into effective applications
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