Preparing data and putting predictive models to work usually requires a numerous set of different skills, tight collaborations with business and IT professionals, and long and intensive deployment efforts to operationalize machine learning within a business process.
Jen Underwood, from Impact Analytix, a recognized expert in the industry, takes a look at how to expedite the entire machine learning project life-cycle by evaluating new approaches and technologies. Her in depth guide, take a real-life example to provide practical and actionable recommendations to successful machine learning adoption.
This guide covers:
- What modern machine learning life cycle is
- Define machine learning objectives for success
- How to assess, explore and prepare data for ML
- How to automate and operationalize ML modeling