Using ML Canvas to Define Machine Learning Objectives
- Posted by GM, Digital Solutions
- On January 19, 2021
A Daitan Case Study
It’s essential to keep goals and data at the center of your AI projects. Machine Learning goals require clarity, communication, and forward-thinking. The ML Canvas has helped us with a variety of customers prove results efficiently and successfully. This particular case study allows us to see the full story from start to finish of how the ML Canvas helped a customer realize their true vision of success, and define their machine learning goals.
Even if you have the right experts on your team, sufficient resources to move quickly, and stakeholders for your AI project, your objectives and machine learning goals are what will be the foundation of your final product, and ultimately, your success.
Defining Machine Learning Goals
This case study sheds light on the challenges a US-based telecom equipment provider overcame despite their large volumes of data and need to filter through their alert systems. To accomplish this, they were in need of a tool that would allow communication across teams, and across countries.
The ML Canvas platform allowed for efficient two-way communication that enabled the teams to find alignment and a productive approach at an accelerated rate, and reach common ground on a final product – in record time. The case study will also outline the ML Canvas’ supporting features like:
- Solve technical problems
- Support efficient iterations
- Accurately define the value proposition
This case study will also outline the ML Canvas’ ability to be a tool that allows communication across teams and across countries. Download the case study below to get the full picture of ML Canvas’ capabilities: