Machine Learning (ML) has emerged as a game-changing technology, but many businesses struggle to translate ML investments into tangible business value.
The fact is, most ML models fail to make it into production environments. Certain risks along the development and deployment lifecycle hold companies back.
A Guide to Mitigating Risks
In this e-book we aim to help companies avoid the three most common pitfalls in ML adoption to make the most of the exciting developments we are living through.
- Pitfall 1: Picking The Wrong Evaluation Criteria
- Pitfall 2: Not Deeply Exploring Your Data
- Pitfall 3: Removing Humans From The Loop
For each pitfall we outline simple steps to guide future decision-making and ML program structure. With these ideas in mind, you’ll be prepared to set your company up for successful implementation, adding measurable value to your business.