3 Strategies to Build Resilient ML Models

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.

In this e-book we’ll cover:

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.

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About the Author

Patrick Steeves
Product Manager

Lisa Avvocato
VP, Global Marketing

Lisa Avvocato is a veteran product marketer/moderator specializing in AI and ML technologies. She’s passionate about the interaction of machine learning and digital transformation strategies to reduce inefficiencies and drive sustainability. With over 15 years of experience in Enterprise SaaS technology, she has worked across a diverse set of industries including retail, education, manufacturing, and healthcare.

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