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As machine learning (ML) has delivered business value on common use cases such as detecting fraud, recommending products and predicting customer churn, more companies are seeking to apply it to innovative use cases – only to find the tools and processes to be disconnected, unreliable and unscalable.

These challenges are creating friction that is inhibiting the complex process of developing ML. The friction is evident - in a recent survey of over 500 ML practitioners, 68% admitted abandoning 40-80% of their experiments in the past year.

Explore this report to learn about 3 characteristics ML practitioners should consider when evaluating tools to reduce friction and accelerate the ML development process.

Vendor:
Comet
Posted:
Jun 17, 2022
Published:
Jun 17, 2022
Format:
PDF
Type:
Analyst Report

This resource is no longer available.