How to Accelerate Machine Learning Development

Achieve faster time to value with Ai using strategic investments in people, tools, and data.

50% of leaders struggle to move their Ai projects to production, citing lack of skills, difficulty hiring, and data quality as barriers to entry.  - Wall Street Journal

As it can take several months to build, test, and put a machine learning model into production, many companies do not quickly benefit from Ai. Furthermore, it can be costly to try building a machine learning model on your own. 

Companies can accelerate machine learning development by strategically deploying resources at critical stages in the process. Teams can speed the development process by about 65% and increase the likelihood of moving solutions into production by wisely leveraging these resources. 

In this white paper, we will share some of what we have learned in over 17 years of building and deploying machine learning solutions for enterprise organizations across multiple industries and use cases. 

Readers will learn: 
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• The people, data, tools, and time it takes to develop a machine learning model – from proof of concept to production.
• Ways teams can accelerate machine learning projects to achieve faster time to value with Ai
• Key factors to consider when teams are preparing to build Ai and put it into production