Getting Machine Learning (ML) Models into Production: A New Approach

The biggest bottleneck in AI right now is getting machine learning (ML) models into production. According to Capgemini Research Institute, 72% of a cohort of organizations that began AI pilots before 2019 haven’t deployed a single application into production.


Here’s why getting ML models into production is challenging.


Data scientists did not learn the fundamentals of Software Engineering. According to LinkedIn, there are only 11,400 data scientists employed in the US. On the other hand, there are over 4.4 million software engineers. Software engineers understand how to formulate production-level code.


A Data Scientist would have to understand system development and system operations to handle the unique complexities of the practical application of ML. Unfortunately, this skill set is rare and super expensive.

Luckily, Salynt Inc., a little-known start-up, has developed a low code solution to solve this challenge. The start-up founders are three Ex-Booz Allen Hamilton employees (Jeremy Lawson, CEO. James Dempsey, CTO. Ruthe Huang, CDS.) All three have extensive backgrounds in Data Science or Software Engineering. Their CTO, James Dempsey, says, “our low-code platform provides the modules that automate the tasks normally performed by software engineers to put code into production while allowing the Data Scientist to augment our codebase as necessary.”


Salynt’s platform will prepare developers for current and future workplace challenges for implementing AI initiatives. Data Scientists will be able to rapidly produce automated analytics, which gives back time to Software Engineers to focus on more complex system operation tasks.


Sign up today to demo Salynt’s application. Software Engineers can develop their machine learning engineering skills, and Data Scientists can learn to roll out ML models fit for production that benefit their company and their users.

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