Companies are investing billions of dollars into the development and industrialization of machine learning (ML) models to improve their products, enhance customer experience, reduce cost and prevent fraud. However, 90 percent of these models never make it into production. In other words, only 10 percent of ML efforts actually ends up producing something useful for the company.

Many reasons account for the difference between data science aspirations and IT reality. Deploying ML models at scale is hard and often takes longer than expected. Complexity and lack of automation in both data pipelines and management of model lifecycles present unique challenges. In addition, speed in deployment requires a number of best practices such as abstraction from infrastructure, self-service data support and the proper use of standard development methods.

Daman can help you with the advanced data engineering that is central to industrializing your ML operation. Our solutions support the delivery of exploratory data for data scientists, accelerate data preparation for model scoring and backtesting, and enable the development of data pipelines for inline model deployment. When complemented with advanced model governance and automated model monitoring and scoring, you will achieve success with models that offer accuracy, precision, speed, and minimal drift.