An in-depth look at how our end-to-end MLOps solution on AWS reduces machine learning lifecycle steps by over 50%.
60% of machine learning models never make it to production. Miscommunication between data scientists and operations, the abundance of tools and methodologies and the lack of an industry standard complicate the path from development to production. There are existing services that can help streamline model development, but it doesn’t support two other machine learning challenges: lifecycle management and integration.
We developed the Model Factory Framework to complete the MLOps lifecycle using a proven methodology to help you get your models into production and delivering value. In this white paper, you’ll learn how our proven framework addresses the biggest challenges of operationalizing machine learning models so that you can achieve accurate insights faster.
Topics covered include:
- An overview of the machine learning lifecycle and its challenges
- How DevOps practices are misaligned to the machine learning lifecycle
- The Model Factory Framework overview, tools and processes
- How the Model Factory Framework cuts model deployment from 25 to as few as 10 steps
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