Levvel Blog - Machine Learning Part Two--Running a Machine Learning Data Store on Redis Labs

Editor's note: This is the second post in a two-part series about machine learning. In part one, we discussed how to get started with machine learning: define, benchmark, and deploy. Managing large, pre-trained predictive models across an organization and ensuring the same version is on production can be a challenge with the rapid pace of changes in the AI/machine learning space. Here, we have an approach that demonstrates how to automate building, storing, and deploying predictive models from a Remote Machine Learning Data Store hosted on Redis Labs. This approach is focused on showing how DevOps CI/CD artifact pipelines can be used to build and manage machine learning model artifacts with Jupyter IPython notebooks, accompanying command line automation versions, and administration tools to help manage artifacts across a team.