A Complete MLOps Toolbox

In Rappi as in many other high-potential startups, it is clear that one of the keys to success has been and continues to be the implementation of analytics and data science, using machine learning models that provide valuable insights to the business. Its use in startups and traditional companies that have focused on digital transformation has been increasing exponentially and today, being a part of the broader AI field, machine learning should be as common as software applications in general, and that is precisely where MLOps treat ML algorithms as reusable software appliances, offering rapid and repeatable deployment of models, followed by continuous and monitored integration ensuring that each model performs optimally as its environment evolves over time. In other words, and to wrap up, MLOps are the set of practices that an enterprise must have in place in order to run AI and ML successfully. If you have data science and IA you must have almost by obligation a dedicated MLOps team, the models by themselves are helpless and biased only to the data they were trained with. Nowadays, a start-up must face big challenges in terms of managing their data as it is constantly growing and changing.