A multinational company providing a complete marketing automation SaaS platform was already familiar with Machine Learning and Artificial intelligence but was looking for a way to bring its AI algorithm to the next production-ready level.
Addepto turned out to be the best partner to do that.
Company AI models were implemented in two main product lines.
The first was especially crucial for the platform’s core feature, marketing automation.
With AI, the company could effectively verify e-mails and detect the ones that can be considered spam or phishing by webmails, so landing in SPAM folders never opened by end-users. A variety of AI models were designed to prevent that from happening, providing visibility on the desired level.
The second product – a website builder – included AI that enables end-users to migrate their existing web pages to the company’s solutions.
It made the whole process, which can be painful for non-technical users – smooth and easy: users could just put the link to their website, and AI – under the hood – do the magic.
With no MLOps “back office,” which slowed down the AI-driven processes. There were no pipelines that could automate, for example, the data flow, and the need for manually starting the operations significantly lowered their effectiveness.
The company wanted to take its Machine Learning instances to the production level by adding automation, structuring the flow of further iterations, and refactoring the methods with the best MLOps practices in mind.
Addepto Team decided to revamp some of the AI models in order to avoid bottlenecks that might jeopardize the whole process in the following steps.
The goal was to:
With MLOps, the company can actively monitor and optimize Machine Learning models in production.
By making adjustments as needed, managers can always be up-to-date with mail providers’ requirements, making the system efficient in delivering mail to end-users.
For example, the number of emails sent to a particular hosting provider never exceeds the allowable number, which prevents them from being considered SPAM. It helped the company to manage the risks and enabled them to scale up their capabilities to meet the needs of growing demand without sacrificing performance or reliability.
The company aimed to enhance their AI algorithm to a production-ready standard.
MLOps enables the company to monitor and optimize Machine Learning models in production, ensuring efficient mail delivery to end-users by keeping up with mail providers’ requirements.
We assist businesses in leveraging AI technologies, including machine and deep learning, to improve their data utilization.
Here you can learn more about the technologies used in this project:
We enable the creation of data-driven services that enhance customer satisfaction, boost revenue, and expedite internal processes.