Lerna AI helps mobile apps boost their campaign and product recommendation conversions, with the world’s first mobile hyper-personalization recommender. In a post-cookies world where third-party data are dying, apps need to better understand their users and make the most of their first-party data potential. Lerna AI mobile SDK enables apps to personalize their content to each user, by training models on content metadata along with user data, on-device. This way it predicts the optimal content for each user at a specific time while preserving user privacy.
Lerna AI is a mobile personalization service that uses a complex system of servers, ML algorithms, and data analytics to deliver personalized content to mobile apps. The platform is known for the following.
Lerna SDK is the core of machine learning module of Lerna AI. These web/mobile native libraries provides both functionalities of training and inferencing.
Lerna.AI uses a Federated Learning server to aggregate the ML data and delivered back to Lerna SDK.
Lerna AI uses MPC service to ensure personal data privacy. This service provide mechanism to secure end-to-end communication between all services. Also provide mechanism to support communication with noised payload, that’s make pointless any man-in-the-middle attack.
Lerna AI also provide an out-of-the-box recommendation service that developers can use in combination with Lerna AI personalization service.
The Lerna AI uses a wide range of technologies to power its federated learning platform. The Lerna AI hyper-personalized recommendation architecture is presented in the following diagram.
Here’s an overview of the technologies used in different parts of the Lerna AI tech stack.
Lerna AI provides a multiplatform SDK that is the core of Lerna AI system. This SDK provides a friendly API to developer to use our federated learning service. The SDK implements all required functionality to collect data and train ML models. Also, the SDK can use ML models to predict personalized items for specific user.
Lerna AI SDK implemented in Kotlin Multiplatform, that can be
native SDK for both Android and iOS platforms. Also, a beta version of
web assembly (WASM) SDK provided for web applications.
Lerna FL Service is a centralized orchestrator/aggregator for all participators in a Lerna AI Machine Learning instance. In order to expose value of each training held on edge devices, Lerna AI requires an orchestrator to provide required data to edge devices and an aggregator to combine data and prepare it for the next iteration of machine learning.
Lerna AI Federation Learning Service is a Spring Boot application runs on one/multiple AWS EC2 instances. Lerna AI FL Service, also use AWS RDS Postgres database for persistence and a Redis instance for caching.
Lerna AI MPC Service is a privacy service, implemented in ANSI C, that can be run on an Intel® Software Guard Extensions (Intel® SGX).
Lerna AI uses a custom release of ActionML OSS in order to enhance platform by adding an optional recommendation system.
Lerna AI’s dashboard front end, part of the application developers interact with, is built primarily using JavaScript. This allows Lerna AI to create a highly dynamic and responsive user interface that can quickly respond to user input and provide a seamless listening experience. Also, JavaScript allows Lerna AI to easily integrate with a wide range of third-party services and APIs, which helps power many of the app’s advanced features. By leveraging the power of JavaScript, Lerna AI can provide a modern, feature-rich user experience that keeps users engaged and coming back for more.
Vue is a JavaScript framework for building user interfaces. It builds on top of standard HTML, CSS, and JavaScript and provides a declarative, component-based programming model that helps you efficiently develop user interfaces of any complexity.
Our current infrastructure runs on manually managed EC2 instances.
There is a single EC2 instance per application per environment (dev, stage). Currently, dev and stage share the same VPC and region.
Respectively, dev and stage microservices (Lambda) share the same VPC with the EC2 instances hosting our applications.
Instances run on public subnets and SSH connections are established directly on them via network load balancer by AWS ELB.
SSL termination (where applicable) is done on the reverse proxies (Nginx).
Provisioning of hosts is done manually and new revisions of applications are deployed via SSH.
Production hosts are deployed on a rolling-updates fashion between the reverse proxies and the application copies inside the same instance.
The following diagram depicts a full-blown environment:
A set of principles, guides and practices, a framework if you like to help us achieve the company goals.
It's not written in stone - it's in markdown actually :) - so it will be debated & refined over time.